Showing 1–50 of 112 results for author: Chang, Y

Two-Photon Bound States in the Continuum: A No-Go Theorem and Long-Lived Quasi-Bound States

Authors: Yue Chang

Abstract: Two-photon bound states in the continuum provide a stringent setting in which quantum interference must suppress radiative decay despite photon--photon interactions. Here, we prove a no-go theorem showing that a single nonlinear mode linearly coupled to a noninteracting bosonic continuum cannot support an interaction-active two-photon bound state in the continuum, provided the local spectral densi… ▽ More Two-photon bound states in the continuum provide a stringent setting in which quantum interference must suppress radiative decay despite photon--photon interactions. Here, we prove a no-go theorem showing that a single nonlinear mode linearly coupled to a noninteracting bosonic continuum cannot support an interaction-active two-photon bound state in the continuum, provided the local spectral density vanishes only at isolated energies. Apart from this regularity condition, the result is independent of the continuum dispersion and the frequency-dependent coupling. Although such an exact bound state is forbidden, long-lived two-photon quasi-bound states remain possible. For a giant Kerr cavity nonlocally coupled to a waveguide, we derive the asymptotic scaling of the two-photon decay rate with the coupling-point separation from weak nonlinearity to the hard-core limit. We further identify a regime in which the two-photon resonance is substantially longer lived than the single-photon excitation. Our work establishes the limits of exact multiphoton confinement while opening a route to engineering long-lived interacting few-photon states. △ Less

Submitted 17 August, 2026; originally announced August 2026.

Engineering Nanodiamonds for Quantum Sensing: Material Constraints at the Nanoscale

Authors: Ashutosh Rathi, Keisuke Oshimi, Kento Sasaki, Kensuke Kobayashi, Yutaka Shikano, Oliver Benson, Tim Schröder, Shery L. Y. Chang, Masazumi Fujiwara

Abstract: Optically addressable solid-state spin defects have emerged as powerful multimodal quantum sensors, with nitrogen-vacancy (NV) centers in bulk diamond providing benchmark quantum control and sensitivity under ambient conditions. Embedding such defects in nanodiamonds (NDs) extends these capabilities to mobile probes capable of accessing complex biological and nanoscale environments. Reduced dimens… ▽ More Optically addressable solid-state spin defects have emerged as powerful multimodal quantum sensors, with nitrogen-vacancy (NV) centers in bulk diamond providing benchmark quantum control and sensitivity under ambient conditions. Embedding such defects in nanodiamonds (NDs) extends these capabilities to mobile probes capable of accessing complex biological and nanoscale environments. Reduced dimensions, however, introduce constraints beyond volumetric spin impurities, notably enhanced lattice strain and surface-induced noise sources, which shorten NV spin relaxation times (T1 and T2) and destabilize the NV charge state, as well as resulting in pronounced particle-to-particle variability in NDs typically produced by top-down approaches. These effects complicate both sensing performance and the quantitative interpretation of multimodal signals in realistic environments. This article provides a structured perspective on the physical mechanisms by which material properties constrain NV behavior in NDs, together with mitigation strategies that shape the robust use of these mobile quantum sensors for biosensing and nanoscale science. △ Less

Submitted 5 August, 2026; originally announced August 2026.

Comments: Published in ACS Nano under CC-BY 4.0

Journal ref: ACS Nano 20, 17143 (2026)

Efficient Analysis of Carrier Transport and TM-TE Emission in AlGaN UVC LEDs via Multi-band Localization Landscape Theory

Authors: Yu-Ming Chang, Ping-Jie Zhuang, Marcel Filoche, Claude Weisbuch, James S. Speck, Yuh-Renn Wu

Abstract: AlGaN-based UVC LEDs (220-250 nm) suffer from poor hole confinement and strain-induced |Z>-band dominance at high Al content (>60%), leading to increased TM emission and reduced external quantum efficiency (EQE). While conventional k.p models combined with Schrodinger, Poisson, and drift-diffusion solvers are widely used to study optical transitions, they are computationally expensive. In this wor… ▽ More AlGaN-based UVC LEDs (220-250 nm) suffer from poor hole confinement and strain-induced |Z>-band dominance at high Al content (>60%), leading to increased TM emission and reduced external quantum efficiency (EQE). While conventional k.p models combined with Schrodinger, Poisson, and drift-diffusion solvers are widely used to study optical transitions, they are computationally expensive. In this work, we apply the multiband Localization Landscape (LL) model, including the effect of strain, as an alternative that replaces the eigenvalue problem to efficiently capture quantum effects and carrier localization. Using the 3D multi-band LL model with the Wigner-Weyl formalism, we reproduce emission and absorption spectra trends similar to the results in 3D k.p calculations, but with significantly reduced simulation time. The polarization ratio also agrees well with published experimental results across a wide spectral range. Furthermore, we analyze electrical characteristics such as band structure, polarization switching, and carrier confinement under alloy fluctuations and strain. This multi-band LL-based approach provides a fast and reliable solution for understanding and optimizing UVC LED performance. △ Less

Submitted 4 July, 2026; originally announced July 2026.

Comments: 11 figures

An Iterative Dual-Channel Neural Quantum State Algorithm for Selected Configuration Interaction

Authors: Jen-Yu Chang, Yi-Chun Chang, Yu-Jui Lin, Ming-Chun Yang, Hsiu-Chi Tsai, Tai-Yue Li, Nan Yow Chen, Tsung-Wei Huang, En-Jui Kuo

Abstract: Accurately solving the electronic Schrödinger equation for strongly correlated systems remains a central challenge in quantum chemistry, where the exponential growth of configuration space limits the applicability of exact methods. Selected Configuration Interaction (SCI) algorithms address this challenge by adaptively constructing compact determinantal expansions, yet their efficiency depends cri… ▽ More Accurately solving the electronic Schrödinger equation for strongly correlated systems remains a central challenge in quantum chemistry, where the exponential growth of configuration space limits the applicability of exact methods. Selected Configuration Interaction (SCI) algorithms address this challenge by adaptively constructing compact determinantal expansions, yet their efficiency depends critically on the quality of the sampling strategy used to identify chemically important configurations. Here we introduce the Handover Iterative Neural Quantum State (HI-NQS) algorithm, which embeds a classically trained autoregressive Transformer neural quantum state within the iterative sample--diagonalize--update framework of Sample-Based Quantum Diagonalization. A dual-channel Transformer architecture with explicit spin-up/spin-down cross-attention encodes fermionic spin structure as an architectural inductive bias, enabling expressive and physically informed wavefunction representations. After each subspace diagonalization, the resulting eigenvector is distilled back into the network through a factorized spin-marginal teacher signal, establishing a closed feedback loop between generative sampling and exact diagonalization. Benchmarks across a range of small molecules and a systematic nitrogen active-space series demonstrate that HI-NQS achieves chemical accuracy on all systems tested, with determinant-count scaling substantially more favorable than conventional CIPSI-based SCI for all but the smallest active spaces. All calculations are performed on GPU hardware without quantum computing resources, establishing HI-NQS as an efficient and scalable purely classical approach to the selected configuration interaction problem. △ Less

Submitted 25 June, 2026; originally announced June 2026.

Benchmarking Dark Matter Search using a Parity-Check Protocol with Machine-Learning Optimized Pulses

Authors: Yu-Han Chang, Ilya Moskalenko, Marko Kuzmanović, Ognjen Stanisavljević, Isak Björkman, David Díez-Ibáñez, Yikun Gu, Akash V. Dixit, Igor G. Irastorza, Gheorghe Sorin Paraoanu

Abstract: We report on an improved microwave detection protocol for dark matter candidates such as the axion and the dark photon. We employ a superconducting transmon qubit dispersively coupled to a double-cavity system, enabling quantum non-demolition measurements of the photon occupation in a relatively short-lived storage cavity. To reduce the experimental cycle time and enhance sensitivity for axion and… ▽ More We report on an improved microwave detection protocol for dark matter candidates such as the axion and the dark photon. We employ a superconducting transmon qubit dispersively coupled to a double-cavity system, enabling quantum non-demolition measurements of the photon occupation in a relatively short-lived storage cavity. To reduce the experimental cycle time and enhance sensitivity for axion and dark-photon searches, we operate this detector in a regime of increased qubit-cavity coupling, resulting in Stark shifts of 4.6 MHz. In this regime, conventional control pulses suffer from strong frequency-detuning sensitivity and photon-number-dependent errors. We address this limitation by implementing frequency-detuning-robust $π/2$ pulses (obtained by machine-learning optimization) that preserve high-fidelity qubit control over a bandwidth of approximately 20 MHz. We experimentally validate this protocol and demonstrate single-photon detection performance comparable to previous implementations, despite significantly reduced qubit coherence times and storage-cavity lifetimes. Using parity-based measurement sequences combined with a Hidden Markov Model (HMM) analysis, we achieve background rates on the order of $\mathcal{O}(20)$ Hz. In the absence of a magnetic field, we derive exclusion limits on the dark photon model for dark matter, reaching a sensitivity to the kinetic mixing angle of $ε_{95\%} \sim 1\times10^{-14}$ at 5.051 GHz. These results establish machine-learning robust control as a key enabler for faster, more scalable microwave quantum sensors for dark-matter searches. △ Less

Submitted 24 June, 2026; originally announced June 2026.

Comments: 17 pages, 14 figures, 4 tables

Coherent Control of an Embedded Bound State Without a Spectral Gap

Authors: Yue Chang

Abstract: Bound states in the continuum (BICs) can confine photonic excitations in open systems without conventional cavities or band gaps, making them natural candidates for long-lived quantum storage and single-photon control. Their use is limited, however, by two obstacles: they are dark to incident photons, and they lack spectral-gap protection from the surrounding continuum. We overcome both limitation… ▽ More Bound states in the continuum (BICs) can confine photonic excitations in open systems without conventional cavities or band gaps, making them natural candidates for long-lived quantum storage and single-photon control. Their use is limited, however, by two obstacles: they are dark to incident photons, and they lack spectral-gap protection from the surrounding continuum. We overcome both limitations in a giant atom coupled to a one-dimensional waveguide using two temporal control knobs. Atomic-frequency modulation breaks and restores the destructive-interference condition, enabling deterministic capture and release of mode-matched single photons. Coupling modulation instead preserves the BIC condition while tuning the atomic and photonic weights of the stored state. A key result is that this embedded state can nevertheless be controlled adiabatically despite the absence of a spectral gap, with an intrinsic leakage probability linear in the ramp rate. By separating radiative access from BIC-preserving deformation, the protocol turns a dark BIC into a single-photon memory whose fidelity is set by the intrinsic continuum-induced leakage law, providing a route to embedded-state control in open photonic platforms. △ Less

Submitted 16 June, 2026; originally announced June 2026.

