Showing 1–50 of 98 results for author: Chen, F

High fidelity control of superconducting qubits with optical transmitted signal

Authors: Yu-Huai Li, Daojin Fan, Na Li, Fusheng Chen, Shaowei Li, Dong-Dong Li, Yu Xu, Jin Lin, Ming Gong, He-Liang Huang, Hui Deng, Yulin Wu, Haoran Qian, Shaojun Guo, Futian Liang, Xiaobo Zhu, Cheng-Zhi Peng, Jian-Wei Pan

Abstract: Superconducting circuits exhibit remarkable potential for constructing large-scale quantum simulation and computation systems, featuring numerous qubits, extended coherence time, and precise control. Nevertheless, the growing number of signal cables poses a challenge in dilution refrigerators due to space and heat load constraints. To overcome this issue, we experimentally implemented an optically… ▽ More Superconducting circuits exhibit remarkable potential for constructing large-scale quantum simulation and computation systems, featuring numerous qubits, extended coherence time, and precise control. Nevertheless, the growing number of signal cables poses a challenge in dilution refrigerators due to space and heat load constraints. To overcome this issue, we experimentally implemented an optically-assisted transmission line as an alternative to coaxial cables. By modulating microwave signals on laser intensities at room temperature and regenerating the signals at a cryogenic plate within the dilution refrigerator, we demonstrated full control of superconducting qubits using photocurrent. We demonstrate and benchmark both single-qubit and two-qubit gates on frequency tunable transmon qubits, achieving fidelities of 99.915% $\pm$ 0.005% and 99.676% $\pm$ 0.041%, respectively, which have reached the requirement of the surface code. △ Less

Submitted 19 August, 2026; originally announced August 2026.

Comments: 11 pages, 7 figures

Tabletop reversibility of phase-covariant operations

Authors: Fangzhen Chen, Xueyuan Hu

Abstract: Irreversibility and time-translation symmetry are both fundamental in open quantum dynamics, and it is of great importance to study the interplay between them. In this paper, we study tabletop reversibility (TTR) for phase-covariant quantum operations and obtain sharply different conclusions for finite-dimensional and Gaussian systems. For finite-dimensional systems, we prove that any phase-covari… ▽ More Irreversibility and time-translation symmetry are both fundamental in open quantum dynamics, and it is of great importance to study the interplay between them. In this paper, we study tabletop reversibility (TTR) for phase-covariant quantum operations and obtain sharply different conclusions for finite-dimensional and Gaussian systems. For finite-dimensional systems, we prove that any phase-covariant operation that admits a Petz recovery map can be implemented by a dilation which is both time-translational symmetric and tabletop time reversible. In contrast, for phase-covariant Gaussian operations we exhibit a concrete obstruction: any Gaussian dilation of a single-mode Gaussian amplification channel is not tabletop time reversible. As a byproduct, we find that almost every completely positive and trace preserving (CPTP) map admits a dilation that realizes TTR. These results clarify whether and how TTR can be realized under phase-covariant symmetry constraints and reveal a qualitative difference between finite-dimensional and Gaussian systems. △ Less

Submitted 12 August, 2026; originally announced August 2026.

Comments: 16 pages, 3 figures

Journal ref: Phys. Rev. A 114, 022419 (2026)

Generalized Mermin Inequalities for Benchmarking Large-Scale GHZ States

Authors: Jianbin Cai, Junxiang Huang, Fynn Otto, Yuan Li, Carlos de Gois, Tao Jiang, Sirui Cao, Fangzheng Chen, Hao Fu, Jin Lin, Wei Xie, Naibin Zhou, Shibiao Tang, Xiang-Yang Li, Cheng-Zhi Peng, Xiao Yuan, Otfried Gühne, Ming Gong

Abstract: Multipartite Bell tests provide a correlation-only route to benchmarking quantum processors, but their application at large scales is hindered by the rapid decay of many-body correlators under noise and exponentially many terms in conventional Bell expressions. Here we address these scalability obstacles by introducing a finite-setting generalized Mermin family of state-tailored Bell inequalities… ▽ More Multipartite Bell tests provide a correlation-only route to benchmarking quantum processors, but their application at large scales is hindered by the rapid decay of many-body correlators under noise and exponentially many terms in conventional Bell expressions. Here we address these scalability obstacles by introducing a finite-setting generalized Mermin family of state-tailored Bell inequalities with analytic certification bounds, in which the measurement-setting number $m$ provides an additional certification dimension complementary to the system size $n$. We show that, for the powers-of-two setting choices considered here, increasing $m$ leaves the ideal normalized multipartite quantum value unchanged while lowering the relevant classical bounds, thereby strengthening the Bell-violation ratios and yielding an improved noise-robustness scaling compared to the standard Mermin inequality. We test this construction experimentally on a programmable superconducting processor by preparing Greenberger-Horne-Zeilinger (GHZ) states of up to 80 qubits. Using randomized sampling for direct Bell-operator estimation, we observe Bell ratios that grow exponentially with system size, certify a nonlocality depth of 14, and show that increasing $m$ strengthens both the Bell ratio and depth certification. All results are obtained solely from measured correlators and analytical bounds, without readout correction, tomography, or model-based mitigation. Generalized Mermin inequalities therefore provide a sharper Bell benchmark for noisy large-scale GHZ states. △ Less

Submitted 26 July, 2026; originally announced July 2026.

Comments: 6 pages, 4 figures; supplemental material: 22 pages

Continuous-Variable MIMO THz Quantum Secret Sharing: Gaussian-modulation and Passive-modulation

Authors: Leixin Wu, Jiayu Pan, Fangzhe Chen, Lingtao Zhang, Bowen Zheng, Tie Qiu

Abstract: Although quantum key distribution (QKD) enables information-theoretically secure key distribution, it is mainly designed for point-to-point communication and cannot directly support multi-user collaborative scenarios. To address this limitation, quantum secret sharing (QSS) has been proposed to enable secure multiparty key sharing. However, most existing QSS protocols rely on a single-input single… ▽ More Although quantum key distribution (QKD) enables information-theoretically secure key distribution, it is mainly designed for point-to-point communication and cannot directly support multi-user collaborative scenarios. To address this limitation, quantum secret sharing (QSS) has been proposed to enable secure multiparty key sharing. However, most existing QSS protocols rely on a single-input single-output (SISO) channel, which limits the achievable secret key rate (SKR) and transmission distance. This paper proposes a continuous-variable (CV) QSS protocol based on a multiple-input multiple-output (MIMO) architecture operating in the terahertz (THz) band. In the proposed scheme, transmit-receive beamforming decomposes the MIMO channel into multiple parallel SISO subchannels, thereby improving both the SKR and transmission distance. We describe the QSS transmission procedure and derive the SKR expressions for eight protocol variants under Gaussian collective attacks. Specifically, Gaussian modulation and passive modulation are considered at the transmitter, while homodyne and heterodyne detection are considered at the receiver. Both asymptotic and composable finite-size SKR formulas are derived to characterize the ideal upper-bound performance and the achievable performance under finite resources, respectively. Simulation results show that, under ideal assumptions including perfect channel state information, perfect phase synchronization, and ideal beamforming, the Gaussian-modulation protocol with a 32 x 32 antenna configuration and the passive-modulation protocol with a 1024 x 1024 antenna configuration achieve transmission distances of 14.99 m and 160 m in the atmospheric channel, respectively. These results provide an idealized theoretical benchmark for evaluating the potential performance gains of MIMO-assisted THz CV-QSS in indoor and short-range outdoor wireless networks. △ Less

Submitted 9 July, 2026; originally announced July 2026.

Surface code logical operations on a superconducting quantum processor

Authors: Weiping Lin, Shaojun Guo, Yuwei Ma, Zhengzhong Yi, Kai Zhang, Jiahao Bei, Jianbin Cai, Sirui Cao, Danning Chen, Guoben Chen, Jianguo Chen, Kefu Chen, Xiawei Chen, Zhe Chen, Zhiyuan Chen, Zihua Chen, Wenhao Chu, Hui Deng, Xun Ding, Zhuzhengqi Ding, Yajie Du, Bo Fan, Daojin Fan, Yuanhao Fu, Dongxin Gao , et al. (122 additional authors not shown)

Abstract: Fault-tolerant quantum computation requires logical operations that manipulate encoded information while preserving quantum error-correction protection. In planar surface-code architectures, code deformation and lattice surgery provide a local, measurement-based route to such operations. Here we experimentally realize key elements of patch-based surface-code logical processing on a 107-qubit super… ▽ More Fault-tolerant quantum computation requires logical operations that manipulate encoded information while preserving quantum error-correction protection. In planar surface-code architectures, code deformation and lattice surgery provide a local, measurement-based route to such operations. Here we experimentally realize key elements of patch-based surface-code logical processing on a 107-qubit superconducting quantum processor. We first implement a reusable primitive layer comprising merge and split, patch expansion and shrinkage, and deformations mediated by domain walls and twist defects. We then compose these primitives to realize logical state routing, the logical controlled-NOT gate, and the single-qubit Hadamard and phase gates, which together form a Clifford-generating set. All operations are implemented on distance-three rotated surface-code patches with multi-round syndrome extraction and neural-network decoding, without post-selection. Our results advance superconducting surface-code experiments from protected logical memory to active, patch-based fault-tolerant logical operations. △ Less

Submitted 1 July, 2026; originally announced July 2026.

Strong-field control of the $Z$-boson resonance in $e^+e^-$ collisions

Authors: Fengye Chen, Qingzheng Lv, Libin Fu

Abstract: Resonant $Z$-boson production is a cornerstone of precision electroweak physics, with its vacuum line shape set by the $Z$ mass, width, and collision kinematics. We show that a strong laser field can significantly alter this picture. By treating the field nonperturbatively, we find that laser dressing of the incoming fermions alters the effective collision kinematics and opens laser-photon exchang… ▽ More Resonant $Z$-boson production is a cornerstone of precision electroweak physics, with its vacuum line shape set by the $Z$ mass, width, and collision kinematics. We show that a strong laser field can significantly alter this picture. By treating the field nonperturbatively, we find that laser dressing of the incoming fermions alters the effective collision kinematics and opens laser-photon exchange channels, including multiphoton processes, in $e^{+}e^{-}$ collisions. As a result, the $Z$-resonance profile develops distinct intensity-dependent regimes, evolving from the vacuum limit to saturation at intermediate field strengths and to an approximately quadratic enhancement at higher intensities. Additionally, the polarization composition of the produced $Z$ bosons is redistributed. In particular, at high intensities the laser-induced contribution can compensate the intrinsic chiral asymmetry of the electroweak interaction, leading to nearly parity-balanced $Z$-boson production. Our results identify that strong classical fields can dynamically control electroweak resonance phenomena, opening a bridge between strong-field QED and high-energy collider physics. △ Less

Submitted 9 June, 2026; v1 submitted 8 June, 2026; originally announced June 2026.

