Showing 1–50 of 112 results for author: Khan, A

Pulsed single-photon spectroscopy of an emitter with vibrational coupling

Authors: Sourav Das, Aiman Khan, Elnaz Darsheshdar, Francesco Albarelli, Animesh Datta

Abstract: We analytically derive the quantum state of a single-photon pulse scattered from a single quantum two-level emitter interacting with a vibrational bath. This solution for the quadripartite system enables an information-theoretic characterization of vibrational effects in quantum light spectroscopy. We show that vibration-induced dephasing reduces the quantum Fisher information (QFI) for estimating… ▽ More We analytically derive the quantum state of a single-photon pulse scattered from a single quantum two-level emitter interacting with a vibrational bath. This solution for the quadripartite system enables an information-theoretic characterization of vibrational effects in quantum light spectroscopy. We show that vibration-induced dephasing reduces the quantum Fisher information (QFI) for estimating the emitter's linewidth, largely reflecting the Franck-Condon suppression of light-matter coupling. Comparing time- and frequency-resolved photodetection, we find the latter to be more informative in estimating the emitter's linewidth for stronger vibrational coupling. △ Less

Submitted 16 December, 2025; originally announced December 2025.

Tunable giant Purcell enhancement of quantum light emitters by means of acoustic graphene plasmons

Authors: Justin Gruber, Mahtab A. Khan, Dirk R. Englund, Michael N. Leuenberger

Abstract: Inspired by the remarkable ability of plasmons to boost radiative emission rates, we propose leveraging acoustic graphene plasmons (AGPs) to realize tunable, giant Purcell enhancements for single-photon, entangled-photon, and multipolar quantum emitters. These AGPs are localized inside a cavity defined by a graphene sheet and a metallic nanocube and filled with a dielectric of thickness of a few n… ▽ More Inspired by the remarkable ability of plasmons to boost radiative emission rates, we propose leveraging acoustic graphene plasmons (AGPs) to realize tunable, giant Purcell enhancements for single-photon, entangled-photon, and multipolar quantum emitters. These AGPs are localized inside a cavity defined by a graphene sheet and a metallic nanocube and filled with a dielectric of thickness of a few nanometers and consisting of stacked layers of 2D materials, containing impurities or defects that act as quantum light emitters. Through finite-difference time domain (FDTD) calculations, we show that this geometry can achieve giant Purcell enhancement factors over a large portion of the infrared (IR) spectrum, up to 6 orders of magnitude in the mid-IR and up to 4 orders of magnitude at telecommunications wavelengths, reaching quantum efficiencies of 95\% and 89\%, respectively, with high-mobility graphene. We obtain Purcell enhancement factors for single-photon electric dipole (E1), electric quadrupole (E2), and electric octupole (E3) transitions and two-photon spontaneous emission (2PSE) transitions, of the orders of $10^{4}$, $10^{7}$, $10^{9}$, and $10^9$, respectively, and a quantum efficiency of 79\% for entangled-photon emission with high-mobility graphene at a wavelength of $λ=1.55$ $μ$m. Importantly, AGP mode frequencies depend on the graphene Fermi energy, which can be tuned via electrostatic gating to modulate fluorescence enhancement in real time. As an example, we consider the Purcell enhancement of spontaneous single- and two-photon emissions from an erbium atom inside single-layer (SL) WS$_2$. Our results could be useful for electrically tunable quantum emitter devices with applications in quantum communication and quantum information processing. △ Less

Submitted 2 December, 2025; originally announced December 2025.

Comments: 18 pages, 10 figures

Relativistic Quantum-Speed Limit for Gaussian Systems and Prospective Experimental Verification