Photon Anomalous Blockade in Waveguide Cavity QED with Atomic Mirrors

Authors: Yang Xue, Yue Chang, Tao Shi, Yu-xi Liu

Abstract: Waveguide cavity quantum electrodynamics (QED) with atomic mirrors is a growing research area of quantum optics and can be applied to quantum information processing. We here study the photon statistics of output fields from a waveguide cavity QED system, in which the waveguide is coupled to quantized mirror atoms and one driven medium atom. Our results show that the photon blockade can occur even… ▽ More Waveguide cavity quantum electrodynamics (QED) with atomic mirrors is a growing research area of quantum optics and can be applied to quantum information processing. We here study the photon statistics of output fields from a waveguide cavity QED system, in which the waveguide is coupled to quantized mirror atoms and one driven medium atom. Our results show that the photon blockade can occur even for a bad atom cavity with large dissipation and small coupling between the medium atom and the cavity, in contrast to the small dissipation and the strong coupling of the medium atom to the cavity field for the conventional photon blockade or the quantum interference for the unconventional photon blockade in the cavity QED system. Utilizing both the master equation and scattering theories, we derive the condition under which the photon blockade occurs in weakly driven systems. We find that such photon anomalous blockade is due to the quantum Zeno effect and is robust against variations of the medium atom's position within the cavity. Our study paves a way to exploit the photon blockade and single-photon devices via the waveguide cavity QED. △ Less

Submitted 20 May, 2026; originally announced May 2026.

Hidden Quantum Advantage near the Decoding Threshold of Decoded Quantum Interferometry

Authors: Maoxin Gao, Yan Chang

Abstract: Where is the true boundary of the quantum advantage region of decoded quantum interferometry (DQI)? The best existing answer is provided by Theorem 7.1 in the Supplementary Material of Jordan et al. (2025), yet we show that this answer systematically underestimates the extent of quantum advantage. On the standard partial-win LDPC benchmark instance, there exist 26 consecutive parameter points (… ▽ More Where is the true boundary of the quantum advantage region of decoded quantum interferometry (DQI)? The best existing answer is provided by Theorem 7.1 in the Supplementary Material of Jordan et al. (2025), yet we show that this answer systematically underestimates the extent of quantum advantage. On the standard partial-win LDPC benchmark instance, there exist 26 consecutive parameter points ($\ell \in [642, 667]$) at which Jordan's analysis declares no quantum advantage ($\langle s\rangle/m < 0.5$), while quantum advantage is in fact present with an approximation ratio reaching $0.66$. The root cause is that Jordan's bound penalizes the entire system with the worst-case Hamming-layer decoding failure rate $\varepsilon = \max_k \varepsilon_k$, discarding the spectral structure of the DQI tridiagonal matrix. Exploiting the concentration of the Perron eigenvector, we replace the uniform penalty with the eigenvector-weighted average $\bar\varepsilon = \sum_k \varepsilon_k w_k^2$ and establish a unified lower bound (Master Theorem) valid over arbitrary finite fields $\mathbb{F}_q$, proving that it strictly improves upon the relaxed form of Jordan's bound by replacing the operator-norm penalty $2\varepsilon(q-1)(m+1)$ with a tighter Rayleigh-quotient penalty $2\bar\varepsilonλ_{\max}$. △ Less

Submitted 28 April, 2026; v1 submitted 16 April, 2026; originally announced April 2026.

Comments: fixed theorem numbering (10.1 to 7.1) and revised Table I

MSC Class: 81P68; 94B05

A Digital Spreading Framework for Quantum Expectation Computation Without Rotation Gates or Arithmetic Circuits

Authors: Yu-Ting Kao, Yeong-Jar Chang

Abstract: In the pursuit of quantum advantage for financial engineering, researchers face a critical dilemma: analog rotation gates suffer from inherent 'sine-to-square' biases and error magnification, while digital arithmetic circuits (e.g., WeightedAdder) incur prohibitive quadratic complexity that exceeds NISQ capabilities. This study introduces Digital Spreading (DS), a fully digital quantum computing f… ▽ More In the pursuit of quantum advantage for financial engineering, researchers face a critical dilemma: analog rotation gates suffer from inherent 'sine-to-square' biases and error magnification, while digital arithmetic circuits (e.g., WeightedAdder) incur prohibitive quadratic complexity that exceeds NISQ capabilities. This study introduces Digital Spreading (DS), a fully digital quantum computing framework designed to resolve this trade-off. DS overcomes these limitations by utilizing a pruned Cuccaro ripple-carry architecture that avoids costly multiplication and eliminates rotation gates entirely. The proposed circuit employs integer comparison operations on superposed quantum states, mapping multi-qubit outcomes onto the probability of a single target qubit. Experiments based on a random walk model for option pricing demonstrate that DS achieves floating-point precision with a relative error as low as 0.0001%, outperforming JP Morgan's rotation-based method (1.43%), as well as ITRI's analog calibration (1.43%) and digital calibration approaches (19.14%). Overall, DS provides a compact, robust, and accurate framework for quantum weighted-average computation. △ Less

Submitted 7 April, 2026; originally announced April 2026.

Photonic Quantum-Enhanced Knowledge Distillation

Authors: Kuan-Cheng Chen, Shang Yu, Chen-Yu Liu, Samuel Yen-Chi Chen, Huan-Hsin Tseng, Yen Jui Chang, Wei-Hao Huang, Felix Burt, Esperanza Cuenca Gomez, Zohim Chandani, William Clements, Ian Walmsley, Kin K. Leung

Abstract: Photonic quantum processors naturally produce intrinsically stochastic measurement outcomes, offering a hardware-native source of structured randomness that can be exploited during machine-learning training. Here we introduce Photonic Quantum-Enhanced Knowledge Distillation (PQKD), a hybrid quantum photonic--classical framework in which a programmable photonic circuit generates a compact condition… ▽ More Photonic quantum processors naturally produce intrinsically stochastic measurement outcomes, offering a hardware-native source of structured randomness that can be exploited during machine-learning training. Here we introduce Photonic Quantum-Enhanced Knowledge Distillation (PQKD), a hybrid quantum photonic--classical framework in which a programmable photonic circuit generates a compact conditioning signal that constrains and guides a parameter-efficient student network during distillation from a high-capacity teacher. PQKD replaces fully trainable convolutional kernels with dictionary convolutions: each layer learns only a small set of shared spatial basis filters, while sample-dependent channel-mixing weights are derived from shot-limited photonic features and mapped through a fixed linear transform. Training alternates between standard gradient-based optimisation of the student and sampling-robust, gradient-free updates of photonic parameters, avoiding differentiation through photonic hardware. Across MNIST, Fashion-MNIST and CIFAR-10, PQKD traces a controllable compression--accuracy frontier, remaining close to teacher performance on simpler benchmarks under aggressive convolutional compression. Performance degrades predictably with finite sampling, consistent with shot-noise scaling, and exponential moving-average feature smoothing suppresses high-frequency shot-noise fluctuations, extending the practical operating regime at moderate shot budgets. △ Less

Submitted 16 March, 2026; originally announced March 2026.

Practical framework for simulating permutation-equivariant quantum circuits

Authors: Su Yeon Chang, Martin Larocca, M. Cerezo

Abstract: Understanding which subclasses of quantum circuits are efficiently classically simulable is fundamental to delineating the boundary between classical and quantum computation. In this context, it is well known that certain tasks based on permutation-equivariant unitaries-i.e., $n$-qubit circuits whose action commutes with the qubit-permuting representation of the symmetric group $S_n$-can be simula… ▽ More Understanding which subclasses of quantum circuits are efficiently classically simulable is fundamental to delineating the boundary between classical and quantum computation. In this context, it is well known that certain tasks based on permutation-equivariant unitaries-i.e., $n$-qubit circuits whose action commutes with the qubit-permuting representation of the symmetric group $S_n$-can be simulated in polynomial time. However, existing approaches scale as $O(n^7)$, and can rapidly become prohibitively expensive. In this work, we introduce a practical algorithm for simulating $S_n$-equivariant circuits under the assumption that the gate generators are at most $k$-local, with $k\in O(1)$. The resulting method runs in $O(n^{ω+1})$ time for constant depth, where $ω$ is the matrix multiplication exponent, significantly lowering the polynomial degree compared to existing techniques. Finally, we numerically validate this scaling by simulating the dynamical evolution of the Lipkin-Meshkov-Glick model, and show that for $n=512$ spins, a standard laptop can compute the concurrence of the evolved state in under two minutes. △ Less

Submitted 13 March, 2026; originally announced March 2026.

Comments: 13+14 pages, 6 figures, 1 table, 1 algorithm

Report number: LA-UR-26-21171

Digitizing ultrafast adiabatic passage with a pulse train

Authors: Bo Y. Chang, Ignacio R. Sola, Svetlana A. Malinovskaya, Sebastian C. Carrasco, Vladimir S. Malinovsky

Abstract: We present a digitized implementation of rapid adiabatic passage based on a train of weak, frequency-varying ultrafast pulses. Analytic conditions on the subpulse Rabi frequencies and detunings are derived to reproduce the continuous-time population dynamics of a conventional long-pulse excitation. We find that the reproduced dynamics achieves high fidelity even for pulse trains with a small numbe… ▽ More We present a digitized implementation of rapid adiabatic passage based on a train of weak, frequency-varying ultrafast pulses. Analytic conditions on the subpulse Rabi frequencies and detunings are derived to reproduce the continuous-time population dynamics of a conventional long-pulse excitation. We find that the reproduced dynamics achieves high fidelity even for pulse trains with a small number of subpulses, provided that each subpulse remains within the perturbative regime. The subpulses act as discrete samples of the underlying continuous evolution; consequently, more complex population dynamics, characterized by multiple oscillations prior to the onset of adiabaticity, require a larger number of subpulses for accurate reproduction. In addition, we demonstrate how the sidebands of a frequency comb can be exploited for resonant excitation at large carrier detuning and for the precise preparation of superposition states. △ Less

Submitted 13 February, 2026; originally announced February 2026.