Comments: The incorrect summation of the spin and polarization indices in Equations (23) and (30) in the supplementary materials has been removed

QASM-Eval: A Dataset to Train and Evaluate LLMs on OpenQASM-3 Beyond Quantum Circuits

Authors: Zhenxiao Fu, Lei Jiang, Fan Chen

Abstract: Quantum computing remains in the Noisy Intermediate-Scale Quantum (NISQ) era, where the performance is highly constrained to noise. Addressing the limitation often requires hardware-facing capabilities beyond gate-sequence circuit specification, including mid-circuit measurement and classical feedback for quantum error correction (QEC), precise timing control for dynamical decoupling (DD), and pul… ▽ More Quantum computing remains in the Noisy Intermediate-Scale Quantum (NISQ) era, where the performance is highly constrained to noise. Addressing the limitation often requires hardware-facing capabilities beyond gate-sequence circuit specification, including mid-circuit measurement and classical feedback for quantum error correction (QEC), precise timing control for dynamical decoupling (DD), and pulse-level waveform access for calibration. OpenQASM-3 was introduced to expose exactly these capabilities, providing a hardware-level programming interface. However, despite the rapid progress of large language models in code generation, there is still no dataset specifically designed to train and evaluate LLMs on OpenQASM-3 programs that involve its advanced hardware-oriented features. To address this gap, we introduce QASM-Eval, the first comprehensive dataset designed to train and evaluate LLMs on OpenQASM-3. Rather than focusing on quantum algorithm design or reasoning, QASM-Eval explicitly targets the language's hardware-facing features. QASM-Eval comprises an expert-verified test set of 100 tasks and a training set of 4,000 tasks, systematically covering classical logic, timing scheduling, pulse control, and complex real-world workflows. To automatically validate generated programs, we check syntax, quantum states and program timeline using an extended verifier. Our evaluation reveals that while state-of-the-art LLMs struggle heavily in OpenQASM-3 coding tasks, targeted fine-tuning on QASM-Eval yields significant gains. QASM-Eval provides a crucial benchmark and training foundation to accelerate the development of reliable LLM assistants for hardware-facing quantum programming in NISQ era. Data and code: https://github.com/fuzhenxiao/QASM-Eval △ Less

Submitted 28 April, 2026; originally announced May 2026.

Backdoor Threats in Variational Quantum Circuits: Taxonomy, Attacks, and Defenses

Authors: Lei Jiang, Fan Chen

Abstract: Variational quantum algorithms (VQAs) are a central paradigm for noisy intermediate-scale (NISQ) quantum computing, yet their reliance on predesigned and pretrained variational quantum circuits (VQCs) introduces critical security vulnerabilities, particularly backdoor attacks. These attacks embed hidden malicious behaviors that remain dormant under normal conditions but are activated by specific t… ▽ More Variational quantum algorithms (VQAs) are a central paradigm for noisy intermediate-scale (NISQ) quantum computing, yet their reliance on predesigned and pretrained variational quantum circuits (VQCs) introduces critical security vulnerabilities, particularly backdoor attacks. These attacks embed hidden malicious behaviors that remain dormant under normal conditions but are activated by specific triggers, leading to adversarial outcomes such as incorrect predictions or manipulated objective values. This paper presents a survey of backdoor attacks in VQCs, covering data-poisoning, compiler-level, and quantum-native mechanisms. We formalize key terminology and threat models, and review existing attack strategies along with their empirical characteristics. We also analyze current detection and defense approaches, highlighting their limitations, especially against quantum-specific threats. By synthesizing recent advances, this survey outlines the evolving security landscape of VQCs and identifies key challenges and future directions for developing robust, quantum-aware defenses in hybrid quantum-classical systems. △ Less

Submitted 13 May, 2026; originally announced May 2026.

Observation of Non-Markovian Evolution of Tripartite Quantum Steering

Authors: Yan Wang, Shao-qi Lin, Rui-qi Shen, Fang-liang Chen, Guo-qiang Zhang, Li-jiong Chen, Yong-nan Sun, Qi-ping Su, Chui-ping Yang

Abstract: The memory effects in open quantum systems can induce information backflow and revive quantum correlations, thereby providing a powerful way to protect and recover useful quantum resources in realistic noisy environments. However, such dynamics remains experimentally unexplored in multipartite quantum steering. Here we observe different non-Markovian evolution of tripartite quantum steering using… ▽ More The memory effects in open quantum systems can induce information backflow and revive quantum correlations, thereby providing a powerful way to protect and recover useful quantum resources in realistic noisy environments. However, such dynamics remains experimentally unexplored in multipartite quantum steering. Here we observe different non-Markovian evolution of tripartite quantum steering using Greenberger-Horne-Zeilinger-type mixed states, covering both death and revival processes. In particular, we experimentally demonstrate the more intricate asymmetric steering structure of tripartite quantum steering through different bipartitions, which do not arise in bipartite systems. Our results provide foundational insights into the hierarchical and directional structures in multipartite quantum steering, and highlight its potential as a useful resource for asymmetric quantum information processing. △ Less

Submitted 4 May, 2026; v1 submitted 29 April, 2026; originally announced April 2026.

Bayesian Phase Stabilization at the Shot-Noise Limit for Scalable Quantum Networks

Authors: Guang-Cheng Liu, Chao-Hui Xue, Fa-Xi Chen, Ming-Yang Zheng, Yi Yang, Li-Bo Li, Bin Wang, Bo-Wen Yang, Hai-Feng Jiang, Yong Wan, Ye Wang, Jiu-Peng Chen, Qiang Zhang, Jian-Wei Pan

Abstract: High-precision optical phase stabilization in quantum networks is fundamentally constrained by the strict photon-flux and duty-cycle limits required to avoid disturbing fragile quantum states. This challenge becomes especially critical when coordinating multiple independent light sources for multi-step quantum protocols. Here, we develop an integrated phase-stabilization framework that incorporate… ▽ More High-precision optical phase stabilization in quantum networks is fundamentally constrained by the strict photon-flux and duty-cycle limits required to avoid disturbing fragile quantum states. This challenge becomes especially critical when coordinating multiple independent light sources for multi-step quantum protocols. Here, we develop an integrated phase-stabilization framework that incorporates a Bayesian phase estimator to optimally extract information from sparse single-photon detection events. This approach outperforms conventional maximum-likelihood estimation and achieves the shot-noise limit under minimal photon flux. The framework enables real-time correction of combined phase noise from both nodal lasers and transmission fibers, facilitating a two-step excitation protocol for heralded entanglement generation between separate trapped-ion nodes via single-photon interference. Operating with a detected photon rate of approximately 1 MHz and a duty cycle less than or equal to 6.5%, the system maintains interferometric visibility greater than 97% over fiber links of 10 km and 100 km. This phase control yields deterministic ion-ion entanglement with parity contrast exceeding 85% at both distances, enabling device-independent quantum key distribution. Moreover, the resulting memory-memory entanglement at 10 km survives beyond the average time required to establish it -- a fundamental requirement for quantum repeaters. This work establishes a robust and scalable foundation for practical long-distance quantum networks. △ Less

Submitted 23 April, 2026; originally announced April 2026.

Dark Matter Detection through Rydberg Atom Transducer

Authors: J. F. Chen, Haokun Fu, Christina Gao, Jing Shu, Geng-Bo Wu, Peiran Yin, Yi-Ming Zhong, Ying Zuo

Abstract: Ultralight bosonic dark matter with masses in the meV range, corresponding to terahertz (THz) Compton frequencies, remains largely unexplored due to the difficulty of achieving both efficient signal conversion and single-photon-sensitive detection at THz frequencies. We propose a hybrid detection architecture that integrates a dielectric haloscope, Rydberg-atom transducer, and superconducting nano… ▽ More Ultralight bosonic dark matter with masses in the meV range, corresponding to terahertz (THz) Compton frequencies, remains largely unexplored due to the difficulty of achieving both efficient signal conversion and single-photon-sensitive detection at THz frequencies. We propose a hybrid detection architecture that integrates a dielectric haloscope, Rydberg-atom transducer, and superconducting nanowire single-photon detection within a unified cryogenic platform operating at $\lesssim 1\,\text{K}$. The dielectric haloscope converts dark matter into THz photons via phase-matched resonant enhancement, achieving form factors $C \sim 0.4$ and loaded quality factors $Q_L \sim 10^4$. A cold $^{87}$Rb ensemble then coherently up-converts the THz signal to the optical domain through six-wave mixing among Rydberg states. The intrinsic directionality and narrow bandwidth ($Δν_{\mathrm{atomic}} \sim 1\,\text{MHz}$) of this process provide extra suppression of isotropic thermal backgrounds. With 10 days of integration at $0.3\,\text{K}$, we project sensitivity to the axion-photon coupling $g_{aγγ} \sim 10^{-13}\,\mathrm{GeV}^{-1}$ at $m_a \sim 0.4\,\text{meV}$, reaching the QCD axion band and opening the THz window for searches of both axion and dark photon dark matter. △ Less

Submitted 24 March, 2026; originally announced March 2026.

Comments: 12 pages, 5 figures, 3 tables

Realizing anomalous Floquet non-Abelian band topology in photonic scattering networks

Authors: Yuze Hu, Mingyu Tong, Tian Jiang, Shuxing Yang, Ning Han, Fujia Chen, Li Zhang, Rui Zhao, Qiaolu Chen, Hongsheng Chen, F. Nur Ünal, Robert-Jan Slager, Yihao Yang

Abstract: The concept of multi-gap topology has recently been shown to give rise to uncharted phases beyond conventional single-gap classifications. These phases relate to band nodes with non-Abelian quaternion charges and momentum-space braiding processes characterized by new invariants such as paradigmatic Euler class, phenomena that intrinsically require at least two spatial dimensions. Extending such ph… ▽ More The concept of multi-gap topology has recently been shown to give rise to uncharted phases beyond conventional single-gap classifications. These phases relate to band nodes with non-Abelian quaternion charges and momentum-space braiding processes characterized by new invariants such as paradigmatic Euler class, phenomena that intrinsically require at least two spatial dimensions. Extending such phases into the non-equilibrium regime is predicted to unlock even richer multi-gap topologies beyond static settings, yet their experimental realization has remained elusive due to the stringent requirements on dimensionality, symmetry, and dynamical control. Here, we theoretically demonstrate and, for the first time, experimentally realize two-dimensional (2D) Floquet non-Abelian band topology in photonic scattering networks. Within this platform, we uncover a sequence of topological phenomena unique to 2D multi-gap systems far from equilibrium, including anomalous multi-gap phases interconnected by band nodes, Floquet Euler transfer, gapped phases with anomalous Dirac string configurations, and Floquet-induced non-Abelian braiding of band nodes. In addition, we observe Floquet-periodic anomalous edge states across multiple gaps, providing experimental signatures of these sought-after 2D multi-gap Floquet topological phases. Our results establish photonic scattering networks as a practical and versatile route to non-Abelian Floquet systems, opening avenues for dynamical topological physics with braiding capability and robust photonic functionalities. △ Less

Submitted 4 March, 2026; originally announced March 2026.