Authors: Salman Sajad Wani, Aatif Kaisar Khan, Saif Al-Kuwari, Mir Faizal

Abstract: Timing and phase resolution in satellite QKD, kilometre-scale gravitational-wave detectors, and space-borne clock networks hinge on quantum-speed limits (QSLs), yet benchmarks omit relativistic effects for coherent and squeezed probes. We derive first-order relativistic corrections to the Mandelstam-Tamm and Margolus-Levitin bounds. Starting from the Foldy-Wouthuysen expansion and treating… ▽ More Timing and phase resolution in satellite QKD, kilometre-scale gravitational-wave detectors, and space-borne clock networks hinge on quantum-speed limits (QSLs), yet benchmarks omit relativistic effects for coherent and squeezed probes. We derive first-order relativistic corrections to the Mandelstam-Tamm and Margolus-Levitin bounds. Starting from the Foldy-Wouthuysen expansion and treating $-p^{4}/(8 m^{3} c^{2})$ as a harmonic-oscillator perturbation, we propagate Gaussian states to obtain closed-form QSLs and the quantum Cramér-Rao bound. Relativistic kinematics slow evolution in an amplitude- and squeezing-dependent way, increase both bounds, and introduce an $ε^{2} t^{2}$ phase drift that weakens timing sensitivity while modestly increasing the squeeze factor. A single electron ($ε\approx 1.5\times 10^{-10}$) in a $5.4\,\mathrm{T}$ Penning trap, read out with $149\,\mathrm{GHz}$ quantum-limited balanced homodyne, should reveal this drift within $\sim 15\,\mathrm{min}$ -- within known hold times. These results benchmark relativistic corrections in continuous-variable systems and point to an accessible test of the quantum speed limit in high-velocity or strong-field regimes. △ Less

Submitted 24 November, 2025; originally announced November 2025.

Comments: 16(9+7) PAGES, 4 figures

Journal ref: Physics Letters A, 2025, 131147, ISSN 0375-9601

Quantum Approximate Walk Algorithm

Authors: Ziqing Guo, Jan Balewski, Wenshuo Hu, Alex Khan, Ziwen Pan

Abstract: The encoding of classical to quantum data mapping through trigonometric functions within arithmetic-based quantum computation algorithms leads to the exploitation of multivariate distributions. The studied variational quantum gate learning mechanism, which relies on agnostic gradient optimization, does not offer algorithmic guarantees for the correlation of results beyond the measured bitstring ou… ▽ More The encoding of classical to quantum data mapping through trigonometric functions within arithmetic-based quantum computation algorithms leads to the exploitation of multivariate distributions. The studied variational quantum gate learning mechanism, which relies on agnostic gradient optimization, does not offer algorithmic guarantees for the correlation of results beyond the measured bitstring outputs. Consequently, existing methodologies are inapplicable to this problem. In this study, we present a classical data-traceable quantum oracle characterized by a circuit depth that increases linearly with the number of qubits. This configuration facilitates the learning of approximate result patterns through a shallow quantum circuit (SQC) layout. Moreover, our approach demonstrates that the classical preprocessing of mid-quantum measurement data enhances the interpretability of quantum approximate optimization algorithm (QAOA) outputs without requiring full quantum state tomography. By establishing an inferable mapping between the classical input and quantum circuit outcomes, we obtained experimental results on the state-of-the-art IBM Pittsburgh hardware, which yielded polynomial-time verification of the solution quality. This hybrid framework bridges the gap between near-term quantum capabilities and practical optimization requirements, offering a pathway toward reliable quantum-classical algorithms for industrial applications. △ Less

Submitted 10 November, 2025; originally announced November 2025.

IQNN-CS: Interpretable Quantum Neural Network for Credit Scoring

Authors: Abdul Samad Khan, Nouhaila Innan, Aeysha Khalique, Muhammad Shafique

Abstract: Credit scoring is a high-stakes task in financial services, where model decisions directly impact individuals' access to credit and are subject to strict regulatory scrutiny. While Quantum Machine Learning (QML) offers new computational capabilities, its black-box nature poses challenges for adoption in domains that demand transparency and trust. In this work, we present IQNN-CS, an interpretable… ▽ More Credit scoring is a high-stakes task in financial services, where model decisions directly impact individuals' access to credit and are subject to strict regulatory scrutiny. While Quantum Machine Learning (QML) offers new computational capabilities, its black-box nature poses challenges for adoption in domains that demand transparency and trust. In this work, we present IQNN-CS, an interpretable quantum neural network framework designed for multiclass credit risk classification. The architecture combines a variational QNN with a suite of post-hoc explanation techniques tailored for structured data. To address the lack of structured interpretability in QML, we introduce Inter-Class Attribution Alignment (ICAA), a novel metric that quantifies attribution divergence across predicted classes, revealing how the model distinguishes between credit risk categories. Evaluated on two real-world credit datasets, IQNN-CS demonstrates stable training dynamics, competitive predictive performance, and enhanced interpretability. Our results highlight a practical path toward transparent and accountable QML models for financial decision-making. △ Less

Submitted 16 October, 2025; originally announced October 2025.