Integrating AI and Quantum-Inspired Techniques for Efficient Enzyme Fermentation Optimization

Authors: Ying-Wei Tseng, Yu-Ting Kao, Yeong-Jar Chang, Jia-Han Ou, Wen-Zhi Zhang, Jin-Jia Wang, Yung-Hsiang Lin

Abstract: This paper introduces a new method that combines Artificial Intelligence (AI) and quantum-inspired techniques to improve the efficiency of multi-variable optimization experiments. By using advanced software simulations, this approach significantly reduces the time and cost compared to traditional physical experiments. The research focuses on enzyme fermentation, demonstrating that this method can… ▽ More This paper introduces a new method that combines Artificial Intelligence (AI) and quantum-inspired techniques to improve the efficiency of multi-variable optimization experiments. By using advanced software simulations, this approach significantly reduces the time and cost compared to traditional physical experiments. The research focuses on enzyme fermentation, demonstrating that this method can achieve better results with fewer experiments. The findings highlight the potential of this approach to more effectively identify optimal formulations, leading to advancements in enzyme fermentation and other fields that require complex optimization. Initially, the Active Ingredients (AIN) could not be improved even after 600 experiments. However, by adopting the method outlined in this paper, we were able to identify a better formula in just 405 experiments. This resulted in an increase of AIN from 8481 to 10068, representing an improvement of 18.7%. △ Less

Submitted 9 February, 2026; v1 submitted 6 February, 2026; originally announced February 2026.

Comments: 12 pages, 6 figures

Overcoming Stark-Shift Constraints in Phase-Controlled Rydberg Two-Qubit Gates

Authors: Ignacio R. Sola, Sebastian C. Carrasco, Vladimir S. Malinovsky, Seokmin Shin, Bo Y. Chang

Abstract: Stark shifts introduce additional phases that constrain the set of entangling gates that can be prepared via two-photon transitions in the strong Rydberg blockade limit. For non-independently addressed qubits, by controlling the absolute phases and the local amplitudes of the pulses at each qubit, we show that any two-qubit phase gate can be prepared with high fidelity using a three-pulse sequence… ▽ More Stark shifts introduce additional phases that constrain the set of entangling gates that can be prepared via two-photon transitions in the strong Rydberg blockade limit. For non-independently addressed qubits, by controlling the absolute phases and the local amplitudes of the pulses at each qubit, we show that any two-qubit phase gate can be prepared with high fidelity using a three-pulse sequence. Based on these insights, we introduce two robust control schemes tailored to different phase gates that yield better results with pulse sequences of either even or odd length. △ Less

Submitted 4 January, 2026; originally announced January 2026.

Comments: 6 figures

Leveraging Symmetry Merging in Pauli Propagation

Authors: Yanting Teng, Su Yeon Chang, Manuel S. Rudolph, Zoë Holmes

Abstract: We introduce a symmetry-adapted framework for simulating quantum dynamics based on Pauli propagation. When a quantum circuit possesses a symmetry, many Pauli strings evolve redundantly under actions of the symmetry group. We exploit this by merging Pauli strings related through symmetry transformations. This procedure, formalized as the symmetry-merging Pauli propagation algorithm, propagates only… ▽ More We introduce a symmetry-adapted framework for simulating quantum dynamics based on Pauli propagation. When a quantum circuit possesses a symmetry, many Pauli strings evolve redundantly under actions of the symmetry group. We exploit this by merging Pauli strings related through symmetry transformations. This procedure, formalized as the symmetry-merging Pauli propagation algorithm, propagates only a minimal set of orbit representatives. Analytically, we show that symmetry merging reduces space complexity by a factor set by orbit sizes, with explicit gains for translation and permutation symmetries. Numerical benchmarks of all-to-all Heisenberg dynamics confirm improved stability, particularly under truncation and noise. Our results establish a group-theoretic framework for enhancing Pauli propagation, supported by open-source code demonstrating its practical relevance for classical quantum-dynamics simulations. △ Less

Submitted 11 January, 2026; v1 submitted 12 December, 2025; originally announced December 2025.

Comments: 5 + 18 pages, 3 + 1 figures

Report number: LA-UR-25-31325

Continuously tunable single-photon level nonlinearity with Rydberg state wave-function engineering

Authors: Biao Xu, Gen-Sheng Ye, Yue Chang, Tao Shi, Lin Li

Abstract: Extending optical nonlinearity into the extremely weak light regime is at the heart of quantum optics, since it enables the efficient generation of photonic entanglement and implementation of photonic quantum logic gate. Here, we demonstrate the capability for continuously tunable single-photon level nonlinearity, enabled by precise control of Rydberg interaction over two orders of magnitude, thro… ▽ More Extending optical nonlinearity into the extremely weak light regime is at the heart of quantum optics, since it enables the efficient generation of photonic entanglement and implementation of photonic quantum logic gate. Here, we demonstrate the capability for continuously tunable single-photon level nonlinearity, enabled by precise control of Rydberg interaction over two orders of magnitude, through the use of microwave-assisted wave-function engineering. To characterize this nonlinearity, light storage and retrieval protocol utilizing Rydberg electromagnetically induced transparency is employed, and the quantum statistics of the retrieved photons are analyzed. As a first application, we demonstrate our protocol can speed up the preparation of single photons in low-lying Rydberg states by a factor of up to ~ 40. Our work holds the potential to accelerate quantum operations and to improve the circuit depth and connectivity in Rydberg systems, representing a crucial step towards scalable quantum information processing with Rydberg atoms. △ Less

Submitted 4 December, 2025; originally announced December 2025.

Journal ref: Rep. Prog. Phys. 87 110502 (2024)

Experimental signatures of a $\hat{Z}\hat{X}$ beam-splitter interaction between a Kerr-cat and transmon qubit

Authors: Josiah Cochran, Haley M. Cole, Hebah Goderya, Zhuoqun Hao, Yao-Chun Chang, Theo Shaw, Aikaterini Kargioti, Shyam Shankar

Abstract: Quantum error correction (QEC) requires ancilla qubits to extract error syndromes from data qubits which store quantum information. However, ancilla errors can propagate back to the data qubits, introducing additional errors and limiting fault-tolerance. In superconducting quantum circuits, Kerr-cat qubits (KCQs), which exhibit strongly biased noise, have been proposed as ancillas to suppress this… ▽ More Quantum error correction (QEC) requires ancilla qubits to extract error syndromes from data qubits which store quantum information. However, ancilla errors can propagate back to the data qubits, introducing additional errors and limiting fault-tolerance. In superconducting quantum circuits, Kerr-cat qubits (KCQs), which exhibit strongly biased noise, have been proposed as ancillas to suppress this back-action and enhance QEC performance. Here, we experimentally demonstrate a beamsplitter interaction between a KCQ and a transmon, realizing an effective $\hat{Z}_{cat}\hat{X}_q$ coupling that can be employed for parity measurements in QEC protocols. We characterize the interaction across a range of cat sizes and drive amplitudes, confirming the expected scaling of the interaction rate. These results establish a step towards hybrid architectures that combine transmons as data qubits with noise-biased bosonic ancillas, enabling hardware-efficient syndrome extraction and advancing the development of fault-tolerant quantum processors. △ Less

Submitted 13 May, 2026; v1 submitted 26 November, 2025; originally announced November 2025.

A Primer on Quantum Machine Learning

Authors: Su Yeon Chang, M. Cerezo

Abstract: Quantum machine learning (QML) is a computational paradigm that seeks to apply quantum-mechanical resources to solve learning problems. As such, the goal of this framework is to leverage quantum processors to tackle optimization, supervised, unsupervised and reinforcement learning, and generative modeling-among other tasks-more efficiently than classical models. Here we offer a high level overview… ▽ More Quantum machine learning (QML) is a computational paradigm that seeks to apply quantum-mechanical resources to solve learning problems. As such, the goal of this framework is to leverage quantum processors to tackle optimization, supervised, unsupervised and reinforcement learning, and generative modeling-among other tasks-more efficiently than classical models. Here we offer a high level overview of QML, focusing on settings where the quantum device is the primary learning or data generating unit. We outline the field's tensions between practicality and guarantees, access models and speedups, and classical baselines and claimed quantum advantages-flagging where evidence is strong, where it is conditional or still lacking, and where open questions remain. By shedding light on these nuances and debates, we aim to provide a friendly map of the QML landscape so that the reader can judge when-and under what assumptions-quantum approaches may offer real benefits. △ Less

Submitted 19 November, 2025; originally announced November 2025.

Comments: 29+16 pages, 5 figures, 15 boxes. Chapter for Comprehensive Quantum Physics. Comments welcomed!

Report number: LA-UR-25-3138

Heat measurement of quantum interference

Authors: Christoforus Dimas Satrya, Aleksandr S. Strelnikov, Luca Magazzù, Yu-Cheng Chang, Rishabh Upadhyay, Joonas T. Peltonen, Bayan Karimi, Jukka P. Pekola

Abstract: Coherence is a key property of quantum systems, and it plays a central role in the operation and performance of quantum heat engines and refrigerators. Despite its importance for the fundamental understanding in quantum thermodynamics and its technological implications, coherence effects in heat transport have not been observed previously. Here, we measure quantum features in the heat transfer bet… ▽ More Coherence is a key property of quantum systems, and it plays a central role in the operation and performance of quantum heat engines and refrigerators. Despite its importance for the fundamental understanding in quantum thermodynamics and its technological implications, coherence effects in heat transport have not been observed previously. Here, we measure quantum features in the heat transfer between a qubit and a thermal bath. The system is formed of a driven flux qubit galvanically coupled to a $λ/4$ coplanar-waveguide resonator that is coupled to a heat reservoir. This thermal bath is a normal-metal mesoscopic resistor, whose temperature can be measured and controlled. We detect interference patterns in the heat current due to driving-induced coherence. In particular, resonance peaks in the heat transferred to the bath are found at driving frequencies which are integer fractions of the resonator frequency. A selection rule on the even/odd parity of the peaks holds at the qubit symmetry point. We present a theoretical model based on Floquet theory that captures the experimental results. The studied system provides a platform for studying the role of coherence in quantum thermodynamics. Our work opens the possibility to demonstrate a true quantum thermal machine where heat is measured directly. △ Less

Submitted 8 December, 2025; v1 submitted 27 October, 2025; originally announced October 2025.