Comments: 42 pages, 18 figures

Atom-light hybrid interferometer for atomic sensing with quantum memory

Authors: Xingchang Wang, Xinyun Liang, Liang Dong, Ying Zuo, Jianmin Wang, Dasen Yang, Linyu Chen, Georgios A. Siviloglou, Z. Y. Ou, J. F. Chen

Abstract: Quantum memories feature a reversible conversion of optical fields into long-lived atomic spin waves, and are therefore ideal for operating as sensitive atomic sensors. However, up to now, atom-light interferometers have lacked an efficient approach to exploit their ultimate atomic sensing performance, since an extra optical delay line is required to compensate for the memory time. Here, we report… ▽ More Quantum memories feature a reversible conversion of optical fields into long-lived atomic spin waves, and are therefore ideal for operating as sensitive atomic sensors. However, up to now, atom-light interferometers have lacked an efficient approach to exploit their ultimate atomic sensing performance, since an extra optical delay line is required to compensate for the memory time. Here, we report a new protocol that records the photocurrent via heterodyne mixing with a stable local oscillator. The obtained complex quadrature amplitude that carries information imprinted on its phase by an external magnetic field, is successfully recovered from the interference patterns between the light and the atomic spin wave, without the stringent requirement of having them overlap in time. Our results reveal that the sensitivity scales favorably with the lifetime of the quantum memory. Our work may have important applications in building distributed quantum networks through quantum memory-assisted atom-light interferometers. △ Less

Submitted 26 January, 2026; originally announced January 2026.

Comments: 8 pages, 4 figures

Learning to Decode in Parallel: Self-Coordinating Neural Network for Real-Time Quantum Error Correction

Authors: Kai Zhang, Zhengzhong Yi, Shaojun Guo, Linghang Kong, Situ Wang, Xiaoyu Zhan, Tan He, Weiping Lin, Tao Jiang, Dongxin Gao, Yiming Zhang, Fangming Liu, Fang Zhang, Zhengfeng Ji, Fusheng Chen, Jianxin Chen

Abstract: Fast, reliable decoders are pivotal components for enabling fault-tolerant quantum computation (FTQC). Neural network decoders like AlphaQubit have demonstrated potential, achieving higher accuracy than traditional human-designed decoding algorithms. However, existing implementations of neural network decoders lack the parallelism required to decode the syndrome stream generated by a superconducti… ▽ More Fast, reliable decoders are pivotal components for enabling fault-tolerant quantum computation (FTQC). Neural network decoders like AlphaQubit have demonstrated potential, achieving higher accuracy than traditional human-designed decoding algorithms. However, existing implementations of neural network decoders lack the parallelism required to decode the syndrome stream generated by a superconducting logical qubit in real time. Moreover, integrating AlphaQubit with sliding window-based parallel decoding schemes presents non-trivial challenges: AlphaQubit is trained solely to output a single bit corresponding to the global logical correction for an entire memory experiment, rather than local physical corrections that can be easily integrated. We address this issue by training a recurrent, transformer-based neural network specifically tailored for parallel window decoding. While it still outputs a single bit, we derive training labels from a consistent set of local corrections and train on various types of decoding windows simultaneously. This approach enables the network to self-coordinate across neighboring windows, facilitating high-accuracy parallel decoding of arbitrarily long memory experiments. As a result, we overcome the throughput bottleneck that previously precluded the use of AlphaQubit-type decoders in FTQC. Our work presents the first scalable, neural-network-based parallel decoding framework that simultaneously achieves SOTA accuracy and the stringent throughput required for real-time quantum error correction. Using an end-to-end experimental workflow, we benchmark our decoder on the Zuchongzhi 3.2 superconducting quantum processor on surface codes with distances up to 7, demonstrating its superior accuracy. Moreover, we demonstrate that, using our approach, a single TPU v6e is capable of decoding surface codes with distances up to 25 within 1us per decoding round. △ Less

Submitted 14 January, 2026; originally announced January 2026.

Comments: The main text consists of 25 pages and 9 figures, extending our prior work (arXiv:2509.03815) with new results on surface code decoding in superconducting qubit systems and real-time performance benchmarks on TPU v6e

Heisenberg scaling in optical magnetometry with measurement-induced correlations as a quantum resource

Authors: Georg Engelhardt, Ming Li, Xingchang Wang, JunYan Luo, J. F. Chen

Abstract: Theoretical proposals to reach the Heisenberg scaling of the measurement precision typically require carefully engineered interactions or initial entanglement. In studying optical magnetometry, we show that the continuous collective measurement process itself can generate the necessary many-body quantum correlations to achieve the elusive Heisenberg scaling of the quantum Fisher information in a d… ▽ More Theoretical proposals to reach the Heisenberg scaling of the measurement precision typically require carefully engineered interactions or initial entanglement. In studying optical magnetometry, we show that the continuous collective measurement process itself can generate the necessary many-body quantum correlations to achieve the elusive Heisenberg scaling of the quantum Fisher information in a dissipative, steady-state system without direct inter-atomic interactions. By contrasting a correlation-neglecting but otherwise consistent semiclassical model, which can violate the quantum Cramér-Rao bound (QCRB) by several orders of magnitude, with a collective quantum model, we isolate measurement-induced correlations as the essential mechanism. The violation of the QCRB serves thereby as a fundamental sanity check for semiclassical spectroscopic theories. This work reveals measurement-induced correlations as a widely unexplored quantum resource for quantum-enhanced sensing, establishes a new paradigm for achieving Heisenberg scaling in open quantum systems, and provides a direct path to test the foundations of quantum mechanics using macroscopic atom ensembles. △ Less

Submitted 23 July, 2026; v1 submitted 5 January, 2026; originally announced January 2026.

Comments: 28 pages including appendices; 5 figures; comments are welcome

Algorithmic Quantum Simulations of Quantum Thermodynamics

Authors: Yangsen Ye, Jue Nan, Dong Chen, Torsten V. Zache, Qingling Zhu, Yiming Zhang, Yuan Li, Xiawei Chen, Chong Ying, Chen Zha, Sirui Cao, Shaowei Li, Shaojun Guo, Haoran Qian, Hao Rong, Yulin Wu, Kai Yan, Feifan Su, Hui Deng, Yu Xu, Jin Lin, Ming Gong, Fusheng Chen, Gang Wu, Yong-Heng Huo , et al. (5 additional authors not shown)

Abstract: Characterizing quantum phases-of-matter at finite-temperature is essential for understanding complex materials and large-scale thermodynamic phenomena. Here, we develop algorithmic protocols for simulating quantum thermodynamics on quantum hardware through quantum kernel function expansion (QKFE), producing the free energy as an analytic function of temperature with uniform convergence. These prot… ▽ More Characterizing quantum phases-of-matter at finite-temperature is essential for understanding complex materials and large-scale thermodynamic phenomena. Here, we develop algorithmic protocols for simulating quantum thermodynamics on quantum hardware through quantum kernel function expansion (QKFE), producing the free energy as an analytic function of temperature with uniform convergence. These protocols are demonstrated by simulating transverse field Ising and XY models with superconducting qubits. In both analogue and digital implementations of the QKFE algorithms, we exhibit quantitative agreement of our quantum simulation experiments with the exact results. Our approach provides a general framework for computing thermodynamic potentials on programmable quantum devices, granting access to key thermodynamic properties such as entropy, heat capacity and criticality, with far-reaching implications for material design and drug development. △ Less

Submitted 28 November, 2025; originally announced November 2025.

Comments: 31 pages, 19 figures, 7 tables

Enhancing pair production with optimized chirped laser fields

Authors: Z. L. Li, Y. F. Chen, R. Z. Jiang, Y. J. Li

Abstract: The optimal chirped field for enhancing electron-positron (EP) pair production is explored using a quantum kinetic approach. First, the momentum spectrum and number density of EP pairs produced by Gaussian chirped fields are investigated. The results show that the momentum spectrum exhibits distinct interference patterns, while the number density grows monotonically with chirp parameters but oscil… ▽ More The optimal chirped field for enhancing electron-positron (EP) pair production is explored using a quantum kinetic approach. First, the momentum spectrum and number density of EP pairs produced by Gaussian chirped fields are investigated. The results show that the momentum spectrum exhibits distinct interference patterns, while the number density grows monotonically with chirp parameters but oscillates with the carrier angular frequency. Moreover, the number density increases by four orders of magnitude compared to chirp-free fields. The results are further compared with those from four other chirped fields: frequency-modulated, linear, quadratic, and sinusoidal chirp. The analysis reveals that the maximum number density for sinusoidally chirped fields is the highest, followed by Gaussian, frequency-modulated, quadratically, and linearly chirped fields. This ranking also applies to the maximum enhancement factors for these chirped fields. Notably, the number density for sinusoidally chirped fields improves nine orders of magnitude compared to chirp-free fields. These results not only deepen our understanding of pair production in chirped fields but also provide significant optimization strategies for future vacuum pair production experiments. △ Less

Submitted 30 September, 2025; originally announced October 2025.

Comments: 10 pages, 8 figures

DiffQ: Unified Parameter Initialization for Variational Quantum Algorithms via Diffusion Models

Authors: Chi Zhang, Mengxin Zheng, Qian Lou, Fan Chen

Abstract: Variational Quantum Algorithms (VQAs) are widely used in the noisy intermediate-scale quantum (NISQ) era, but their trainability and performance depend critically on initialization parameters that shape the optimization landscape. Existing machine learning-based initializers achieve state-of-the-art results yet remain constrained to single-task domains and small datasets of only hundreds of sample… ▽ More Variational Quantum Algorithms (VQAs) are widely used in the noisy intermediate-scale quantum (NISQ) era, but their trainability and performance depend critically on initialization parameters that shape the optimization landscape. Existing machine learning-based initializers achieve state-of-the-art results yet remain constrained to single-task domains and small datasets of only hundreds of samples. We address these limitations by reformulating VQA parameter initialization as a generative modeling problem and introducing DiffQ, a parameter initializer based on the Denoising Diffusion Probabilistic Model (DDPM). To support robust training and evaluation, we construct a dataset of 15,085 instances spanning three domains and five representative tasks. Experiments demonstrate that DiffQ surpasses baselines, reducing initial loss by up to 8.95 and convergence steps by up to 23.4%. △ Less

Submitted 21 September, 2025; originally announced September 2025.