Comments: Accepted for oral presentation at QUEST-IS'25. To appear in Springer proceedings

Optimal quantum spectroscopy using single-photon pulses

Authors: Sourav Das, Aiman Khan, Francesco Albarelli, Animesh Datta

Abstract: We provide the ultimate precision attainable in spectroscopy of a quantum emitter using single-photon pulses. We find the maximum for estimating the linewidth to be independent of the details of the emitter's bare Hamiltonian while that for the detunings not to be so. We also identify optimal pulse shapes attaining these precisions. We provide the ultimate precision attainable in spectroscopy of a quantum emitter using single-photon pulses. We find the maximum for estimating the linewidth to be independent of the details of the emitter's bare Hamiltonian while that for the detunings not to be so. We also identify optimal pulse shapes attaining these precisions. △ Less

Submitted 9 October, 2025; originally announced October 2025.

A Duality Theorem for Classical-Quantum States with Applications to Complete Relational Program Logics

Authors: Gilles Barthe, Minbo Gao, Jam Kabeer Ali Khan, Matthijs Muis, Ivan Renison, Keiya Sakabe, Michael Walter, Yingte Xu, Li Zhou

Abstract: Duality theorems play a fundamental role in convex optimization. Recently, it was shown how duality theorems for countable probability distributions and finite-dimensional quantum states can be leveraged for building relatively complete relational program logics for probabilistic and quantum programs, respectively. However, complete relational logics for classical-quantum programs, which combine c… ▽ More Duality theorems play a fundamental role in convex optimization. Recently, it was shown how duality theorems for countable probability distributions and finite-dimensional quantum states can be leveraged for building relatively complete relational program logics for probabilistic and quantum programs, respectively. However, complete relational logics for classical-quantum programs, which combine classical and quantum computations and operate over classical as well as quantum variables, have remained out of reach. The main gap is that while prior duality theorems could readily be derived using optimal transport and semidefinite programming methods, respectively, the combined setting falls out of the scope of these methods and requires new ideas. In this paper, we overcome this gap and establish the desired duality theorem for classical-quantum states. Our argument relies critically on a novel dimension-independent analysis of the convex optimization problem underlying the finite-dimensional quantum setting, which, in particular, allows us to take the limit where the classical state space becomes infinite. Using the resulting duality theorem, we establish soundness and completeness of a new relational program logic, called $\mathsf{cqOTL}$, for classical-quantum programs. In addition, we lift prior restrictions on the completeness of two existing program logics: $\mathsf{eRHL}$ for probabilistic programs (Avanzini et al., POPL 2025) and $\mathsf{qOTL}$ for quantum programs (Barthe et al., LICS 2025). △ Less

Submitted 8 October, 2025; originally announced October 2025.

Comments: 63 pages, 11 figures, 4 tables

Vectorized Attention with Learnable Encoding for Quantum Transformer

Authors: Ziqing Guo, Ziwen Pan, Alex Khan, Jan Balewski

Abstract: Vectorized quantum block encoding provides a way to embed classical data into Hilbert space, offering a pathway for quantum models, such as Quantum Transformers (QT), that replace classical self-attention with quantum circuit simulations to operate more efficiently. Current QTs rely on deep parameterized quantum circuits (PQCs), rendering them vulnerable to QPU noise, and thus hindering their prac… ▽ More Vectorized quantum block encoding provides a way to embed classical data into Hilbert space, offering a pathway for quantum models, such as Quantum Transformers (QT), that replace classical self-attention with quantum circuit simulations to operate more efficiently. Current QTs rely on deep parameterized quantum circuits (PQCs), rendering them vulnerable to QPU noise, and thus hindering their practical performance. In this paper, we propose the Vectorized Quantum Transformer (VQT), a model that supports ideal masked attention matrix computation through quantum approximation simulation and efficient training via vectorized nonlinear quantum encoder, yielding shot-efficient and gradient-free quantum circuit simulation (QCS) and reduced classical sampling overhead. In addition, we demonstrate an accuracy comparison for IBM and IonQ in quantum circuit simulation and competitive results in benchmarking natural language processing tasks on IBM state-of-the-art and high-fidelity Kingston QPU. Our noise intermediate-scale quantum friendly VQT approach unlocks a novel architecture for end-to-end machine learning in quantum computing. △ Less

Submitted 3 September, 2025; v1 submitted 25 August, 2025; originally announced August 2025.