Comments: 33 pages and 11 figures

Femtojoule-per-operation photonic computer for the subset sum problem

Authors: Tian-Yu Zhang, Xiao-Yun Xu, Wen-Hao Zhou, Xiao-Wei Wang, Chu-Han Wang, Yi-Jun Chang, Ying-Yue Yang, Jie Ma, Ka-Di Zhu, Xian-Min Jin

Abstract: Energy-efficient computing is becoming increasingly important in the information era. However, electronic computers with von Neumann architecture can hardly meet the challenge due to the inevitable energy-intensive data movement, especially when tackling computationally hard problems or complicated tasks. Here, we experimentally demonstrate an energy-efficient photonic computer that solves intract… ▽ More Energy-efficient computing is becoming increasingly important in the information era. However, electronic computers with von Neumann architecture can hardly meet the challenge due to the inevitable energy-intensive data movement, especially when tackling computationally hard problems or complicated tasks. Here, we experimentally demonstrate an energy-efficient photonic computer that solves intractable subset sum problem (SSP) by making use of the extremely low energy level of photons (~10^(-19) J) and a time-of-flight storage technique. We show that the energy consumption of the photonic computer maintains no larger than 10^(-15) J per operation at a reasonably large problem size N=33, and it consumes 10^(8) times less energy than the most energy-efficient supercomputer for a medium-scale problem. In addition, when the photonic computer is applied to deal with real-life problems that involves iterative computation of the SSP, the photonic advantage in energy consumption is further enhanced and massive energy can be saved. Our results indicate the superior competitiveness of the photonic computer in the energy costs of complex computation, opening a possible path to green computing. △ Less

Submitted 24 August, 2025; originally announced August 2025.

Wireless Josephson parametric amplifier above 20 GHz

Authors: Z. Hao, J. Cochran, Y. -C. Chang, H. M. Cole, S. Shankar

Abstract: Operating superconducting qubits at elevated temperatures offers increased cooling power and thus system scalability, but requires suppression of thermal photons to preserve coherence and readout fidelity. This motivates migration to higher operation frequencies, which demands high-frequency amplification with near-quantum-limited noise characteristics for qubit readout. Here, we report the design… ▽ More Operating superconducting qubits at elevated temperatures offers increased cooling power and thus system scalability, but requires suppression of thermal photons to preserve coherence and readout fidelity. This motivates migration to higher operation frequencies, which demands high-frequency amplification with near-quantum-limited noise characteristics for qubit readout. Here, we report the design and experimental realization of a wireless Josephson parametric amplifier (WJPA) operating above 20~GHz. The wireless design eliminates losses and impedance mismatches that become problematic at high frequencies. The WJPA achieves more than 20~dB of gain across a tunable frequency range of 21--23.5~GHz, with a typical dynamic bandwidth of 3~MHz. Through Y-factor measurements and a qubit-based photon number calibration, we show that the amplifier exhibits an added noise of approximately two photons. △ Less

Submitted 21 January, 2026; v1 submitted 14 August, 2025; originally announced August 2025.

Comments: main text: 5 pages, 3 figures. supplementary: 3 pages, 3 figures. final version - corrected typo in the supplementary

Journal ref: Appl. Phys. Lett. 128, 014004 (2026)

Probing strongly driven and strongly coupled superconducting qubit-resonator system

Authors: Oleh V. Ivakhnenko, Christoforus Dimas Satrya, Yu-Cheng Chang, Rishabh Upadhyay, Joonas T. Peltonen, Sergey N. Shevchenko, Franco Nori, Jukka P. Pekola

Abstract: We investigated a strongly driven qubit strongly connected to a quantum resonator. The measured system was a superconducting flux qubit coupled to a coplanar-waveguide resonator which is weakly coupled to a probing feedline. This hybrid qubit-resonator system was driven by a magnetic flux and probed with a weak probe signal through the feedline. We observed and theoretically described the quantum… ▽ More We investigated a strongly driven qubit strongly connected to a quantum resonator. The measured system was a superconducting flux qubit coupled to a coplanar-waveguide resonator which is weakly coupled to a probing feedline. This hybrid qubit-resonator system was driven by a magnetic flux and probed with a weak probe signal through the feedline. We observed and theoretically described the quantum interference effects, deviating from the usual single-qubit Landau-Zener-Stückelberg-Majorana interferometry, because the strong coupling distorts the qubit energy levels. △ Less

Submitted 5 August, 2025; originally announced August 2025.

Comments: 12 pages, 9 figures

Novel Quantum Circuit Designs of Random Injection and Payoff Computation for Financial Risk Assessment

Authors: Yu-Ting Kao, Yeong-Jar Chang, Ying-Wei Tseng

Abstract: Quantum entanglement enables exponential computational states, while superposition provides inherent parallelism. Consequently, quantum circuits are theoretically capable of supporting large scale parallel computation. However, applying them to financial analysis particularly in the areas of random number generation and payoff computation remains a significant challenge. Experts generally believe… ▽ More Quantum entanglement enables exponential computational states, while superposition provides inherent parallelism. Consequently, quantum circuits are theoretically capable of supporting large scale parallel computation. However, applying them to financial analysis particularly in the areas of random number generation and payoff computation remains a significant challenge. Experts generally believe that quantum computing relies on matrix operations, which are deterministic in nature without randomness. This inherent determinism makes it particularly challenging to design quantum circuits that require random number injection. JP Morgan[1] introduced the piecewise linear (PWL) approach for modeling payoff computations but did not disclose a quantum circuit capable of identifying values exceeding the strike price, suggesting a possible reliance on classical pre processing for interval classification. This paper presents an integrated quantum circuit with two key components: one for random number injection, applicable to risk assessment, and the other for direct payoff computation, relevant to financial pricing. These components are compatible with a scalable framework that leverages large scale parallelism and Quantum Amplitude Estimation (QAE) to achieve quadratic speedup. The circuit was implemented on IBM Qiskit and evaluated using 8 parallel threads and 1600 measurement shots. Results confirmed both the presence of randomness and the correctness of payoff computation. While the current implementation uses 8 threads, the design scales to 2 to the power of n threads, for arbitrarily large n, offering a potential path toward demonstrating quantum supremacy. △ Less

Submitted 31 July, 2025; originally announced July 2025.

Spatial Regionalization: A Hybrid Quantum Computing Approach

Authors: Yunhan Chang, Amr Magdy, Federico M. Spedalieri, Ibrahim Sabek

Abstract: Quantum computing has shown significant potential to address complex optimization problems; however, its application remains confined to specific problems at limited scales. Spatial regionalization remains largely unexplored in quantum computing due to its complexity and large number of variables. In this paper, we introduce the first hybrid quantum-classical method to spatial regionalization by d… ▽ More Quantum computing has shown significant potential to address complex optimization problems; however, its application remains confined to specific problems at limited scales. Spatial regionalization remains largely unexplored in quantum computing due to its complexity and large number of variables. In this paper, we introduce the first hybrid quantum-classical method to spatial regionalization by decomposing the problem into manageable subproblems, leveraging the strengths of both classical and quantum computation. This study establishes a foundational framework for effectively integrating quantum computing methods into realistic and complex spatial optimization tasks. Our initial results show a promising quantum performance advantage for a broad range of spatial regionalization problems and their variants. △ Less

Submitted 23 June, 2025; originally announced June 2025.

Mixed-Signal Quantum Circuit Design for Option Pricing Using Design Compiler

Authors: Yu-Ting Kao, Yeong-Jar Chang, Ying-Wei Tseng

Abstract: Prior studies have largely focused on quantum algorithms, often reducing parallel computing designs to abstract models or overly simplified circuits. This has contributed to the misconception that most applications are feasible only through VLSI circuits and cannot be implemented using quantum circuits. To challenge this view, we present a mixed-signal quantum circuit framework incorporating three… ▽ More Prior studies have largely focused on quantum algorithms, often reducing parallel computing designs to abstract models or overly simplified circuits. This has contributed to the misconception that most applications are feasible only through VLSI circuits and cannot be implemented using quantum circuits. To challenge this view, we present a mixed-signal quantum circuit framework incorporating three novel methods that reduce circuit complexity and improve noise tolerance. In a 12 qubit case study comparing our design with JP Morgan's option pricing circuit, we reduced the gate count from 4095 to 392, depth from 2048 to 6, and error rate from 25.86\% to 1.64\%. Our design combines analog simplicity with digital flexibility and synthesizability, demonstrating that quantum circuits can effectively leverage classical VLSI techniques, such as those enabled by Synopsys Design Compiler to address current quantum design limitations. △ Less

Submitted 18 June, 2025; originally announced June 2025.

Differentiable Quantum Architecture Search in Quantum-Enhanced Neural Network Parameter Generation

Authors: Samuel Yen-Chi Chen, Chen-Yu Liu, Kuan-Cheng Chen, Wei-Jia Huang, Yen-Jui Chang, Wei-Hao Huang

Abstract: The rapid advancements in quantum computing (QC) and machine learning (ML) have led to the emergence of quantum machine learning (QML), which integrates the strengths of both fields. Among QML approaches, variational quantum circuits (VQCs), also known as quantum neural networks (QNNs), have shown promise both empirically and theoretically. However, their broader adoption is hindered by reliance o… ▽ More The rapid advancements in quantum computing (QC) and machine learning (ML) have led to the emergence of quantum machine learning (QML), which integrates the strengths of both fields. Among QML approaches, variational quantum circuits (VQCs), also known as quantum neural networks (QNNs), have shown promise both empirically and theoretically. However, their broader adoption is hindered by reliance on quantum hardware during inference. Hardware imperfections and limited access to quantum devices pose practical challenges. To address this, the Quantum-Train (QT) framework leverages the exponential scaling of quantum amplitudes to generate classical neural network parameters, enabling inference without quantum hardware and achieving significant parameter compression. Yet, designing effective quantum circuit architectures for such quantum-enhanced neural programmers remains non-trivial and often requires expertise in quantum information science. In this paper, we propose an automated solution using differentiable optimization. Our method jointly optimizes both conventional circuit parameters and architectural parameters in an end-to-end manner via automatic differentiation. We evaluate the proposed framework on classification, time-series prediction, and reinforcement learning tasks. Simulation results show that our method matches or outperforms manually designed QNN architectures. This work offers a scalable and automated pathway for designing QNNs that can generate classical neural network parameters across diverse applications. △ Less

Submitted 13 May, 2025; originally announced May 2025.