VQEzy: An Open-Source Dataset for Parameter Initialization in Variational Quantum Eigensolvers

Authors: Chi Zhang, Mengxin Zheng, Qian Lou, Hui Min Leung, Fan Chen

Abstract: Variational Quantum Eigensolvers (VQEs) are a leading class of noisy intermediate-scale quantum (NISQ) algorithms, whose performance is highly sensitive to parameter initialization. Although recent machine learning-based initialization methods have achieved state-of-the-art performance, their progress has been limited by the lack of comprehensive datasets. Existing resources are typically restrict… ▽ More Variational Quantum Eigensolvers (VQEs) are a leading class of noisy intermediate-scale quantum (NISQ) algorithms, whose performance is highly sensitive to parameter initialization. Although recent machine learning-based initialization methods have achieved state-of-the-art performance, their progress has been limited by the lack of comprehensive datasets. Existing resources are typically restricted to a single domain, contain only a few hundred instances, and lack complete coverage of Hamiltonians, ansatz circuits, and optimization trajectories. To overcome these limitations, we introduce VQEzy, the first large-scale dataset for VQE parameter initialization. VQEzy spans three major domains and seven representative tasks, comprising 12,110 instances with full VQE specifications and complete optimization trajectories. The dataset is available online, and will be continuously refined and expanded to support future research in VQE optimization. △ Less

Submitted 26 September, 2025; v1 submitted 21 September, 2025; originally announced September 2025.

QAgent: An LLM-based Multi-Agent System for Autonomous OpenQASM programming

Authors: Zhenxiao Fu, Lei Jiang, Yilun Xu, Gang Huang, Fan Chen

Abstract: Programming quantum circuits at the OpenQASM level is essential for achieving hardware-aware optimization and reliable execution on noisy intermediate-scale quantum (NISQ) devices, yet it remains challenging due to the need for domain-specific planning, iterative code synthesis, and low-level calibration. In this paper, we present QAgent, the first autonomous multi-agent framework for end-to-end O… ▽ More Programming quantum circuits at the OpenQASM level is essential for achieving hardware-aware optimization and reliable execution on noisy intermediate-scale quantum (NISQ) devices, yet it remains challenging due to the need for domain-specific planning, iterative code synthesis, and low-level calibration. In this paper, we present QAgent, the first autonomous multi-agent framework for end-to-end OpenQASM code generation. QAgent integrates schema-aware task planning, example- and tool-driven code synthesis, and hardware-aware calibration within a unified planning-synthesis-calibration workflow. The system leverages retrieval-augmented generation (RAG) to access structured kernel knowledge, examples, and backend constraints, and employs coordinated multi-agent reasoning with iterative execution feedback to ensure correctness. We evaluate QAgent on 12 representative quantum kernels and their compositions across five large language models (LLMs). Results show that QAgent improves Pass@1 accuracy by 47-70% on single-kernel tasks and achieves over 88% accuracy on multi-kernel workflows for large models, substantially outperforming existing baselines. Furthermore, under realistic hardware frequency drift, QAgent maintains near-unit execution fidelity through automated calibration, whereas SDK-based LLM methods suffer significant degradation. These results demonstrate that integrating planning, synthesis, and calibration is critical for reliable quantum program generation. The implementation of QAgent is open-sourced at https://github.com/fuzhenxiao/QAgent △ Less

Submitted 9 July, 2026; v1 submitted 26 August, 2025; originally announced August 2025.

Robust quantum computational advantage with programmable 3050-photon Gaussian boson sampling

Authors: Hua-Liang Liu, Hao Su, Si-Qiu Gong, Yi-Chao Gu, Hao-Yang Tang, Meng-Hao Jia, Qian Wei, Yukun Song, Dongzhou Wang, Mingyang Zheng, Faxi Chen, Libo Li, Siyu Ren, Xuezhi Zhu, Meihong Wang, Yaojian Chen, Yanfei Liu, Longsheng Song, Pengyu Yang, Junshi Chen, Hong An, Lei Zhang, Lin Gan, Guangwen Yang, Jia-Min Xu , et al. (12 additional authors not shown)

Abstract: The creation of large-scale, high-fidelity quantum computers is not only a fundamental scientific endeavour in itself, but also provides increasingly robust proofs of quantum computational advantage (QCA) in the presence of unavoidable noise and the dynamic competition with classical algorithm improvements. To overcome the biggest challenge of photon-based QCA experiments, photon loss, we report n… ▽ More The creation of large-scale, high-fidelity quantum computers is not only a fundamental scientific endeavour in itself, but also provides increasingly robust proofs of quantum computational advantage (QCA) in the presence of unavoidable noise and the dynamic competition with classical algorithm improvements. To overcome the biggest challenge of photon-based QCA experiments, photon loss, we report new Gaussian boson sampling (GBS) experiments with 1024 high-efficiency squeezed states injected into a hybrid spatial-temporal encoded, 8176-mode, programmable photonic quantum processor, Jiuzhang 4.0, which produces up to 3050 photon detection events. Our experimental results outperform all classical spoofing algorithms, particularly the matrix product state (MPS) method, which was recently proposed to utilise photon loss to reduce the classical simulation complexity of GBS. Using the state-of-the-art MPS algorithm on the most powerful supercomputer EI Capitan, it would take > $10^{42}$ years to construct the required tensor network for simulation, while our Jiuzhang 4.0 quantum computer takes 25.6 $μ$s to produce a sample. This work establishes a new frontier of QCA and paves the way to fault-tolerant photonic quantum computing hardware. △ Less

Submitted 24 August, 2025; v1 submitted 12 August, 2025; originally announced August 2025.

BVQC: A Backdoor-style Watermarking Scheme for Variational Quantum Circuits

Authors: Cheng Chu, Lei Jiang, Fan Chen

Abstract: Variational Quantum Circuits (VQCs) have emerged as a powerful quantum computing paradigm, demonstrating a scaling advantage for problems intractable for classical computation. As VQCs require substantial resources and specialized expertise for their design, they represent significant intellectual properties (IPs). However, existing quantum circuit watermarking techniques suffer from two primary d… ▽ More Variational Quantum Circuits (VQCs) have emerged as a powerful quantum computing paradigm, demonstrating a scaling advantage for problems intractable for classical computation. As VQCs require substantial resources and specialized expertise for their design, they represent significant intellectual properties (IPs). However, existing quantum circuit watermarking techniques suffer from two primary drawbacks: (1) watermarks can be removed during re-compilation of the circuits, and (2) these methods significantly increase task loss due to the extensive length of the inserted watermarks across multiple compilation stages. To address these challenges, we propose BVQC, a backdoor-based watermarking technique for VQCs that preserves the original loss in typical execution settings, while deliberately increasing the loss to a predefined level during watermark extraction. Additionally, BVQC employs a grouping algorithm to minimize the watermark task's interference with the base task, ensuring optimal accuracy for the base task. BVQC retains the original compilation workflow, ensuring robustness against re-compilation. Our evaluations show that BVQC greatly reduces Probabilistic Proof of Authorship (PPA) changes by 9.89e-3 and ground truth distance (GTD) by 0.089 compared to prior watermarking technologies. △ Less

Submitted 3 August, 2025; originally announced August 2025.

Calibrating quantum gates up to 52 qubits in a superconducting processor

Authors: Daojin Fan, Guoding Liu, Shaowei Li, Ming Gong, Dachao Wu, Yiming Zhang, Chen Zha, Fusheng Chen, Sirui Cao, Yangsen Ye, Qingling Zhu, Chong Ying, Shaojun Guo, Haoran Qian, Yulin Wu, Hui Deng, Gang Wu, Cheng-Zhi Peng, Xiongfeng Ma, Xiaobo Zhu, Jian-Wei Pan

Abstract: Benchmarking large-scale quantum gates, typically involving multiple native two-qubit and singlequbit gates, is crucial in quantum computing. Global fidelity, encompassing information about intergate correlations, offers a comprehensive metric for evaluating and optimizing gate performance, unlike the fidelities of individual local native gates. In this work, utilizing the character-average benchm… ▽ More Benchmarking large-scale quantum gates, typically involving multiple native two-qubit and singlequbit gates, is crucial in quantum computing. Global fidelity, encompassing information about intergate correlations, offers a comprehensive metric for evaluating and optimizing gate performance, unlike the fidelities of individual local native gates. In this work, utilizing the character-average benchmarking protocol implementable in a shallow circuit, we successfully benchmark gate fidelities up to 52 qubits. Notably, we achieved a fidelity of 63.09$\pm $0.23% for a 44-qubit parallel CZ gate. Utilizing the global fidelity of the parallel CZ gate, we explore the correlations among local CZ gates by introducing an inter-gate correlation metric, enabling one to simultaneously quantify crosstalk error when benchmarking gate fidelity. Finally, we apply our methods in gate optimization. By leveraging global fidelity for optimization, we enhance the fidelity of a 6-qubit parallel CZ gate from 87.65% to 92.04% and decrease the gate correlation from 3.53% to 3.22%, compared to local gate fidelitybased optimization. The experimental results align well with our established composite noise model, incorporating depolarizing and ZZ-coupling noises, and provide valuable insight into further study and mitigation of correlated noise. △ Less

Submitted 28 May, 2025; originally announced May 2025.

Comments: 25 pages, 10 figures

Journal ref: npj Quantum Information,2025,11:33

Generation of 95-qubit genuine entanglement and verification of symmetry-protected topological phases

Authors: Tao Jiang, Jianbin Cai, Junxiang Huang, Naibin Zhou, Yukun Zhang, Jiahao Bei, Guoqing Cai, Sirui Cao, Fusheng Chen, Jiang Chen, Kefu Chen, Xiawei Chen, Xiqing Chen, Zhe Chen, Zhiyuan Chen, Zihua Chen, Wenhao Chu, Hui Deng, Zhibin Deng, Pei Ding, Xun Ding, Zhuzhengqi Ding, Shuai Dong, Bo Fan, Daojin Fan , et al. (130 additional authors not shown)

Abstract: Symmetry-protected topological (SPT) phases are fundamental features of cluster states, serving as key resources for measurement-based quantum computation (MBQC). Generating large-scale cluster states and verifying their SPT phases are essential steps toward practical MBQC, which however still presents significant experimental challenges. In this work, we address these challenges by utilizing adva… ▽ More Symmetry-protected topological (SPT) phases are fundamental features of cluster states, serving as key resources for measurement-based quantum computation (MBQC). Generating large-scale cluster states and verifying their SPT phases are essential steps toward practical MBQC, which however still presents significant experimental challenges. In this work, we address these challenges by utilizing advanced superconducting hardware with optimized gate operations, enhanced readout fidelity, and error mitigation techniques. We successfully generate and verify 95-qubit one-dimensional and 72-qubit two-dimensional genuine entangled cluster states, achieving fidelities of $0.5603 \pm 0.0084$ and $0.5519 \pm 0.0054$, respectively. Leveraging these high-fidelity cluster states, we investigate SPT phases through quantum teleportation across all 95 qubits and demonstrate input-state-dependent robustness against symmetry-breaking perturbations, highlighting the practicality and intrinsic robustness of MBQC enabled by the SPT order. Our results represent a significant advancement in large-scale entanglement generation and topological phase simulation, laying the foundation for scalable and practical MBQC using superconducting quantum systems. △ Less

Submitted 3 May, 2025; originally announced May 2025.