Dynamics of Quantum Droplets in a Quasi-one-dimensional Framework: An Analytical Approach

Authors: Akshat Pandey, Ayan Khan

Abstract: Quantum droplets have been recently observed in dipolar Bose-Einstein condensates (BECs) and in BEC mixtures. This forms the motivation for us to explore the dynamics of these droplets. We make use of the Extended Gross-Pitaevski equation which apart from the effective mean field (MF) interaction, also includes a beyond mean field interaction. The competition of these two interactions in the conte… ▽ More Quantum droplets have been recently observed in dipolar Bose-Einstein condensates (BECs) and in BEC mixtures. This forms the motivation for us to explore the dynamics of these droplets. We make use of the Extended Gross-Pitaevski equation which apart from the effective mean field (MF) interaction, also includes a beyond mean field interaction. The competition of these two interactions in the context of droplet formation is explored. Further, the conditions for droplet formation are studied. △ Less

Submitted 24 July, 2025; originally announced July 2025.

Comments: 7 pages, 4 figures; To appear in Eur. Phys. J. D

Journal ref: Eur. Phys. J. D 79, 99 (2025)

Quantum Computational-Sensing Advantage

Authors: Saeed A. Khan, Sridhar Prabhu, Logan G. Wright, Peter L. McMahon

Abstract: Quantum computing has the potential to deliver large advantages on computational tasks, but advantages for practical tasks are not yet achievable with current hardware. Quantum sensing is an entirely separate quantum technology that can provide its own kind of a quantum advantage. In this Perspective, we explain how the merger of quantum sensing with quantum computing has recently given rise to th… ▽ More Quantum computing has the potential to deliver large advantages on computational tasks, but advantages for practical tasks are not yet achievable with current hardware. Quantum sensing is an entirely separate quantum technology that can provide its own kind of a quantum advantage. In this Perspective, we explain how the merger of quantum sensing with quantum computing has recently given rise to the notion of quantum computational sensing, and a new kind of quantum advantage: a quantum computational-sensing advantage. This advantage can be realized with far lower hardware requirements than purely computational quantum advantage. We explain how several recent proposals and experiments can be understood as quantum computational sensing, and discuss categorizations of the general architectures that quantum-computational-sensing protocols can have. We conclude with an outlook on open questions and the prospects for quantum computational sensors and quantum computational-sensing advantage. △ Less

Submitted 22 July, 2025; originally announced July 2025.

Comments: 27 pages, 147 references

Quantum computational sensing using quantum signal processing, quantum neural networks, and Hamiltonian engineering

Authors: Saeed A. Khan, Sridhar Prabhu, Logan G. Wright, Peter L. McMahon

Abstract: Combining quantum sensing with quantum computing can lead to quantum computational sensors that are able to more efficiently extract task-specific information from physical signals than is possible otherwise. Early examples of quantum computational sensing (QCS) have largely focused on protocols where only a single sensing operation appears before measurement -- with an exception being the recent… ▽ More Combining quantum sensing with quantum computing can lead to quantum computational sensors that are able to more efficiently extract task-specific information from physical signals than is possible otherwise. Early examples of quantum computational sensing (QCS) have largely focused on protocols where only a single sensing operation appears before measurement -- with an exception being the recent application of Grover's algorithm to signal detection. In this paper we present, in theory and numerical simulations, the application of two quantum algorithms -- quantum signal processing and quantum neural networks -- to various binary and multiclass machine-learning classification tasks in sensing. Here sensing operations are interleaved with computing operations, giving rise to nonlinear functions of the sensed signals. We have evaluated tasks based on static and time-varying signals, including spatiotemporal signals. Our approach to optimizing the circuit parameters in a QCS protocol takes into account quantum sampling noise and allows us to engineer protocols that can yield accurate results with as few as just a single measurement shot. In all cases, we have been able to show a regime of operation where a quantum computational sensor can achieve higher accuracy than a conventional quantum sensor, with a simulated accuracy advantage of $>$20 percentage points for some tasks. We also present protocols for performing nonlinear tasks using Hamiltonian-engineered bosonic systems and quantum signal processing with hybrid qubit-bosonic systems. Overall, we have shown that substantial quantum computational-sensing advantages can be obtained even if the quantum system is small, including few-qubit systems, systems comprising a single qubit and a single bosonic mode, and even just a single qubit alone -- raising the prospects for experimental proof-of-principle and practical realizations. △ Less

Submitted 21 July, 2025; originally announced July 2025.