Quantum-Enhanced Parameter-Efficient Learning for Typhoon Trajectory Forecasting

Authors: Chen-Yu Liu, Kuan-Cheng Chen, Yi-Chien Chen, Samuel Yen-Chi Chen, Wei-Hao Huang, Wei-Jia Huang, Yen-Jui Chang

Abstract: Typhoon trajectory forecasting is essential for disaster preparedness but remains computationally demanding due to the complexity of atmospheric dynamics and the resource requirements of deep learning models. Quantum-Train (QT), a hybrid quantum-classical framework that leverages quantum neural networks (QNNs) to generate trainable parameters exclusively during training, eliminating the need for q… ▽ More Typhoon trajectory forecasting is essential for disaster preparedness but remains computationally demanding due to the complexity of atmospheric dynamics and the resource requirements of deep learning models. Quantum-Train (QT), a hybrid quantum-classical framework that leverages quantum neural networks (QNNs) to generate trainable parameters exclusively during training, eliminating the need for quantum hardware at inference time. Building on QT's success across multiple domains, including image classification, reinforcement learning, flood prediction, and large language model (LLM) fine-tuning, we introduce Quantum Parameter Adaptation (QPA) for efficient typhoon forecasting model learning. Integrated with an Attention-based Multi-ConvGRU model, QPA enables parameter-efficient training while maintaining predictive accuracy. This work represents the first application of quantum machine learning (QML) to large-scale typhoon trajectory prediction, offering a scalable and energy-efficient approach to climate modeling. Our results demonstrate that QPA significantly reduces the number of trainable parameters while preserving performance, making high-performance forecasting more accessible and sustainable through hybrid quantum-classical learning. △ Less

Submitted 14 May, 2025; originally announced May 2025.

Quantum Process Tomography with Digital Twins of Error Matrices

Authors: Tangyou Huang, Akshay Gaikwad, Ilya Moskalenko, Anuj Aggarwal, Tahereh Abad, Marko Kuzmanovic, Yu-Han Chang, Ognjen Stanisavljevic, Emil Hogedal, Christopher Warren, Irshad Ahmad, Janka Biznárová, Amr Osman, Mamta Dahiya, Marcus Rommel, Anita Fadavi Rousari, Andreas Nylander, Liangyu Chen, Jonas Bylander, Gheorghe Sorin Paraoanu, Anton Frisk Kockum, Giovanna Tancredi

Abstract: Accurate and robust quantum process tomography (QPT) is crucial for verifying quantum gates and diagnosing implementation faults in experiments aimed at building universal quantum computers. However, the reliability of QPT protocols is often compromised by faulty probes, particularly state preparation and measurement (SPAM) errors, which introduce fundamental inconsistencies in traditional QPT alg… ▽ More Accurate and robust quantum process tomography (QPT) is crucial for verifying quantum gates and diagnosing implementation faults in experiments aimed at building universal quantum computers. However, the reliability of QPT protocols is often compromised by faulty probes, particularly state preparation and measurement (SPAM) errors, which introduce fundamental inconsistencies in traditional QPT algorithms. We propose and investigate enhanced QPT for multi-qubit systems by integrating the error matrix in a digital twin of the identity process matrix, enabling statistical refinement of SPAM error learning and improving QPT precision. Through numerical simulations, we demonstrate that our approach enables highly accurate and faithful process characterization. We further validate our method experimentally using superconducting quantum gates, achieving at least an order-of-magnitude fidelity improvement over standard QPT. Our results provide a practical and precise method for assessing quantum gate fidelity and enhancing QPT on a given hardware. △ Less

Submitted 1 December, 2025; v1 submitted 12 May, 2025; originally announced May 2025.

Journal ref: Phys. Rev. Lett. 135, 230601(2025)

Direct space-time modeling of mechanically dressed dipole-dipole interactions with electromagnetically-coupled oscillating dipoles

Authors: Yi-Ming Chang, Kamran Akbari, Matthew Filipovich, Stephen Hughes

Abstract: We study the radiative dynamics of coupled electric dipoles, modelled as Lorentz oscillators (LOs), in the presence of real-time mechanical oscillations. The dipoles are treated in a self-consistent way through a direct electromagnetic simulation approach that fully includes the dynamical movement of the charges, accounting for radiation reaction, emission and absorption. This allows for a powerfu… ▽ More We study the radiative dynamics of coupled electric dipoles, modelled as Lorentz oscillators (LOs), in the presence of real-time mechanical oscillations. The dipoles are treated in a self-consistent way through a direct electromagnetic simulation approach that fully includes the dynamical movement of the charges, accounting for radiation reaction, emission and absorption. This allows for a powerful numerical solution of optomechanical resonances without any perturbative approximations for the mechanical motion. The scaled population (excitation) dynamics of the LOs are investigated as well as the emitted radiation and electromagnetic spectra, which demonstrates how the usual dipole-dipole resonances couple to the underlying Floquet states, yielding multiple spectral peaks that are separated from the superradiant and subradiant states by an integer number of the mechanical oscillation frequency. Moreover, we observe that when the mechanical amplitude and frequency are sufficiently large, these additional spectral peaks undergo further modification, including spectral splitting, spectral squeezing, or shifting. These observations are fully corroborated by a theoretical Floquet analysis conducted on two coupled harmonic oscillators. △ Less

Submitted 10 May, 2025; originally announced May 2025.

Comments: 18 pages, 5 figures

Journal ref: Phys. Rev. A 112, 033530 (2025)

Highly squeezed nanophotonic quantum microcombs with broadband frequency tunability

Authors: Yichen Shen, Ping-Yen Hsieh, Dhruv Srinivasan, Antoine Henry, Gregory Moille, Sashank Kaushik Sridhar, Alessandro Restelli, You-Chia Chang, Kartik Srinivasan, Thomas A. Smith, Avik Dutt

Abstract: Squeezed light offers genuine quantum advantage in enhanced sensing and quantum computation; yet the level of squeezing or quantum noise reduction generated from nanophotonic chips has been limited. In addition to strong quantum noise reduction, key desiderata for such a nanophotonic squeezer include frequency agility or tunability over a broad frequency range, and simultaneous operation in many d… ▽ More Squeezed light offers genuine quantum advantage in enhanced sensing and quantum computation; yet the level of squeezing or quantum noise reduction generated from nanophotonic chips has been limited. In addition to strong quantum noise reduction, key desiderata for such a nanophotonic squeezer include frequency agility or tunability over a broad frequency range, and simultaneous operation in many distinct, well-defined quantum modes (qumodes). Here we present a strongly overcoupled silicon nitride squeezer based on a below-threshold optical parametric amplifier (OPA) that produces directly detected squeezing of 5.6 dB $\pm$ 0.2 dB, surpassing previous demonstrations in both continuous-wave and pulsed regimes. We introduce a seed-assisted detection technique into such nanophotonic squeezers that reveals a quantum frequency comb (QFC) of 16 qumodes, with a separation of 11~THz between the furthest qumode pair, while maintaining a strong squeezing. Additionally, we report spectral tuning of a qumode comb pair over one free-spectral range of the OPA, thus bridging the spacing between the discrete modes of the QFC. Our results significantly advance both the generation and detection of nanophotonic squeezed light in a broadband and multimode platform, establishing a scalable, chip-integrated path for compact quantum sensors and continuous-variable quantum information processing systems. △ Less

Submitted 6 May, 2025; originally announced May 2025.

Neural-network-based design and implementation of fast and robust quantum gates

Authors: Marko Kuzmanović, Ilya Moskalenko, Yu-Han Chang, Ognjen Stanisavljević, Christopher Warren, Emil Hogedal, Anuj Aggarwal, Irshad Ahmad, Janka Biznárová, Mamta Dahiya, Marcus Rommel, Andreas Nylander, Giovanna Tancredi, Gheorghe Sorin Paraoanu

Abstract: We present a continuous-time, neural-network-based approach to optimal control in quantum systems, with a focus on pulse engineering for quantum gates. Leveraging the framework of neural ordinary differential equations, we construct control fields as outputs of trainable neural networks, thereby eliminating the need for discrete parametrization or predefined bases. This allows for generation of sm… ▽ More We present a continuous-time, neural-network-based approach to optimal control in quantum systems, with a focus on pulse engineering for quantum gates. Leveraging the framework of neural ordinary differential equations, we construct control fields as outputs of trainable neural networks, thereby eliminating the need for discrete parametrization or predefined bases. This allows for generation of smooth, hardware-agnostic pulses that can be optimized directly using differentiable integrators. As a case study we design, and implement experimentally, a short and detuning-robust $π/2$ pulse for photon parity measurements in superconducting transmon circuits. This is achieved through simultaneous optimization for robustness and suppressing the leakage outside of the computational basis. These pulses maintain a fidelity greater than $99.9\%$ over a detuning range of $\approx \pm 20\mathrm{MHz}$, thereby outperforming traditional techniques while retaining comparable gate durations. This showcases its potential for high-performance quantum control in experimentally relevant settings. △ Less

Submitted 4 May, 2025; originally announced May 2025.