Comments: Main text: 15 pages, 4 figures; supplementary materials: 42 pages, 19 figures. Total: 57 pages, 23 figures

Journal ref: Nat. Phys. 22 (2026) 430-438

Qracle: A Graph-Neural-Network-based Parameter Initializer for Variational Quantum Eigensolvers

Authors: Chi Zhang, Lei Jiang, Fan Chen

Abstract: Variational Quantum Eigensolvers (VQEs) are a leading class of noisy intermediate-scale quantum (NISQ) algorithms with broad applications in quantum physics and quantum chemistry. However, as system size increases, VQE optimization is increasingly hindered by the barren plateau phenomenon, where gradients vanish and the loss function becomes trapped in local minima. While machine learning-based pa… ▽ More Variational Quantum Eigensolvers (VQEs) are a leading class of noisy intermediate-scale quantum (NISQ) algorithms with broad applications in quantum physics and quantum chemistry. However, as system size increases, VQE optimization is increasingly hindered by the barren plateau phenomenon, where gradients vanish and the loss function becomes trapped in local minima. While machine learning-based parameter initialization methods have been proposed to address this challenge, they often show limited effectiveness in complex VQE problems. This is primarily due to their inadequate ability to model the intricate correlations embedded in the Hamiltonian structure and the associated ansatz circuits. In this paper, we propose \textit{Qracle}, a graph neural network (GNN)-based parameter initializer for VQEs. \textit{Qracle} systematically encodes both the Hamiltonian and the associated ansatz circuit into a unified graph representation and leverages a GNN to learn a mapping from VQE problem graphs to optimized ansatz parameters. Compared to state-of-the-art initialization techniques, \textit{Qracle} achieves a reduction in initial loss of up to $10.86$, accelerates convergence by decreasing optimization steps by up to $64.42\%$, and improves final performance with up to a $26.43\%$ reduction in Symmetric Mean Absolute Percentage Error (SMAPE). △ Less

Submitted 15 July, 2025; v1 submitted 2 May, 2025; originally announced May 2025.

CopyQNN: Quantum Neural Network Extraction Attack under Varying Quantum Noise

Authors: Zhenxiao Fu, Leyi Zhao, Xuhong Zhang, Yilun Xu, Gang Huang, Fan Chen

Abstract: Quantum Neural Networks (QNNs) have shown significant value across domains, with well-trained QNNs representing critical intellectual property often deployed via cloud-based QNN-as-a-Service (QNNaaS) platforms. Recent work has examined QNN model extraction attacks using classical and emerging quantum strategies. These attacks involve adversaries querying QNNaaS platforms to obtain labeled data for… ▽ More Quantum Neural Networks (QNNs) have shown significant value across domains, with well-trained QNNs representing critical intellectual property often deployed via cloud-based QNN-as-a-Service (QNNaaS) platforms. Recent work has examined QNN model extraction attacks using classical and emerging quantum strategies. These attacks involve adversaries querying QNNaaS platforms to obtain labeled data for training local substitute QNNs that replicate the functionality of cloud-based models. However, existing approaches have largely overlooked the impact of varying quantum noise inherent in noisy intermediate-scale quantum (NISQ) computers, limiting their effectiveness in real-world settings. To address this limitation, we propose the CopyQNN framework, which employs a three-step data cleaning method to eliminate noisy data based on its noise sensitivity. This is followed by the integration of contrastive and transfer learning within the quantum domain, enabling efficient training of substitute QNNs using a limited but cleaned set of queried data. Experimental results on NISQ computers demonstrate that a practical implementation of CopyQNN significantly outperforms state-of-the-art QNN extraction attacks, achieving an average performance improvement of 8.73% across all tasks while reducing the number of required queries by 90x, with only a modest increase in hardware overhead. △ Less

Submitted 31 March, 2025; originally announced April 2025.

Quantum Complex-Valued Self-Attention Model

Authors: Fu Chen, Qinglin Zhao, Li Feng, Longfei Tang, Yangbin Lin, Haitao Huang

Abstract: Self-attention has revolutionized classical machine learning, yet existing quantum self-attention models underutilize quantum states' potential due to oversimplified or incomplete mechanisms. To address this limitation, we introduce the Quantum Complex-Valued Self-Attention Model (QCSAM), the first framework to leverage complex-valued similarities, which captures amplitude and phase relationships… ▽ More Self-attention has revolutionized classical machine learning, yet existing quantum self-attention models underutilize quantum states' potential due to oversimplified or incomplete mechanisms. To address this limitation, we introduce the Quantum Complex-Valued Self-Attention Model (QCSAM), the first framework to leverage complex-valued similarities, which captures amplitude and phase relationships between quantum states more comprehensively. To achieve this, QCSAM extends the Linear Combination of Unitaries (LCUs) into the Complex LCUs (CLCUs) framework, enabling precise complex-valued weighting of quantum states and supporting quantum multi-head attention. Experiments on MNIST and Fashion-MNIST show that QCSAM outperforms recent quantum self-attention models, including QKSAN, QSAN, and GQHAN. With only 4 qubits, QCSAM achieves 100% and 99.2% test accuracies on MNIST and Fashion-MNIST, respectively. Furthermore, we evaluate scalability across 3-8 qubits and 2-4 class tasks, while ablation studies validate the advantages of complex-valued attention weights over real-valued alternatives. This work advances quantum machine learning by enhancing the expressiveness and precision of quantum self-attention in a way that aligns with the inherent complexity of quantum mechanics. △ Less

Submitted 7 April, 2025; v1 submitted 24 March, 2025; originally announced March 2025.

Coherence Properties of Rare-Earth Spins in Micrometer-Thin Films

Authors: Zihua Chai, Zhaocong Wang, Xinghang Chen, Quanshen Shen, Zeyu Gao, Junyu Guan, Hanyu Zhang, Ya Wang, Yang Tan, Feng Chen, Kangwei Xia

Abstract: Rare-earth ions in bulk crystals are excellent solid-state quantum systems in quantum information science, owing to the exceptional optical and spin coherence properties. However, the weak fluorescence of single rare-earth ions present a significant challenge for scalability, necessitating the integration into micro-cavities. Thin films serve as a promising material platform for the integration, y… ▽ More Rare-earth ions in bulk crystals are excellent solid-state quantum systems in quantum information science, owing to the exceptional optical and spin coherence properties. However, the weak fluorescence of single rare-earth ions present a significant challenge for scalability, necessitating the integration into micro-cavities. Thin films serve as a promising material platform for the integration, yet the fabrication without compromising the properties of the materials and rare-earth ions remains challenging. In this work, we fabricate micrometer-thin yttrium aluminum garnet (YAG) films from bulk crystals using ion implantation techniques. The resulting films preserve the single-crystalline structure of the original bulk crystal. Notably, the embedded rare-earth ions are photo-stable and exhibit bulk-like spin coherence properties. Our results demonstrate the compatibility of bulk-like spin properties with the thin-film fabrication technique, facilitating the efficient integration of rare-earth ions into on-chip photonic devices and advancing the applications of rare-earth ions systems in quantum technologies. △ Less

Submitted 12 March, 2025; originally announced March 2025.

PT -symmetry breaking and universal spectral statistics in quantum kicked rotors

Authors: Guang Li, Fuxing Chen, Ping Fang

Abstract: We investigate the spontaneous parity-time (PT )-symmetry breaking and spectral properties of a PT symmetric quantum kicked rotor under resonance conditions. At resonance, the QKR reduces to a finite-dimensional system. In the localized regime, we find that increasing the non-Hermitian parameter always induces a transition from a phase where the states exhibit PT symmetry to one where PT symmetry… ▽ More We investigate the spontaneous parity-time (PT )-symmetry breaking and spectral properties of a PT symmetric quantum kicked rotor under resonance conditions. At resonance, the QKR reduces to a finite-dimensional system. In the localized regime, we find that increasing the non-Hermitian parameter always induces a transition from a phase where the states exhibit PT symmetry to one where PT symmetry is spontaneously broken. In contrast, in the delocalized regime, the existence of such a transition depends on whether the reduced system is PT symmetric. If the reduced system is not PT symmetric, PT symmetry remains in the broken phase regardless of the non-Hermitian parameter. We further analyze the spectral statistics of the system in the delocalized regime. For real energy spectra, the level-spacing distribution transitions from Wigner-Dyson statistics, associated with the Gaussian orthogonal ensemble, to Poisson statistics as the non-Hermitian parameter increases, with the intermediate regime well described by the Brody distribution. For complex spectra, the level-spacing ratios and distributions are governed by time-reversal symmetry. The spectral statistics align with predictions for non-Hermitian random matrix ensembles in classes AI† and A, depending on the presence or absence of time-reversal symmetry. Our results provide insights into the spectral characteristics of non-Hermitian quantum chaotic systems and their connection to PT symmetry. △ Less

Submitted 27 February, 2025; v1 submitted 27 February, 2025; originally announced February 2025.

Comments: 7 pages with 7 figures

Establishing a New Benchmark in Quantum Computational Advantage with 105-qubit Zuchongzhi 3.0 Processor

Authors: Dongxin Gao, Daojin Fan, Chen Zha, Jiahao Bei, Guoqing Cai, Jianbin Cai, Sirui Cao, Xiangdong Zeng, Fusheng Chen, Jiang Chen, Kefu Chen, Xiawei Chen, Xiqing Chen, Zhe Chen, Zhiyuan Chen, Zihua Chen, Wenhao Chu, Hui Deng, Zhibin Deng, Pei Ding, Xun Ding, Zhuzhengqi Ding, Shuai Dong, Yupeng Dong, Bo Fan , et al. (129 additional authors not shown)

Abstract: In the relentless pursuit of quantum computational advantage, we present a significant advancement with the development of Zuchongzhi 3.0. This superconducting quantum computer prototype, comprising 105 qubits, achieves high operational fidelities, with single-qubit gates, two-qubit gates, and readout fidelity at 99.90%, 99.62% and 99.18%, respectively. Our experiments with an 83-qubit, 32-cycle r… ▽ More In the relentless pursuit of quantum computational advantage, we present a significant advancement with the development of Zuchongzhi 3.0. This superconducting quantum computer prototype, comprising 105 qubits, achieves high operational fidelities, with single-qubit gates, two-qubit gates, and readout fidelity at 99.90%, 99.62% and 99.18%, respectively. Our experiments with an 83-qubit, 32-cycle random circuit sampling on Zuchongzhi 3.0 highlight its superior performance, achieving one million samples in just a few hundred seconds. This task is estimated to be infeasible on the most powerful classical supercomputers, Frontier, which would require approximately $6.4\times 10^9$ years to replicate the task. This leap in processing power places the classical simulation cost six orders of magnitude beyond Google's SYC-67 and SYC-70 experiments [Nature 634, 328(2024)], firmly establishing a new benchmark in quantum computational advantage. Our work not only advances the frontiers of quantum computing but also lays the groundwork for a new era where quantum processors play an essential role in tackling sophisticated real-world challenges. △ Less

Submitted 16 December, 2024; originally announced December 2024.