Comments: 22+39 pages, 6+16 figures

Quantum Repeater Chains via Cavity Magnon for Scalable Quantum Networks

Authors: Mughees Ahmed Khan, Syed Shahmir, Muhammad Talha Rahim, Saif Al-Kuwari, Tasawar Abbas

Abstract: Scalable quantum networks require quantum repeaters to overcome major challenges such as photon loss and decoherence in long-distance quantum communication. In this paper, we present a cavity-magnon quantum repeater architecture that exploits the frequency tunability and coherence characteristics of magnonic platforms to enable efficient entanglement swapping across multi-hop networks. Through com… ▽ More Scalable quantum networks require quantum repeaters to overcome major challenges such as photon loss and decoherence in long-distance quantum communication. In this paper, we present a cavity-magnon quantum repeater architecture that exploits the frequency tunability and coherence characteristics of magnonic platforms to enable efficient entanglement swapping across multi-hop networks. Through comprehensive numerical simulations with realistic experimental parameters, we analyze system performance across diverse deployment scenarios and network scales, examining both short-range and long-distance implementations. We identify critical factors influencing performance and scalability, demonstrating that cavity-magnon systems represent a viable and promising quantum repeater platform with significant integration advantages over existing quantum memory technologies. △ Less

Submitted 6 July, 2025; originally announced July 2025.

Sensing Electric Currents in an a-IGZO TFT-Based Circuit Using a Quantum Diamond Microscope

Authors: Mayana Yousuf Ali Khan, Pralekh Dubey, Lakshmi Madhuri P, Ashutosh Kumar Tripathi, Phani Kumar Peddibhotla, Pydi Ganga Bahubalindruni

Abstract: The Quantum Diamond Microscope (QDM) is an emerging magnetic imaging tool enabling noninvasive characterization of electronic circuits through spatially mapping current densities. In this work, we demonstrate wafer-level current sensing of a current mirror circuit composed of 16 amorphous-indium-gallium-zinc oxide (a-IGZO) thin-film transistors (TFTs). a-IGZO TFTs are promising for flexible electr… ▽ More The Quantum Diamond Microscope (QDM) is an emerging magnetic imaging tool enabling noninvasive characterization of electronic circuits through spatially mapping current densities. In this work, we demonstrate wafer-level current sensing of a current mirror circuit composed of 16 amorphous-indium-gallium-zinc oxide (a-IGZO) thin-film transistors (TFTs). a-IGZO TFTs are promising for flexible electronics due to their high performance. Using QDM, we obtain two-dimensional (2D) magnetic field images produced by DC currents, from which accurate current density maps are extracted. Notably, QDM measurements agree well with conventional electrical probing measurements, and enable current sensing in internal circuit paths inaccessible via conventional methods. Our results highlight QDM's capability as a noninvasive diagnostic tool for the characterization of emerging semiconductor technologies, especially oxide-based TFTs. This approach provides essential insights to fabrication engineers, with potential to improve yield and reliability in flexible electronics manufacturing. △ Less

Submitted 24 June, 2025; v1 submitted 21 June, 2025; originally announced June 2025.

Evidence that the Quantum Approximate Optimization Algorithm Optimizes the Sherrington-Kirkpatrick Model Efficiently in the Average Case

Authors: Sami Boulebnane, Abid Khan, Minzhao Liu, Jeffrey Larson, Dylan Herman, Ruslan Shaydulin, Marco Pistoia

Abstract: The Sherrington-Kirkpatrick (SK) model serves as a foundational framework for understanding disordered systems. The Quantum Approximate Optimization Algorithm (QAOA) is a quantum optimization algorithm whose performance monotonically improves with its depth $p$. We analyze QAOA applied to the SK model in the infinite-size limit and provide numerical evidence that it obtains a $(1-ε)$ approximation… ▽ More The Sherrington-Kirkpatrick (SK) model serves as a foundational framework for understanding disordered systems. The Quantum Approximate Optimization Algorithm (QAOA) is a quantum optimization algorithm whose performance monotonically improves with its depth $p$. We analyze QAOA applied to the SK model in the infinite-size limit and provide numerical evidence that it obtains a $(1-ε)$ approximation to the optimal energy with circuit depth $\mathcal{O}(n/ε^{1.13})$ in the average case. Our results are enabled by mapping the task of evaluating QAOA energy onto the task of simulating a spin-boson system, which we perform with modest cost using matrix product states. We optimize QAOA parameters and observe that QAOA achieves $\varepsilon\lesssim2.2\%$ at $p=160$ in the infinite-size limit. We then use these optimized QAOA parameters to evaluate the QAOA energy for finite-sized instances with up to $30$ qubits and find convergence to the ground state consistent with the infinite-size limit prediction. Our results provide strong numerical evidence that QAOA can efficiently approximate the ground state of the SK model in the average case. △ Less

Submitted 12 May, 2025; originally announced May 2025.