Efficient hybrid variational quantum algorithm for solving graph coloring problem

Authors: Dongmei Liu, Jian Li, Xiubo Cheng, Shibing Zhang, Yan Chang, Lili Yan

Abstract: In the era of Noisy Intermediate Scale Quantum (NISQ) computing, available quantum resources are limited. Many NP-hard problems can be efficiently addressed using hybrid classical and quantum computational methods. This paper proposes a hybrid variational quantum algorithm designed to solve the $k$-coloring problem of graph vertices. The hybrid classical and quantum algorithms primarily partition… ▽ More In the era of Noisy Intermediate Scale Quantum (NISQ) computing, available quantum resources are limited. Many NP-hard problems can be efficiently addressed using hybrid classical and quantum computational methods. This paper proposes a hybrid variational quantum algorithm designed to solve the $k$-coloring problem of graph vertices. The hybrid classical and quantum algorithms primarily partition the graph into multiple subgraphs through hierarchical techniques. The Quantum Approximate Optimization Algorithm (QAOA) is employed to determine the coloring within the subgraphs, while a classical greedy algorithm is utilized to find the coloring of the interaction graph. Fixed coloring is applied to the interaction graph, and feedback is provided to correct any conflicting colorings within the subgraphs. The merging process into the original graph is iteratively optimized to resolve any arising conflicts. We employ a hierarchical framework that integrates feedback correction and conflict resolution to achieve $k$-coloring of arbitrary graph vertices. Through experimental analysis, we demonstrate the effectiveness of the algorithm, highlighting the rapid convergence of conflict evolution and the fact that iterative optimization allows the classical algorithm to approximate the number of colorings. Finally, we apply the proposed algorithm to optimize the scheduling of a subway transportation network, demonstrating a high degree of fairness. △ Less

Submitted 30 April, 2025; originally announced April 2025.

Comments: 12 pages,8 figures,1 table

Quantum Walks-Based Adaptive Distribution Generation with Efficient CUDA-Q Acceleration

Authors: Yen-Jui Chang, Wei-Ting Wang, Chen-Yu Liu, Yun-Yuan Wang, Ching-Ray Chang

Abstract: We present a novel Adaptive Distribution Generator that leverages a quantum walks-based approach to generate high precision and efficiency of target probability distributions. Our method integrates variational quantum circuits with discrete-time quantum walks, specifically, split-step quantum walks and their entangled extensions, to dynamically tune coin parameters and drive the evolution of quant… ▽ More We present a novel Adaptive Distribution Generator that leverages a quantum walks-based approach to generate high precision and efficiency of target probability distributions. Our method integrates variational quantum circuits with discrete-time quantum walks, specifically, split-step quantum walks and their entangled extensions, to dynamically tune coin parameters and drive the evolution of quantum states towards desired distributions. This enables accurate one-dimensional probability modeling for applications such as financial simulation and structured two-dimensional pattern generation exemplified by digit representations(0~9). Implemented within the CUDA-Q framework, our approach exploits GPU acceleration to significantly reduce computational overhead and improve scalability relative to conventional methods. Extensive benchmarks demonstrate that our Quantum Walks-Based Adaptive Distribution Generator achieves high simulation fidelity and bridges the gap between theoretical quantum algorithms and practical high-performance computation. △ Less

Submitted 18 April, 2025; originally announced April 2025.

Comments: 17 pages, 5 figures

Journal ref: Quantum Machine Intelligence 8, 42 (2026)

Federated Quantum-Train Long Short-Term Memory for Gravitational Wave Signal

Authors: Chen-Yu Liu, Samuel Yen-Chi Chen, Kuan-Cheng Chen, Wei-Jia Huang, Yen-Jui Chang

Abstract: We present Federated QT-LSTM, a novel framework that combines the Quantum-Train (QT) methodology with Long Short-Term Memory (LSTM) networks in a federated learning setup. By leveraging quantum neural networks (QNNs) to generate classical LSTM model parameters during training, the framework effectively addresses challenges in model compression, scalability, and computational efficiency. Importantl… ▽ More We present Federated QT-LSTM, a novel framework that combines the Quantum-Train (QT) methodology with Long Short-Term Memory (LSTM) networks in a federated learning setup. By leveraging quantum neural networks (QNNs) to generate classical LSTM model parameters during training, the framework effectively addresses challenges in model compression, scalability, and computational efficiency. Importantly, Federated QT-LSTM eliminates the reliance on quantum devices during inference, making it practical for real-world applications. Experiments on simulated gravitational wave (GW) signal datasets demonstrate the framework's superior performance compared to baseline models, including LSTM and QLSTM, achieving lower training and testing losses while significantly reducing the number of trainable parameters. The results also reveal that deeper QT layers enhance model expressiveness for complex tasks, highlighting the adaptability of the framework. Federated QT-LSTM provides a scalable and efficient solution for privacy-preserving distributed learning, showcasing the potential of quantum-inspired techniques in advancing time-series prediction and signal reconstruction tasks. △ Less

Submitted 20 March, 2025; originally announced March 2025.

Comments: Accepted at IEEE INFOCOM 2025 QuNAP Workshop

Automatic Characterization of Fluxonium Superconducting Qubits Parameters with Deep Transfer Learning

Authors: Huan-Hsuan Kung, Chen-Yu Liu, Qian-Rui Lee, Chiang-Yuan Hu, Yu-Chi Chang, Ching-Yeh Chen, Daw-Wei Wang, Yen-Hsiang Lin

Abstract: Accurate determination of qubit parameters is critical for the successful implementation of quantum information and computation applications. In solid state systems, the parameters of individual qubits vary across the entire system, requiring time consuming measurements and manual fitting processes for characterization. Recent developed superconducting qubits, such as fluxonium or 0-pi qubits, off… ▽ More Accurate determination of qubit parameters is critical for the successful implementation of quantum information and computation applications. In solid state systems, the parameters of individual qubits vary across the entire system, requiring time consuming measurements and manual fitting processes for characterization. Recent developed superconducting qubits, such as fluxonium or 0-pi qubits, offer improved fidelity operations but exhibit a more complex physical and spectral structure, complicating parameter extraction. In this work, we propose a machine learning (ML)based methodology for the automatic and accurate characterization of fluxonium qubit parameters. Our approach utilized the energy spectrum calculated by a model Hamiltonian with various magnetic fields, as training data for the ML model. The output consists of the essential fluxonium qubit energy parameters, EJ, EC, and EL in Hamiltonian. The ML model achieves remarkable accuracy (with an average accuracy 95.6%) as an initial guess, enabling the development of an automatic fitting procedure for direct application to realistic experimental data. Moreover, we demonstrate that similar accuracy can be retrieved even when the input experimental spectrum is noisy or incomplete, highlighting the model robustness. These results suggest that our automated characterization method, based on a transfer learning approach, provides a reliable framework for future extensions to other superconducting qubits or different solid-state systems. Ultimately, we believe this methodology paves the way for the construction of large-scale quantum processors. △ Less

Submitted 15 March, 2025; originally announced March 2025.

Comments: 11 pages, 7 figures

Quantum-Chiplet: A Novel Python-Based Efficient and Scalable Design Methodology for Quantum Circuit Verification and Implementation

Authors: Yu-Ting Kao, Hao-Yu Lu, Yeong-Jar Chang, Darsen Lu

Abstract: Analysis and verification of quantum circuits are highly challenging, given the exponential dependence of the number of states on the number of qubits. For analytical derivation, we propose a new quantum polynomial representation (QPR) to facilitate the analysis of massively parallel quantum computation and detect subtle errors. For the verification of quantum circuits, we introduce Quantum-Chiple… ▽ More Analysis and verification of quantum circuits are highly challenging, given the exponential dependence of the number of states on the number of qubits. For analytical derivation, we propose a new quantum polynomial representation (QPR) to facilitate the analysis of massively parallel quantum computation and detect subtle errors. For the verification of quantum circuits, we introduce Quantum-Chiplet, a hierarchical quantum behavior modeling methodology that facilitates rapid integration and simulation. Each chiplet is systematically transformed into quantum gates. For circuits involving n qubits and k quantum gates, the design complexity is reduced from "greater than O(2^n)" to O(k). This approach provides an open-source solution, enabling a highly customized solution for quantum circuit simulation within the native Python environment, thereby reducing reliance on traditional simulation packages. A quantum amplitude estimation example demonstrates that this method significantly improves the design process, with more than 10x speed-up compared to IBM Qiskit at 14 qubits. △ Less

Submitted 13 March, 2025; originally announced March 2025.

Programming Variational Quantum Circuits with Quantum-Train Agent

Authors: Chen-Yu Liu, Samuel Yen-Chi Chen, Kuan-Cheng Chen, Wei-Jia Huang, Yen-Jui Chang

Abstract: In this study, the Quantum-Train Quantum Fast Weight Programmer (QT-QFWP) framework is proposed, which facilitates the efficient and scalable programming of variational quantum circuits (VQCs) by leveraging quantum-driven parameter updates for the classical slow programmer that controls the fast programmer VQC model. This approach offers a significant advantage over conventional hybrid quantum-cla… ▽ More In this study, the Quantum-Train Quantum Fast Weight Programmer (QT-QFWP) framework is proposed, which facilitates the efficient and scalable programming of variational quantum circuits (VQCs) by leveraging quantum-driven parameter updates for the classical slow programmer that controls the fast programmer VQC model. This approach offers a significant advantage over conventional hybrid quantum-classical models by optimizing both quantum and classical parameter management. The framework has been benchmarked across several time-series prediction tasks, including Damped Simple Harmonic Motion (SHM), NARMA5, and Simulated Gravitational Waves (GW), demonstrating its ability to reduce parameters by roughly 70-90\% compared to Quantum Long Short-term Memory (QLSTM) and Quantum Fast Weight Programmer (QFWP) without compromising accuracy. The results show that QT-QFWP outperforms related models in both efficiency and predictive accuracy, providing a pathway toward more practical and cost-effective quantum machine learning applications. This innovation is particularly promising for near-term quantum systems, where limited qubit resources and gate fidelities pose significant constraints on model complexity. QT-QFWP enhances the feasibility of deploying VQCs in time-sensitive applications and broadens the scope of quantum computing in machine learning domains. △ Less

Submitted 2 December, 2024; originally announced December 2024.