High-efficiency On-chip Quantum Photon Source in Modal Phase-matched Lithium Niobate Nanowaveguide

Authors: Xiao-Xu Fang, Hao-Yang Du, Xiuquan Zhang, Lei Wang, Feng Chen, He Lu

Abstract: Thin-film lithium niobate on insulator~(LNOI) emerges as a promising platform for integrated quantum photon source, enabling scalable on-chip quantum information processing. The most popular technique to overcome the phase mismatching between interacting waves in waveguide is periodic poling, which is intrinsically sensitive to poling uniformity. Here, we report an alternative strategy to offset t… ▽ More Thin-film lithium niobate on insulator~(LNOI) emerges as a promising platform for integrated quantum photon source, enabling scalable on-chip quantum information processing. The most popular technique to overcome the phase mismatching between interacting waves in waveguide is periodic poling, which is intrinsically sensitive to poling uniformity. Here, we report an alternative strategy to offset the phase mismatching of spontaneous parametric down-conversion~(SPDC) process, so-called modal phase matching, in a straight waveguide fabricated on a dual-layer LNOI. The dual-layer LNOI consists of two 300~nm lithium niobates with opposite directions, which significantly enhances the spatial overlap between fundamental and high-order modes and thus enables efficient SPDC. This dual-layer waveguide generates photon pairs with pair generation rate of 41.77~GHz/mW, which exhibits excellent signal-to-noise performance with coincidence-to-accidental ratio up to 58298$\pm$1297. Moreover, we observe a heralded single-photon source with second-order autocorrelation $g_{H}^{(2)}(0)<0.2$ and heralded rate exceeding 100~kHz. Our results provide an experiment-friendly approach for efficient generation of quantum photon sources and benefit the on-chip quantum information processing based on LNOI. △ Less

Submitted 15 December, 2024; originally announced December 2024.

Comments: 6 pages, 4 figures

Journal ref: Laser Photonics Rev 2024, 2400782

Integrated spectrally multiplexed light-matter interface at telecom band

Authors: Xueying Zhang, Bin Zhang, Shihai Wei, Hao Li, Jinyu Liao, Tao Zhou, Guangwei Deng, You Wang, Haizhi Song, Lixing You, Boyu Fan, Yunru Fan, Feng Chen, Guangcan Guo, Qiang Zhou

Abstract: Light-matter interface is an important building block for long-distance quantum networks. Towards a scalable quantum network with high-rate quantum information processing, it requires to develop integrated light-matter interfaces with broadband and multiplexing capacities. Here we demonstrate a light-matter interface at telecom band in an integrated system. A five-spectral-channel atomic-frequency… ▽ More Light-matter interface is an important building block for long-distance quantum networks. Towards a scalable quantum network with high-rate quantum information processing, it requires to develop integrated light-matter interfaces with broadband and multiplexing capacities. Here we demonstrate a light-matter interface at telecom band in an integrated system. A five-spectral-channel atomic-frequency-comb photonic memory is prepared on a laser-written Er3+:LiNbO3 chip. The bandwidth of each channel is 4 GHz with a channel spacing of 15 GHz. The signal photons from time-bin entangled photon pairs at telecom band are sent into the on-chip memory and recalled after a storage time of 152 ns. The entanglement-preserving nature of our integrated quantum interface is assessed by an input/output fidelity of >92% for all the five spectral channels. Our light-matter interfaces constitute a notable step forward toward a high-rate quantum network involving integrated device. △ Less

Submitted 24 October, 2024; originally announced October 2024.

System Symmetry and the Classification of Out-of-Time-Ordered Correlator Dynamics in Quantum Chaos

Authors: Fuxing Chen, Ping Fang

Abstract: The symmetry of chaotic systems plays a pivotal role in determining the universality class of spectral statistics and dynamical behaviors, which can be described within the framework of random matrix theory. Understanding the influence of system symmetry on these behaviors is crucial for characterizing universal properties in quantum chaotic systems. In this work, we explore the universality of ou… ▽ More The symmetry of chaotic systems plays a pivotal role in determining the universality class of spectral statistics and dynamical behaviors, which can be described within the framework of random matrix theory. Understanding the influence of system symmetry on these behaviors is crucial for characterizing universal properties in quantum chaotic systems. In this work, we explore the universality of out-of-time-ordered correlator (OTOC) dynamics in quantum chaotic systems, focusing on the kicked rotor and the kicked Harper model. By modulating the periodically kicked potential, we control system symmetry to examine its impact on OTOC dynamics and level spacing distributions. Our results show that ensemble-averaged OTOC dynamics exhibit distinct universal behaviors depending on system symmetry, enabling classification through random matrix theory. These distinctions become evident after the localization time in localized regimes and emerge at specific time scales corresponding to the translational period of the Floquet operator in momentum space under quantum resonance conditions. Our findings provide a rigorous understanding of the relationship between symmetry and quantum chaotic dynamics, contributing to a deeper comprehension of universal behaviors in these systems. △ Less

Submitted 6 October, 2024; originally announced October 2024.

Comments: 9 pages,7 figures

Entanglement distribution over 155 km metropolitan fiber using a CMOS-compatible silicon chip

Authors: Jinyi Du, Xingjian Zhang, George F. R. Chen, Hongwei Gao, Dawn T. H. Tan, Alexander Ling

Abstract: Transmitting entangled states over long distances is crucial for developing quantum networks. Previous demonstrations using satellites or fibers relied on photon pairs generated from bulk crystal arrangements. Polarization entanglement distribution based on CMOS-compatible silicon chips has long been restricted to lab-scale demonstrations spanning only a few meters, due to the difficulty of achiev… ▽ More Transmitting entangled states over long distances is crucial for developing quantum networks. Previous demonstrations using satellites or fibers relied on photon pairs generated from bulk crystal arrangements. Polarization entanglement distribution based on CMOS-compatible silicon chips has long been restricted to lab-scale demonstrations spanning only a few meters, due to the difficulty of achieving sufficient off-chip brightness. We report a silicon chip platform that provides an off-chip entangled photon pair brightness ranging from 8,000 to 460,000 pairs per second, exceeding previous reports by three orders of magnitude. The entanglement fidelity reaches 99.85(6)% and 97.90(3)%, respectively. After addressing key challenges in long distance entanglement distribution over deployed fiber, including phase drift and chromatic dispersion, entangled photons were successfully distributed over 155 km (66 dB loss). These results demonstrate that CMOS-compatible silicon chips can perform competitively with bulk crystal sources and represent an important step toward scalable, chip-based quantum networks. △ Less

Submitted 16 March, 2026; v1 submitted 26 September, 2024; originally announced September 2024.

Journal ref: Du, Jinyi, et al. "Entanglement distribution over 155 km metropolitan fiber using a CMOS-compatible silicon chip." Newton 1.10 (2025)

LSTM-QGAN: Scalable NISQ Generative Adversarial Network

Authors: Cheng Chu, Aishwarya Hastak, Fan Chen

Abstract: Current quantum generative adversarial networks (QGANs) still struggle with practical-sized data. First, many QGANs use principal component analysis (PCA) for dimension reduction, which, as our studies reveal, can diminish the QGAN's effectiveness. Second, methods that segment inputs into smaller patches processed by multiple generators face scalability issues. In this work, we propose LSTM-QGAN,… ▽ More Current quantum generative adversarial networks (QGANs) still struggle with practical-sized data. First, many QGANs use principal component analysis (PCA) for dimension reduction, which, as our studies reveal, can diminish the QGAN's effectiveness. Second, methods that segment inputs into smaller patches processed by multiple generators face scalability issues. In this work, we propose LSTM-QGAN, a QGAN architecture that eliminates PCA preprocessing and integrates quantum long short-term memory (QLSTM) to ensure scalable performance. Our experiments show that LSTM-QGAN significantly enhances both performance and scalability over state-of-the-art QGAN models, with visual data improvements, reduced Frechet Inception Distance scores, and reductions of 5x in qubit counts, 5x in single-qubit gates, and 12x in two-qubit gates. △ Less

Submitted 9 January, 2025; v1 submitted 3 September, 2024; originally announced September 2024.

Quantum Neural Network Extraction Attack via Split Co-Teaching

Authors: Zhenxiao Fu, Fan Chen

Abstract: Quantum Neural Networks (QNNs), now offered as QNN-as-a-Service (QNNaaS), have become key targets for model extraction attacks. Existing methods use ensemble learning to train substitute QNNs, but our analysis reveals significant limitations in real-world environments, where noise and cost constraints undermine their effectiveness. In this work, we introduce a novel attack, \textit{split co-teachi… ▽ More Quantum Neural Networks (QNNs), now offered as QNN-as-a-Service (QNNaaS), have become key targets for model extraction attacks. Existing methods use ensemble learning to train substitute QNNs, but our analysis reveals significant limitations in real-world environments, where noise and cost constraints undermine their effectiveness. In this work, we introduce a novel attack, \textit{split co-teaching}, which uses label variations to \textit{split} queried data by noise sensitivity and employs \textit{co-teaching} schemes to enhance extraction accuracy. The experimental results show that our approach outperforms classical extraction attacks by 6.5\%$\sim$9.5\% and existing QNN extraction methods by 0.1\%$\sim$3.7\% across various tasks. △ Less

Submitted 9 January, 2025; v1 submitted 3 September, 2024; originally announced September 2024.

In situ Qubit Frequency Tuning Circuit for Scalable Superconducting Quantum Computing: Scheme and Experiment

Authors: Lei Jiang, Yu Xu, Shaowei Li, Zhiguang Yan, Ming Gong, Tao Rong, Chenyin Sun, Tianzuo Sun, Tao Jiang, Hui Deng, Chen Zha, Jin Lin, Fusheng Chen, Qingling Zhu, Yangsen Ye, Hao Rong, Kai Yan, Sirui Cao, Yuan Li, Shaojun Guo, Haoran Qian, Yisen Hu, Yulin Wu, Yuhuai Li, Gang Wu , et al. (8 additional authors not shown)

Abstract: Frequency tunable qubit plays a significant role for scalable superconducting quantum processors. The state-of-the-art room-temperature electronics for tuning qubit frequency suffers from unscalable limit, such as heating problem, linear growth of control cables, etc. Here we propose a scalable scheme to tune the qubit frequency by using in situ superconducting circuit, which is based on radio fre… ▽ More Frequency tunable qubit plays a significant role for scalable superconducting quantum processors. The state-of-the-art room-temperature electronics for tuning qubit frequency suffers from unscalable limit, such as heating problem, linear growth of control cables, etc. Here we propose a scalable scheme to tune the qubit frequency by using in situ superconducting circuit, which is based on radio frequency superconducting quantum interference device (rf-SQUID). We demonstrate both theoretically and experimentally that the qubit frequency could be modulated by inputting several single pulses into rf-SQUID. Compared with the traditional scheme, our scheme not only solves the heating problem, but also provides the potential to exponentially reduce the number of cables inside the dilute refrigerator and the room-temperature electronics resource for tuning qubit frequency, which is achieved by a time-division-multiplex (TDM) scheme combining rf-SQUID with switch arrays. With such TDM scheme, the number of cables could be reduced from the usual $\sim 3n$ to $\sim \log_2{(3n)} + 1$ for two-dimensional quantum processors comprising $n$ qubits and $\sim 2n$ couplers. Our work paves the way for large-scale control of superconducting quantum processor. △ Less

Submitted 26 December, 2024; v1 submitted 31 July, 2024; originally announced July 2024.