Comments: 17 pages, 5 figures

Quantum Energy Teleportation across Multi-Qubit Systems using W-State Entanglement

Authors: Alif Elham Khan, Humayra Anjum, Mahdy Rahman Chowdhury

Abstract: Quantum-energy teleportation (QET) has so far only been realised on a two-qubit platform. Real-world communication, however, typically involves multiple parties. Here we design and experimentally demonstrate the first multi-qubit QET protocol using a robust W-state multipartite entanglement. Three-, four- and five-qubit circuits were executed both on noiseless simulators and on IBM superconducting… ▽ More Quantum-energy teleportation (QET) has so far only been realised on a two-qubit platform. Real-world communication, however, typically involves multiple parties. Here we design and experimentally demonstrate the first multi-qubit QET protocol using a robust W-state multipartite entanglement. Three-, four- and five-qubit circuits were executed both on noiseless simulators and on IBM superconducting hardware. In every case a single sender injects an energy E0 that is then deterministically and decrementally harvested by several remote receivers, confirming that energy introduced at one node can be redistributed among many entangled subsystems at light-speed-limited classical latency. Our results open a practical route toward energy-aware quantum networks. △ Less

Submitted 3 May, 2025; originally announced May 2025.

Exponential advantage in quantum sensing of correlated parameters

Authors: Sridhar Prabhu, Vladimir Kremenetski, Saeed A. Khan, Ryotatsu Yanagimoto, Peter L. McMahon

Abstract: Conventionally in quantum sensing, the goal is to estimate one or more unknown parameters that are assumed to be deterministic - that is, they do not change between shots of the quantum-sensing protocol. We instead consider the setting where the parameters are stochastic: each shot of the quantum-sensing protocol senses parameter values that come from independent random draws. In this work, we exp… ▽ More Conventionally in quantum sensing, the goal is to estimate one or more unknown parameters that are assumed to be deterministic - that is, they do not change between shots of the quantum-sensing protocol. We instead consider the setting where the parameters are stochastic: each shot of the quantum-sensing protocol senses parameter values that come from independent random draws. In this work, we explore three examples where the stochastic parameters are correlated and show how using entanglement provides a benefit in classification or estimation tasks: (1) a two-parameter classification task, for which there is an advantage in the low-shot regime; (2) an $N$-parameter estimation task and a classification variant of it, for which an entangled sensor requires just a constant number (independent of $N$) shots to achieve the same accuracy as an unentangled sensor using exponentially many (${\sim}2^N$) shots; (3) classifying the magnetization of a spin chain in thermal equilibrium, where the individual spins fluctuate but the total spin in one direction is conserved - this gives a practical setting in which stochastic parameters are correlated in a way that an entangled sensor can be designed to exploit. We also present a theoretical framework for assessing, for a given choice of entangled sensing protocol and distributions to discriminate between, how much advantage the entangled sensor would have over an unentangled sensor. Our work motivates the further study of sensing correlated stochastic parameters using entangled quantum sensors - and since classical sensors by definition cannot be entangled, our work shows the possibility for entangled quantum sensors to achieve an exponential advantage in sample complexity over classical sensors, in contrast to the typical quadratic advantage. △ Less

Submitted 23 December, 2025; v1 submitted 30 April, 2025; originally announced April 2025.

QI-MPC: A Hybrid Quantum-Inspired Model Predictive Control for Learning Optimal Policies

Authors: Muhammad Al-Zafar Khan, Jamal Al-Karaki

Abstract: In this paper, we present Quantum-Inspired Model Predictive Control (QIMPC), an approach that uses Variational Quantum Circuits (VQCs) to learn control polices in MPC problems. The viability of the approach is tested in five experiments: A target-tracking control strategy, energy-efficient building climate control, autonomous vehicular dynamics, the simple pendulum, and the compound pendulum. Thre… ▽ More In this paper, we present Quantum-Inspired Model Predictive Control (QIMPC), an approach that uses Variational Quantum Circuits (VQCs) to learn control polices in MPC problems. The viability of the approach is tested in five experiments: A target-tracking control strategy, energy-efficient building climate control, autonomous vehicular dynamics, the simple pendulum, and the compound pendulum. Three safety guarantees were established for the approach, and the experiments gave the motivation for two important theoretical results that, in essence, identify systems for which the approach works best. △ Less

Submitted 17 April, 2025; originally announced April 2025.