Comments: 9 pages, 7 figures

Efficient Bitcoin Address Classification Using Quantum-Inspired Feature Selection

Authors: Ming-Fong Sie, Yen-Jui Chang, Chien-Lung Lin, Ching-Ray Chang, Shih-Wei Liao

Abstract: Over 900 million Bitcoin transactions have been recorded, posing considerable challenges for machine learning in terms of computation time and maintaining prediction accuracy. We propose an innovative approach using quantum-inspired algorithms implemented with Simulated Annealing and Quantum Annealing to address the challenge of local minima in solution spaces. This method efficiently identifies k… ▽ More Over 900 million Bitcoin transactions have been recorded, posing considerable challenges for machine learning in terms of computation time and maintaining prediction accuracy. We propose an innovative approach using quantum-inspired algorithms implemented with Simulated Annealing and Quantum Annealing to address the challenge of local minima in solution spaces. This method efficiently identifies key features linked to mixer addresses, significantly reducing model training time. By categorizing Bitcoin addresses into six classes: exchanges, faucets, gambling, marketplaces, mixers, and mining pools, and applying supervised learning methods, our results demonstrate that feature selection with SA reduced training time by 30.3% compared to using all features in a random forest model while maintaining a 91% F1-score for mixer addresses. This highlights the potential of quantum-inspired algorithms to swiftly and accurately identify high-risk Bitcoin addresses based on transaction features. △ Less

Submitted 22 November, 2024; originally announced November 2024.

Comments: 19 pages

Strong nanophotonic quantum squeezing exceeding 3.5 dB in a foundry-compatible Kerr microresonator

Authors: Yichen Shen, Ping-Yen Hsieh, Sashank Kaushik Sridhar, Samantha Feldman, You-Chia Chang, Thomas A. Smith, Avik Dutt

Abstract: Squeezed light, with its quantum noise reduction capabilities, has emerged as a powerful resource in quantum information processing and precision metrology. To reach noise reduction levels such that a quantum advantage is achieved, off-chip squeezers are typically used. The development of on-chip squeezed light sources, particularly in nanophotonic platforms, has been challenging. We report 3.7… ▽ More Squeezed light, with its quantum noise reduction capabilities, has emerged as a powerful resource in quantum information processing and precision metrology. To reach noise reduction levels such that a quantum advantage is achieved, off-chip squeezers are typically used. The development of on-chip squeezed light sources, particularly in nanophotonic platforms, has been challenging. We report 3.7 $\pm$ 0.2 dB of directly detected nanophotonic quantum squeezing using foundry-fabricated silicon nitride (Si$_3$N$_4$) microrings with an inferred squeezing level of 10.7 dB on-chip. The squeezing level is robust across multiple devices and pump detunings, and is consistent with the overcoupling degree without noticeable degradation from excess classical noise. We also offer insights to mitigate thermally-induced excess noise, that typically degrades squeezing, by using small-radius rings with a larger free spectral range (450 GHz) and consequently lower parametric oscillation thresholds. Our results demonstrate that Si$_3$N$_4$ is a viable platform for strong quantum noise reduction in a CMOS-compatible, scalable architecture. △ Less

Submitted 18 November, 2024; originally announced November 2024.

Journal ref: Optica 12 (3), pp. 302-308 (2025)

Strong-coupling quantum thermodynamics using a superconducting flux qubit

Authors: Rishabh Upadhyay, Bayan Karimi, Diego Subero, Christoforus Dimas Satrya, Joonas T. Peltonen, Yu-Cheng Chang, Jukka P. Pekola

Abstract: Thermodynamics in quantum circuits aims to find improved functionalities of thermal machines, highlight fundamental phenomena peculiar to quantum nature in thermodynamics, and point out limitations in quantum information processing due to coupling of the system to its environment. An important aspect to achieve some of these goals is the regime of strong coupling that has remained until now a doma… ▽ More Thermodynamics in quantum circuits aims to find improved functionalities of thermal machines, highlight fundamental phenomena peculiar to quantum nature in thermodynamics, and point out limitations in quantum information processing due to coupling of the system to its environment. An important aspect to achieve some of these goals is the regime of strong coupling that has remained until now a domain of theoretical works only. Our aim is to demonstrate strong coupling features in heat transport using a superconducting flux qubit, which is capable of reaching strong to deep-ultra strong coupling regimes, as shown in previous studies. Here, we show experimental evidence of strong coupling by observing a hybridized state of the qubit with two cavities coupled to it, leading to a triplet-like thermal transport via this combined system around the minimum energy of the qubit, at power levels of tens of femtowatts, exceeding by an order of magnitude those in earlier experiments. We also demonstrate close to 100% on-off switching ratio of heat current mediated by photons by applying magnetic flux to the qubit. Our experiment opens a way towards testing debated questions in strong coupling thermodynamics such as what heat in this regime is. We also present a theoretical model that aligns with our experimental findings and explains the mechanism behind heat transport in our device. Furthermore, our experiment opens new possibilities for quantum thermodynamics, aiming to realize true quantum heat engines and refrigerators with enhanced power and efficiency, by leveraging ultra-strong coupling between the system and its environment in future experiments. △ Less

Submitted 20 September, 2025; v1 submitted 16 November, 2024; originally announced November 2024.

Comments: 18 pages, 12 Figures

Quantum Entanglement in Dirac Dynamics via Continuous-Time Quantum Walks in a Quantum Circuit Framework

Authors: Wei-Ting Wang, Yen-Jui Chang, Ching Ray Chang

Abstract: We propose a Continuous-Time Quantum Walks (CTQW) model for one-dimensional Dirac dynamics simulation with higher-order approximation. Our model bridges CTQW with a discrete-time model called Dirac Cellular Automata (DCA) via Quantum Fourier Transformation (QFT). From our continuous-time model, we demonstrate how varying time intervals and position space sizes affect both quantum entanglement betw… ▽ More We propose a Continuous-Time Quantum Walks (CTQW) model for one-dimensional Dirac dynamics simulation with higher-order approximation. Our model bridges CTQW with a discrete-time model called Dirac Cellular Automata (DCA) via Quantum Fourier Transformation (QFT). From our continuous-time model, we demonstrate how varying time intervals and position space sizes affect both quantum entanglement between the internal space and external (position) space of the quantum state and the relativistic effect called Zitterbewegung. We find that the time interval changes the transition range for each site, and the position space sizes affect the value of transition amplitude. Therefore, it shows that the size of spacetime plays a crucial role in the observed quantum entanglement and relativistic phenomena in quantum computers. These results enhance the understanding of the interplay between internal and external spaces in Dirac dynamics through the insights of quantum information theory and enrich the application of the quantum walks-based algorithm. △ Less

Submitted 7 November, 2024; originally announced November 2024.

Quantum-Inspired Portfolio Optimization In The QUBO Framework

Authors: Ying-Chang Lu, Chao-Ming Fu, Lien-Po Yu, Yen-Jui Chang, Ching-Ray Chang

Abstract: A quantum-inspired optimization approach is proposed to study the portfolio optimization aimed at selecting an optimal mix of assets based on the risk-return trade-off to achieve the desired goal in investment. By integrating conventional approaches with quantum-inspired methods for penalty coefficient estimation, this approach enables faster and accurate solutions to portfolio optimization which… ▽ More A quantum-inspired optimization approach is proposed to study the portfolio optimization aimed at selecting an optimal mix of assets based on the risk-return trade-off to achieve the desired goal in investment. By integrating conventional approaches with quantum-inspired methods for penalty coefficient estimation, this approach enables faster and accurate solutions to portfolio optimization which is validated through experiments using a real-world dataset of quarterly financial data spanning over ten-year period. In addition, the proposed preprocessing method of two-stage search further enhances the effectiveness of our approach, showing the ability to improve computational efficiency while maintaining solution accuracy through appropriate setting of parameters. This research contributes to the growing body of literature on quantum-inspired techniques in finance, demonstrating its potential as a useful tool for asset allocation and portfolio management. △ Less

Submitted 13 November, 2024; v1 submitted 8 October, 2024; originally announced October 2024.

Thermal spectrometer for superconducting circuits

Authors: Christoforus Dimas Satrya, Yu-Cheng Chang, Aleksandr S. Strelnikov, Rishabh Upadhyay, Ilari K. Makinen, Joonas T. Peltonen, Bayan Karimi, Jukka P. Pekola

Abstract: Superconducting circuits provide a versatile and controllable platform for studies of fundamental quantum phenomena as well as for quantum technology applications. A conventional technique to read out the state of a quantum circuit or to characterize its properties is based on RF measurement schemes. Here we demonstrate a simple DC measurement of a thermal spectrometer to investigate properties of… ▽ More Superconducting circuits provide a versatile and controllable platform for studies of fundamental quantum phenomena as well as for quantum technology applications. A conventional technique to read out the state of a quantum circuit or to characterize its properties is based on RF measurement schemes. Here we demonstrate a simple DC measurement of a thermal spectrometer to investigate properties of a superconducting circuit, in this proof-of-concept experiment a coplanar waveguide resonator. A fraction of the microwave photons in the resonator is absorbed by an on-chip bolometer, resulting in a measurable temperature rise. By monitoring the DC signal of the thermometer due to this process, we are able to determine the resonance frequency and the lineshape (quality factor) of the resonator. The demonstrated scheme, which is a simple DC measurement, offers a wide frequency band potentially reaching up to 200 GHz, far exceeding that of the typical RF spectrometer. Moreover, the thermal measurement yields a highly frequency independent reference level of the Lorentzian absorption signal. In the low power regime, the measurement is fully calibration-free. Our technique offers an alternative spectrometer for quantum circuits. △ Less

Submitted 26 February, 2025; v1 submitted 20 September, 2024; originally announced September 2024.

Comments: 13 pages and 11 figures

A Study on Quantum Graph Neural Networks Applied to Molecular Physics

Authors: Simone Piperno, Andrea Ceschini, Su Yeon Chang, Michele Grossi, Sofia Vallecorsa, Massimo Panella

Abstract: This paper introduces a novel architecture for Quantum Graph Neural Networks, which is significantly different from previous approaches found in the literature. The proposed approach produces similar outcomes with respect to previous models but with fewer parameters, resulting in an extremely interpretable architecture rooted in the underlying physics of the problem. The architectural novelties ar… ▽ More This paper introduces a novel architecture for Quantum Graph Neural Networks, which is significantly different from previous approaches found in the literature. The proposed approach produces similar outcomes with respect to previous models but with fewer parameters, resulting in an extremely interpretable architecture rooted in the underlying physics of the problem. The architectural novelties arise from three pivotal aspects. Firstly, we employ an embedding updating method that is analogous to classical Graph Neural Networks, therefore bridging the classical-quantum gap. Secondly, each layer is devoted to capturing interactions of distinct orders, aligning with the physical properties of the system. Lastly, we harness SWAP gates to emulate the problem's inherent symmetry, a novel strategy not found currently in the literature. The obtained results in the considered experiments are encouraging to lay the foundation for continued research in this field. △ Less

Submitted 6 August, 2024; originally announced August 2024.