Comments: 9 pages, 6 figures

Journal ref: Phys. Rev. Applied 24, 034057 (2025)

Light-induced fictitious magnetic fields for quantum storage in cold atomic ensembles

Authors: Jianmin Wang, Liang Dong, Xingchang Wang, Zihan Zhou, Ying Zuo, Georgios A. Siviloglou, J. F. Chen

Abstract: In this work, we have demonstrated that optically generated fictitious magnetic fields can be utilized to extend the lifetime of quantum memories in cold atomic ensembles. All the degrees of freedom of an AC Stark shift such as polarization, spatial profile, and temporal waveform can be readily controlled in a precise manner. Temporal fluctuations over several experimental cycles, and spatial inho… ▽ More In this work, we have demonstrated that optically generated fictitious magnetic fields can be utilized to extend the lifetime of quantum memories in cold atomic ensembles. All the degrees of freedom of an AC Stark shift such as polarization, spatial profile, and temporal waveform can be readily controlled in a precise manner. Temporal fluctuations over several experimental cycles, and spatial inhomogeneities along a cold atomic gas have been compensated by an optical beam. The advantage of the use of fictitious magnetic fields for quantum storage stems from the speed and spatial precision that these fields can be synthesized. Our simple and versatile technique can find widespread application in coherent pulse and single-photon storage in any atomic species. △ Less

Submitted 12 June, 2024; originally announced June 2024.

Comments: 14pages,8 figures

JustQ: Automated Deployment of Fair and Accurate Quantum Neural Networks

Authors: Ruhan Wang, Fahiz Baba-Yara, Fan Chen

Abstract: Despite the success of Quantum Neural Networks (QNNs) in decision-making systems, their fairness remains unexplored, as the focus primarily lies on accuracy. This work conducts a design space exploration, unveiling QNN unfairness, and highlighting the significant influence of QNN deployment and quantum noise on accuracy and fairness. To effectively navigate the vast QNN deployment design space, we… ▽ More Despite the success of Quantum Neural Networks (QNNs) in decision-making systems, their fairness remains unexplored, as the focus primarily lies on accuracy. This work conducts a design space exploration, unveiling QNN unfairness, and highlighting the significant influence of QNN deployment and quantum noise on accuracy and fairness. To effectively navigate the vast QNN deployment design space, we propose JustQ, a framework for deploying fair and accurate QNNs on NISQ computers. It includes a complete NISQ error model, reinforcement learning-based deployment, and a flexible optimization objective incorporating both fairness and accuracy. Experimental results show JustQ outperforms previous methods, achieving superior accuracy and fairness. This work pioneers fair QNN design on NISQ computers, paving the way for future investigations. △ Less

Submitted 16 March, 2024; originally announced March 2024.

Journal ref: published at ASP-DAC 2024

QuantumLeak: Stealing Quantum Neural Networks from Cloud-based NISQ Machines

Authors: Zhenxiao Fu, Min Yang, Cheng Chu, Yilun Xu, Gang Huang, Fan Chen

Abstract: Variational quantum circuits (VQCs) have become a powerful tool for implementing Quantum Neural Networks (QNNs), addressing a wide range of complex problems. Well-trained VQCs serve as valuable intellectual assets hosted on cloud-based Noisy Intermediate Scale Quantum (NISQ) computers, making them susceptible to malicious VQC stealing attacks. However, traditional model extraction techniques desig… ▽ More Variational quantum circuits (VQCs) have become a powerful tool for implementing Quantum Neural Networks (QNNs), addressing a wide range of complex problems. Well-trained VQCs serve as valuable intellectual assets hosted on cloud-based Noisy Intermediate Scale Quantum (NISQ) computers, making them susceptible to malicious VQC stealing attacks. However, traditional model extraction techniques designed for classical machine learning models encounter challenges when applied to NISQ computers due to significant noise in current devices. In this paper, we introduce QuantumLeak, an effective and accurate QNN model extraction technique from cloud-based NISQ machines. Compared to existing classical model stealing techniques, QuantumLeak improves local VQC accuracy by 4.99\%$\sim$7.35\% across diverse datasets and VQC architectures. △ Less

Submitted 15 March, 2024; originally announced March 2024.

Journal ref: published in IJCNN 2024

Quantum Mixed-State Self-Attention Network

Authors: Fu Chen, Qinglin Zhao, Li Feng, Chuangtao Chen, Yangbin Lin, Jianhong Lin

Abstract: Attention mechanisms have revolutionized natural language processing. Combining them with quantum computing aims to further advance this technology. This paper introduces a novel Quantum Mixed-State Self-Attention Network (QMSAN) for natural language processing tasks. Our model leverages quantum computing principles to enhance the effectiveness of self-attention mechanisms. QMSAN uses a quantum at… ▽ More Attention mechanisms have revolutionized natural language processing. Combining them with quantum computing aims to further advance this technology. This paper introduces a novel Quantum Mixed-State Self-Attention Network (QMSAN) for natural language processing tasks. Our model leverages quantum computing principles to enhance the effectiveness of self-attention mechanisms. QMSAN uses a quantum attention mechanism based on mixed state, allowing for direct similarity estimation between queries and keys in the quantum domain. This approach leads to more effective attention coefficient calculations. We also propose an innovative quantum positional encoding scheme, implemented through fixed quantum gates within the circuit, improving the model's ability to capture sequence information without additional qubit resources. In numerical experiments of text classification tasks on public datasets, QMSAN outperforms Quantum Self-Attention Neural Network (QSANN). Furthermore, we demonstrate QMSAN's robustness in different quantum noise environments, highlighting its potential for near-term quantum devices. △ Less

Submitted 30 November, 2024; v1 submitted 5 March, 2024; originally announced March 2024.

TITAN: A Distributed Large-Scale Trapped-Ion NISQ Computer

Authors: Cheng Chu, Zhenxiao Fu, Yilun Xu, Gang Huang, Hausi Muller, Fan Chen, Lei Jiang

Abstract: Trapped-Ion (TI) technology offers potential breakthroughs for Noisy Intermediate Scale Quantum (NISQ) computing. TI qubits offer extended coherence times and high gate fidelity, making them appealing for large-scale NISQ computers. Constructing such computers demands a distributed architecture connecting Quantum Charge Coupled Devices (QCCDs) via quantum matter-links and photonic switches. Howeve… ▽ More Trapped-Ion (TI) technology offers potential breakthroughs for Noisy Intermediate Scale Quantum (NISQ) computing. TI qubits offer extended coherence times and high gate fidelity, making them appealing for large-scale NISQ computers. Constructing such computers demands a distributed architecture connecting Quantum Charge Coupled Devices (QCCDs) via quantum matter-links and photonic switches. However, current distributed TI NISQ computers face hardware and system challenges. Entangling qubits across a photonic switch introduces significant latency, while existing compilers generate suboptimal mappings due to their unawareness of the interconnection topology. In this paper, we introduce TITAN, a large-scale distributed TI NISQ computer, which employs an innovative photonic interconnection design to reduce entanglement latency and an advanced partitioning and mapping algorithm to optimize matter-link communications. Our evaluations show that TITAN greatly enhances quantum application performance by 56.6% and fidelity by 19.7% compared to existing systems. △ Less

Submitted 16 February, 2024; originally announced February 2024.

Demonstration of a low loss, highly stable and re-useable edge coupler for high heralding efficiency and low g^(2) (0) SOI correlated photon pair sources

Authors: Jinyi Du, George F. R. Chen, Hongwei Gao, James A. Grieve, Dawn T. H. Tan, Alexander Ling

Abstract: We report a stable, low loss method for coupling light from silicon-on-insulator (SOI) photonic chips into optical fibers. The technique is realized using an on-chip tapered waveguide and a cleaved small core optical fiber. The on-chip taper is monolithic and does not require a patterned cladding, thus simplifying the chip fabrication process. The optical fiber segment is composed of a centimeter-… ▽ More We report a stable, low loss method for coupling light from silicon-on-insulator (SOI) photonic chips into optical fibers. The technique is realized using an on-chip tapered waveguide and a cleaved small core optical fiber. The on-chip taper is monolithic and does not require a patterned cladding, thus simplifying the chip fabrication process. The optical fiber segment is composed of a centimeter-long small core fiber (UHNA7) which is spliced to SMF-28 fiber with less than -0.1 dB loss. We observe an overall coupling loss of -0.64 dB with this design. The chip edge and fiber tip can be butt coupled without damaging the on-chip taper or fiber. Friction between the surfaces maintains alignment leading to an observation of +-0.1 dB coupling fluctuation during a ten-day continuous measurement without use of any adhesive. This technique minimizes the potential for generating Raman noise in the fiber, and has good stability compared to coupling strategies based on longer UHNA fibers or fragile lensed fibers. We also applied the edge coupler on a correlated photon pair source and observed a raw coincidence count rate of 1.21 million cps and raw heralding efficiency of 21.3%. We achieved an auto correlation function g^(2) (0) as low as 0.0004 at the low pump power regime. △ Less

Submitted 14 March, 2024; v1 submitted 28 December, 2023; originally announced December 2023.

Journal ref: "Demonstration of a low loss, highly stable and re-useable edge coupler for high heralding efficiency and low g(2)(0) SOI correlated photon pair sources," Opt. Express 32, 11406-11418 (2024)

Twin-field quantum key distribution with local frequency reference

Authors: Jiu-Peng Chen, Fei Zhou, Chi Zhang, Cong Jiang, Fa-Xi Chen, Jia Huang, Hao Li, Li-Xing You, Xiang-Bin Wang, Yang Liu, Qiang Zhang, Jian-Wei Pan

Abstract: Twin-field quantum key distribution (TF-QKD) overcomes the linear rate-loss limit, which promises a boost of secure key rate over long distance. However, the complexity of eliminating the frequency differences between the independent laser sources hinders its practical application. Here, taking the saturated absorption spectroscopy of acetylene as an absolute reference, we propose and demonstrate… ▽ More Twin-field quantum key distribution (TF-QKD) overcomes the linear rate-loss limit, which promises a boost of secure key rate over long distance. However, the complexity of eliminating the frequency differences between the independent laser sources hinders its practical application. Here, taking the saturated absorption spectroscopy of acetylene as an absolute reference, we propose and demonstrate a simple and practical approach to realize TF-QKD without requiring relative frequency control of the independent laser sources. Adopting the 4-intensity sending-or-not-sending TF-QKD protocol, we experimentally demonstrate the TF-QKD over 502 km, 301 km and 201 km ultra-low loss optical fiber respectively. We expect this high-performance scheme will find widespread usage in future intercity and free-space quantum communication networks. △ Less

Submitted 27 October, 2023; originally announced October 2023.