Comments: 41 pages, 21 figures

A tensor network approach to sensing quantum light-matter interactions

Authors: Aiman Khan, Francesco Albarelli, Animesh Datta

Abstract: We present the fundamental limits to the precision of estimating parameters of a quantum matter system probed by light, even when some of the light is lost. This practically inevitable scenario leads to a tripartite quantum system of matter, and light -- detected and lost. Evaluating fundamental information theoretic quantities such as the quantum Fisher information of only the detected light was… ▽ More We present the fundamental limits to the precision of estimating parameters of a quantum matter system probed by light, even when some of the light is lost. This practically inevitable scenario leads to a tripartite quantum system of matter, and light -- detected and lost. Evaluating fundamental information theoretic quantities such as the quantum Fisher information of only the detected light was heretofore impossible. We succeed by expressing the final quantum state of the detected light as a matrix product operator. We apply our method to resonance fluorescence and pulsed spectroscopy. For both, we quantify the sub-optimality of continuous homodyning and photo-counting measurements in parameter estimation. For the latter, we find that single-photon Fock state pulses allow higher precision per photon than pulses of coherent states. Our method should be valuable in studies of quantum light-matter interactions, quantum light spectroscopy, quantum stochastic thermodynamics, and quantum clocks. △ Less

Submitted 16 April, 2025; originally announced April 2025.

Comments: 21 pages, 5 figures. See related work by D. Yang et al

Journal ref: PRX Quantum 6, 040343 (2025)

Quantum parallel information exchange (QPIE) hybrid network with transfer learning

Authors: Ziqing Guo, Alex Khan, Victor S. Sheng, Shabnam Jabeen, Ziwen Pan

Abstract: Quantum machine learning (QML) has emerged as an innovative framework with the potential to uncover complex patterns by leveraging quantum systems ability to simulate and exploit high-dimensional latent spaces, particularly in learning tasks. Quantum neural network (QNN) frameworks are inherently sensitive to the precision of gradient calculations and the computational limitations of current quant… ▽ More Quantum machine learning (QML) has emerged as an innovative framework with the potential to uncover complex patterns by leveraging quantum systems ability to simulate and exploit high-dimensional latent spaces, particularly in learning tasks. Quantum neural network (QNN) frameworks are inherently sensitive to the precision of gradient calculations and the computational limitations of current quantum hardware as unitary rotations introduce overhead from complex number computations, and the quantum gate operation speed remains a bottleneck for practical implementations. In this study, we introduce quantum parallel information exchange (QPIE) hybrid network, a new non-sequential hybrid classical quantum model architecture, leveraging quantum transfer learning by feeding pre-trained parameters from classical neural networks into quantum circuits, which enables efficient pattern recognition and temporal series data prediction by utilizing non-clifford parameterized quantum gates thereby enhancing both learning efficiency and representational capacity. Additionally, we develop a dynamic gradient selection method that applies the parameter shift rule on quantum processing units (QPUs) and adjoint differentiation on GPUs. Our results demonstrate model performance exhibiting higher accuracy in ad-hoc benchmarks, lowering approximately 88% convergence rate for extra stochasticity time-series data within 100-steps, and showcasing a more unbaised eigenvalue spectrum of the fisher information matrix on CPU/GPU and IonQ QPU simulators. △ Less

Submitted 5 April, 2025; originally announced April 2025.