Comments: 20 pages, 10 figures, 3 tables

Journal ref: Physica Scripta, Vol. 100, No. 6, Article No. 065126, pp. 1-18, 2025

Quantum Beam Splitter as a Quantum Coherence Controller

Authors: Li-Ping Yang, Yue Chang

Abstract: We propose a quantum beam splitter (QBS) with tunable reflection and transmission coefficients. More importantly, our device based on a Hermitian parity-time ($\mathcal{PT}$) symmetric system enables the generation and manipulation of asymmetric quantum coherence of the output photons. For the interference of two weak coherent-state inputs, our QBS can produce anti-bunched photons from one output… ▽ More We propose a quantum beam splitter (QBS) with tunable reflection and transmission coefficients. More importantly, our device based on a Hermitian parity-time ($\mathcal{PT}$) symmetric system enables the generation and manipulation of asymmetric quantum coherence of the output photons. For the interference of two weak coherent-state inputs, our QBS can produce anti-bunched photons from one output port and bunched photons from the other, showcasing high parity asymmetry and strong coherence control capabilities. Beyond the Hong-Ou-Mandel effect, perfect photon blockade with vanishing $g^{(2)}(0)$ is achievable in two-photon interference. These striking effects of the QBS fundamentally arise from the parity-symmetry-breaking interaction and the quantum interference between the photon scattering channels. Our results could inspire novel applications and the development of innovative photonic devices for the manipulation of weak quantum light. △ Less

Submitted 13 July, 2024; originally announced July 2024.

Comments: The document consists of 6 figures and spans 22 pages, including detailed appendices

Non-Markovian Multiphoton Chiral Dynamics with Giant Systems

Authors: Yue Chang

Abstract: Chiral interactions enable directional control of quantum light, providing new routes for nonreciprocal photon dynamics. While previous studies focused on single photon regimes, this work explores the non-Markovian multiphoton regime in a giant nonlinear system nonlocally coupled to a one-dimensional waveguide, with a tunable phase difference breaking parity symmetry. We show that single-photon tr… ▽ More Chiral interactions enable directional control of quantum light, providing new routes for nonreciprocal photon dynamics. While previous studies focused on single photon regimes, this work explores the non-Markovian multiphoton regime in a giant nonlinear system nonlocally coupled to a one-dimensional waveguide, with a tunable phase difference breaking parity symmetry. We show that single-photon transport remains reciprocal, but multiphoton dynamics become chiral, generating direction-dependent higher-order correlations and enabling deterministic creation of multiphoton states with nontrivial statistics from a single input direction, even under strong dissipation. The non-Markovian nature allows tuning of transmitted photon statistics via the coupling-point separation. At a specific phase, internal dynamics become Markovian while output photons retain non-Markovian features. Under strong coherent driving, we uncover nonreciprocal dissipative phase transitions and demonstrate how non-Markovianity shapes the critical response. This framework offers a general strategy for controlling chiral multiphoton dynamics in nonlocally coupled waveguide QED systems. △ Less

Submitted 9 September, 2025; v1 submitted 8 July, 2024; originally announced July 2024.

Latent Style-based Quantum GAN for high-quality Image Generation

Authors: Su Yeon Chang, Supanut Thanasilp, Bertrand Le Saux, Sofia Vallecorsa, Michele Grossi

Abstract: Quantum generative modeling is among the promising candidates for achieving a practical advantage in data analysis. Nevertheless, one key challenge is to generate large-size images comparable to those generated by their classical counterparts. In this work, we take an initial step in this direction and introduce the Latent Style-based Quantum GAN (LaSt-QGAN), which employs a hybrid classical-quant… ▽ More Quantum generative modeling is among the promising candidates for achieving a practical advantage in data analysis. Nevertheless, one key challenge is to generate large-size images comparable to those generated by their classical counterparts. In this work, we take an initial step in this direction and introduce the Latent Style-based Quantum GAN (LaSt-QGAN), which employs a hybrid classical-quantum approach in training Generative Adversarial Networks (GANs) for arbitrary complex data generation. This novel approach relies on powerful classical auto-encoders to map a high-dimensional original image dataset into a latent representation. The hybrid classical-quantum GAN operates in this latent space to generate an arbitrary number of fake features, which are then passed back to the auto-encoder to reconstruct the original data. Our LaSt-QGAN can be successfully trained on realistic computer vision datasets beyond the standard MNIST, namely Fashion MNIST (fashion products) and SAT4 (Earth Observation images) with 10 qubits, resulting in a comparable performance (and even better in some metrics) with the classical GANs. Moreover, we analyze the barren plateau phenomena within this context of the continuous quantum generative model using a polynomial depth circuit and propose a method to mitigate the detrimental effect during the training of deep-depth networks. Through empirical experiments and theoretical analysis, we demonstrate the potential of LaSt-QGAN for the practical usage in the context of image generation and open the possibility of applying it to a larger dataset in the future. △ Less

Submitted 4 June, 2024; originally announced June 2024.

Comments: 28 pages, 22 figures

Quantum-Train: Rethinking Hybrid Quantum-Classical Machine Learning in the Model Compression Perspective

Authors: Chen-Yu Liu, En-Jui Kuo, Chu-Hsuan Abraham Lin, Jason Gemsun Young, Yeong-Jar Chang, Min-Hsiu Hsieh, Hsi-Sheng Goan

Abstract: We introduces the Quantum-Train(QT) framework, a novel approach that integrates quantum computing with classical machine learning algorithms to address significant challenges in data encoding, model compression, and inference hardware requirements. Even with a slight decrease in accuracy, QT achieves remarkable results by employing a quantum neural network alongside a classical mapping model, whic… ▽ More We introduces the Quantum-Train(QT) framework, a novel approach that integrates quantum computing with classical machine learning algorithms to address significant challenges in data encoding, model compression, and inference hardware requirements. Even with a slight decrease in accuracy, QT achieves remarkable results by employing a quantum neural network alongside a classical mapping model, which significantly reduces the parameter count from $M$ to $O(\text{polylog} (M))$ during training. Our experiments demonstrate QT's effectiveness in classification tasks, offering insights into its potential to revolutionize machine learning by leveraging quantum computational advantages. This approach not only improves model efficiency but also reduces generalization errors, showcasing QT's potential across various machine learning applications. △ Less

Submitted 10 June, 2024; v1 submitted 18 May, 2024; originally announced May 2024.

Comments: 12 pages, 6 figures

Spin relaxation in inhomogeneous magnetic fields with depolarizing boundaries

Authors: Yue Chang, Shuangai Wan, Shichao Dong, Jie Qin

Abstract: Field-inhomogeneity-induced relaxation of atomic spins confined in vapor cells with depolarizing walls is studied. In contrast to nuclear spins, such as noble-gas spins, which experience minimal polarization loss at cell walls, atomic spins in uncoated cells undergo randomization at the boundaries. This distinct boundary condition results in a varied dependence of the relaxation rate on the field… ▽ More Field-inhomogeneity-induced relaxation of atomic spins confined in vapor cells with depolarizing walls is studied. In contrast to nuclear spins, such as noble-gas spins, which experience minimal polarization loss at cell walls, atomic spins in uncoated cells undergo randomization at the boundaries. This distinct boundary condition results in a varied dependence of the relaxation rate on the field gradient. By solving the Bloch-Torrey equation under fully depolarizing boundary conditions, we illustrate that the relaxation rate induced by field inhomogeneity is more pronounced for spins with a smaller original relaxation rate (in the absence of the inhomogeneous field). We establish an upper limit for the relaxation rate through calculations in the perturbation regime. Moreover, we connect it to the spin-exchange-relaxation-free magnetometers, demonstrating that its linewidth is most sensitive to inhomogeneous fields along the magnetometer's sensitive axis. Our theoretical result agrees with the experimental data for cells subjected to small pump power. A deviation in high input-power scenarios arises from pump field attenuation, resulting in a non-uniformly distributed light shift that behaves like an inhomogeneous magnetic field. △ Less

Submitted 8 December, 2024; v1 submitted 13 March, 2024; originally announced March 2024.

Training Classical Neural Networks by Quantum Machine Learning

Authors: Chen-Yu Liu, En-Jui Kuo, Chu-Hsuan Abraham Lin, Sean Chen, Jason Gemsun Young, Yeong-Jar Chang, Min-Hsiu Hsieh

Abstract: In recent years, advanced deep neural networks have required a large number of parameters for training. Therefore, finding a method to reduce the number of parameters has become crucial for achieving efficient training. This work proposes a training scheme for classical neural networks (NNs) that utilizes the exponentially large Hilbert space of a quantum system. By mapping a classical NN with… ▽ More In recent years, advanced deep neural networks have required a large number of parameters for training. Therefore, finding a method to reduce the number of parameters has become crucial for achieving efficient training. This work proposes a training scheme for classical neural networks (NNs) that utilizes the exponentially large Hilbert space of a quantum system. By mapping a classical NN with $M$ parameters to a quantum neural network (QNN) with $O(\text{polylog} (M))$ rotational gate angles, we can significantly reduce the number of parameters. These gate angles can be updated to train the classical NN. Unlike existing quantum machine learning (QML) methods, the results obtained from quantum computers using our approach can be directly used on classical computers. Numerical results on the MNIST and Iris datasets are presented to demonstrate the effectiveness of our approach. Additionally, we investigate the effects of deeper QNNs and the number of measurement shots for the QNN, followed by the theoretical perspective of the proposed method. This work opens a new branch of QML and offers a practical tool that can greatly enhance the influence of QML, as the trained QML results can benefit classical computing in our daily lives. △ Less

Submitted 26 February, 2024; originally announced February 2024.

Comments: 7 pages, 3 figures