Comments: 13 pages, 5 figures, 7 tables

Journal ref: Phys. Rev. Lett. 132, 260802 (2024)

QDoor: Exploiting Approximate Synthesis for Backdoor Attacks in Quantum Neural Networks

Authors: Cheng Chu, Fan Chen, Philip Richerme, Lei Jiang

Abstract: Quantum neural networks (QNNs) succeed in object recognition, natural language processing, and financial analysis. To maximize the accuracy of a QNN on a Noisy Intermediate Scale Quantum (NISQ) computer, approximate synthesis modifies the QNN circuit by reducing error-prone 2-qubit quantum gates. The success of QNNs motivates adversaries to attack QNNs via backdoors. However, naïvely transplanting… ▽ More Quantum neural networks (QNNs) succeed in object recognition, natural language processing, and financial analysis. To maximize the accuracy of a QNN on a Noisy Intermediate Scale Quantum (NISQ) computer, approximate synthesis modifies the QNN circuit by reducing error-prone 2-qubit quantum gates. The success of QNNs motivates adversaries to attack QNNs via backdoors. However, naïvely transplanting backdoors designed for classical neural networks to QNNs yields only low attack success rate, due to the noises and approximate synthesis on NISQ computers. Prior quantum circuit-based backdoors cannot selectively attack some inputs or work with all types of encoding layers of a QNN circuit. Moreover, it is easy to detect both transplanted and circuit-based backdoors in a QNN. In this paper, we propose a novel and stealthy backdoor attack, QDoor, to achieve high attack success rate in approximately-synthesized QNN circuits by weaponizing unitary differences between uncompiled QNNs and their synthesized counterparts. QDoor trains a QNN behaving normally for all inputs with and without a trigger. However, after approximate synthesis, the QNN circuit always predicts any inputs with a trigger to a predefined class while still acts normally for benign inputs. Compared to prior backdoor attacks, QDoor improves the attack success rate by $13\times$ and the clean data accuracy by $65\%$ on average. Furthermore, prior backdoor detection techniques cannot find QDoor attacks in uncompiled QNN circuits. △ Less

Submitted 16 February, 2024; v1 submitted 13 July, 2023; originally announced July 2023.

Detection of entangled states supported by reinforcement learning

Authors: Jia-Hao Cao, Feng Chen, Qi Liu, Tian-Wei Mao, Wen-Xin Xu, Ling-Na Wu, Li You

Abstract: Discrimination of entangled states is an important element of quantum enhanced metrology. This typically requires low-noise detection technology. Such a challenge can be circumvented by introducing nonlinear readout process. Traditionally, this is realized by reversing the very dynamics that generates the entangled state, which requires a full control over the system evolution. In this work, we pr… ▽ More Discrimination of entangled states is an important element of quantum enhanced metrology. This typically requires low-noise detection technology. Such a challenge can be circumvented by introducing nonlinear readout process. Traditionally, this is realized by reversing the very dynamics that generates the entangled state, which requires a full control over the system evolution. In this work, we present nonlinear readout of highly entangled states by employing reinforcement learning (RL) to manipulate the spin-mixing dynamics in a spin-1 atomic condensate. The RL found results in driving the system towards an unstable fixed point, whereby the (to be sensed) phase perturbation is amplified by the subsequent spin-mixing dynamics. Working with a condensate of 10900 {87}^Rb atoms, we achieve a metrological gain of 6.97 dB beyond the classical precision limit. Our work would open up new possibilities in unlocking the full potential of entanglement caused quantum enhancement in experiments. △ Less

Submitted 18 July, 2023; originally announced July 2023.

Journal ref: Phys. Rev. Lett. 131, 073201 (2023)

CryptoQFL: Quantum Federated Learning on Encrypted Data

Authors: Cheng Chu, Lei Jiang, Fan Chen

Abstract: Recent advancements in Quantum Neural Networks (QNNs) have demonstrated theoretical and experimental performance superior to their classical counterparts in a wide range of applications. However, existing centralized QNNs cannot solve many real-world problems because collecting large amounts of training data to a common public site is time-consuming and, more importantly, violates data privacy. Fe… ▽ More Recent advancements in Quantum Neural Networks (QNNs) have demonstrated theoretical and experimental performance superior to their classical counterparts in a wide range of applications. However, existing centralized QNNs cannot solve many real-world problems because collecting large amounts of training data to a common public site is time-consuming and, more importantly, violates data privacy. Federated Learning (FL) is an emerging distributed machine learning framework that allows collaborative model training on decentralized data residing on multiple devices without breaching data privacy. Some initial attempts at Quantum Federated Learning (QFL) either only focus on improving the QFL performance or rely on a trusted quantum server that fails to preserve data privacy. In this work, we propose CryptoQFL, a QFL framework that allows distributed QNN training on encrypted data. CryptoQFL is (1) secure, because it allows each edge to train a QNN with local private data, and encrypt its updates using quantum \homo~encryption before sending them to the central quantum server; (2) communication-efficient, as CryptoQFL quantize local gradient updates to ternary values, and only communicate non-zero values to the server for aggregation; and (3) computation-efficient, as CryptoQFL presents an efficient quantum aggregation circuit with significantly reduced latency compared to state-of-the-art approaches. △ Less

Submitted 13 July, 2023; originally announced July 2023.

Telecom-band integrated multimode photonic quantum memory

Authors: Xueying Zhang, Bin Zhang, Shihai Wei, Hao Li, Jinyu Liao, Cheng Li, Guangwei Deng, You Wang, Haizhi Song, Lixing You, Bo Jing, Feng Chen, Guang-Can Guo, Qiang Zhou

Abstract: Telecom-band integrated quantum memory is an elementary building block for developing quantum networks compatible with fiber communication infrastructures. Towards such a network with large capacity, an integrated multimode photonic quantum memory at telecom band has yet been demonstrated. Here we report a fiber-integrated multimode quantum storage of single photon at telecom band on a laser-writt… ▽ More Telecom-band integrated quantum memory is an elementary building block for developing quantum networks compatible with fiber communication infrastructures. Towards such a network with large capacity, an integrated multimode photonic quantum memory at telecom band has yet been demonstrated. Here we report a fiber-integrated multimode quantum storage of single photon at telecom band on a laser-written chip. The storage device is a fiber-pigtailed Er3+:LiNbO3 waveguide and allows a storage of up to 330 temporal modes of heralded single photon with 4-GHz-wide bandwidth at 1532 nm and a 167-fold increasing of coincidence detection rate with respect to single mode. Our memory system with all-fiber addressing is performed using telecom-band fiber-integrated and on-chip devices. The results represent an important step for the future quantum networks using integrated photonics devices. △ Less

Submitted 13 June, 2023; originally announced June 2023.

Logical Magic State Preparation with Fidelity Beyond the Distillation Threshold on a Superconducting Quantum Processor

Authors: Yangsen Ye, Tan He, He-Liang Huang, Zuolin Wei, Yiming Zhang, Youwei Zhao, Dachao Wu, Qingling Zhu, Huijie Guan, Sirui Cao, Fusheng Chen, Tung-Hsun Chung, Hui Deng, Daojin Fan, Ming Gong, Cheng Guo, Shaojun Guo, Lianchen Han, Na Li, Shaowei Li, Yuan Li, Futian Liang, Jin Lin, Haoran Qian, Hao Rong , et al. (13 additional authors not shown)

Abstract: Fault-tolerant quantum computing based on surface code has emerged as an attractive candidate for practical large-scale quantum computers to achieve robust noise resistance. To achieve universality, magic states preparation is a commonly approach for introducing non-Clifford gates. Here, we present a hardware-efficient and scalable protocol for arbitrary logical state preparation for the rotated s… ▽ More Fault-tolerant quantum computing based on surface code has emerged as an attractive candidate for practical large-scale quantum computers to achieve robust noise resistance. To achieve universality, magic states preparation is a commonly approach for introducing non-Clifford gates. Here, we present a hardware-efficient and scalable protocol for arbitrary logical state preparation for the rotated surface code, and further experimentally implement it on the \textit{Zuchongzhi} 2.1 superconducting quantum processor. An average of \hhl{$0.8983 \pm 0.0002$} logical fidelity at different logical states with distance-three is achieved, \hhl{taking into account both state preparation and measurement errors.} In particular, \hhl{the magic states $|A^{π/4}\rangle_L$, $|H\rangle_L$, and $|T\rangle_L$ are prepared non-destructively with logical fidelities of $0.8771 \pm 0.0009 $, $0.9090 \pm 0.0009 $, and $0.8890 \pm 0.0010$, respectively, which are higher than the state distillation protocol threshold, 0.859 (for H-type magic state) and 0.827 (for T -type magic state).} Our work provides a viable and efficient avenue for generating high-fidelity raw logical magic states, which is essential for realizing non-Clifford logical gates in the surface code. △ Less

Submitted 30 May, 2023; v1 submitted 25 May, 2023; originally announced May 2023.

Comments: In this version, we do not employ readout error mitigation strategies (in the previous version, we use readout transition matrix to mitigate the measurement error) to remove measurement errors because we believe it provides a more predictive assessment of the actual fidelity when generating and consuming magic states for a non-Clifford gate, as consuming the state involves measurement

Configured Quantum Reservoir Computing for Multi-Task Machine Learning

Authors: Wei Xia, Jie Zou, Xingze Qiu, Feng Chen, Bing Zhu, Chunhe Li, Dong-Ling Deng, Xiaopeng Li

Abstract: Amidst the rapid advancements in experimental technology, noise-intermediate-scale quantum (NISQ) devices have become increasingly programmable, offering versatile opportunities to leverage quantum computational advantage. Here we explore the intricate dynamics of programmable NISQ devices for quantum reservoir computing. Using a genetic algorithm to configure the quantum reservoir dynamics, we sy… ▽ More Amidst the rapid advancements in experimental technology, noise-intermediate-scale quantum (NISQ) devices have become increasingly programmable, offering versatile opportunities to leverage quantum computational advantage. Here we explore the intricate dynamics of programmable NISQ devices for quantum reservoir computing. Using a genetic algorithm to configure the quantum reservoir dynamics, we systematically enhance the learning performance. Remarkably, a single configured quantum reservoir can simultaneously learn multiple tasks, including a synthetic oscillatory network of transcriptional regulators, chaotic motifs in gene regulatory networks, and the fractional-order Chua's circuit. Our configured quantum reservoir computing yields highly precise predictions for these learning tasks, outperforming classical reservoir computing. We also test the configured quantum reservoir computing in foreign exchange (FX) market applications and demonstrate its capability to capture the stochastic evolution of the exchange rates with significantly greater accuracy than classical reservoir computing approaches. Through comparison with classical reservoir computing, we highlight the unique role of quantum coherence in the quantum reservoir, which underpins its exceptional learning performance. Our findings suggest the exciting potential of configured quantum reservoir computing for exploiting the quantum computation power of NISQ devices in developing artificial general intelligence. △ Less

Submitted 30 March, 2023; originally announced March 2023.