Tensor networks for quantum computing

Authors: Aleksandr Berezutskii, Minzhao Liu, Atithi Acharya, Roman Ellerbrock, Johnnie Gray, Reza Haghshenas, Zichang He, Abid Khan, Viacheslav Kuzmin, Dmitry Lyakh, Danylo Lykov, Salvatore Mandrà, Christopher Mansell, Alexey Melnikov, Artem Melnikov, Vladimir Mironov, Dmitry Morozov, Florian Neukart, Alberto Nocera, Michael A. Perlin, Michael Perelshtein, Matthew Steinberg, Ruslan Shaydulin, Benjamin Villalonga, Markus Pflitsch , et al. (3 additional authors not shown)

Abstract: In the rapidly evolving field of quantum computing, tensor networks serve as an important tool due to their multifaceted utility. In this paper, we review the diverse applications of tensor networks and show that they are an important instrument for quantum computing. Specifically, we summarize the application of tensor networks in various domains of quantum computing, including simulation of quan… ▽ More In the rapidly evolving field of quantum computing, tensor networks serve as an important tool due to their multifaceted utility. In this paper, we review the diverse applications of tensor networks and show that they are an important instrument for quantum computing. Specifically, we summarize the application of tensor networks in various domains of quantum computing, including simulation of quantum computation, quantum circuit synthesis, quantum error correction and mitigation, and quantum machine learning. Finally, we provide an outlook on the opportunities and the challenges of the tensor-network techniques. △ Less

Submitted 10 May, 2025; v1 submitted 11 March, 2025; originally announced March 2025.

QDCNN: Quantum Deep Learning for Enhancing Safety and Reliability in Autonomous Transportation Systems

Authors: Ashtakala Meghanath, Subham Das, Bikash K. Behera, Muhammad Attique Khan, Saif Al-Kuwari, Ahmed Farouk

Abstract: In transportation cyber-physical systems (CPS), ensuring safety and reliability in real-time decision-making is essential for successfully deploying autonomous vehicles and intelligent transportation networks. However, these systems face significant challenges, such as computational complexity and the ability to handle ambiguous inputs like shadows in complex environments. This paper introduces a… ▽ More In transportation cyber-physical systems (CPS), ensuring safety and reliability in real-time decision-making is essential for successfully deploying autonomous vehicles and intelligent transportation networks. However, these systems face significant challenges, such as computational complexity and the ability to handle ambiguous inputs like shadows in complex environments. This paper introduces a Quantum Deep Convolutional Neural Network (QDCNN) designed to enhance the safety and reliability of CPS in transportation by leveraging quantum algorithms. At the core of QDCNN is the UU† method, which is utilized to improve shadow detection through a propagation algorithm that trains the centroid value with preprocessing and postprocessing operations to classify shadow regions in images accurately. The proposed QDCNN is evaluated on three datasets on normal conditions and one road affected by rain to test its robustness. It outperforms existing methods in terms of computational efficiency, achieving a shadow detection time of just 0.0049352 seconds, faster than classical algorithms like intensity-based thresholding (0.03 seconds), chromaticity-based shadow detection (1.47 seconds), and local binary pattern techniques (2.05 seconds). This remarkable speed, superior accuracy, and noise resilience demonstrate the key factors for safe navigation in autonomous transportation in real-time. This research demonstrates the potential of quantum-enhanced models in addressing critical limitations of classical methods, contributing to more dependable and robust autonomous transportation systems within the CPS framework. △ Less

Submitted 1 March, 2025; originally announced March 2025.

Comments: 11 Pages, 7 Figures, 4 Tables

Thermal Annealing and Radiation Effects on Structural and Electrical Properties of NbN/GaN Superconductor/Semiconductor Junction

Authors: Stephen Margiotta, Binzhi Liu, Saleh Ahmed Khan, Gabriel Calderon Ortiz, Ahmed Ibreljic, Jinwoo Hwang, A F M Anhar Uddin Bhuiyan

Abstract: In the rapidly evolving field of quantum computing, niobium nitride (NbN) superconductors have emerged as integral components due to their unique structural properties, including a high superconducting transition temperature (Tc), exceptional electrical conductivity, and compatibility with advanced device architectures. This study investigates the impact of high-temperature annealing and high-dose… ▽ More In the rapidly evolving field of quantum computing, niobium nitride (NbN) superconductors have emerged as integral components due to their unique structural properties, including a high superconducting transition temperature (Tc), exceptional electrical conductivity, and compatibility with advanced device architectures. This study investigates the impact of high-temperature annealing and high-dose gamma irradiation on the structural and superconducting properties of NbN films grown on GaN via reactive DC magnetron sputtering. The as-deposited cubic δ-NbN (111) films exhibited a high-intensity XRD peak, high Tc of 12.82K, and an atomically flat surface. Annealing at 500 and 950 °C for varying durations revealed notable structural and surface changes. High-resolution STEM indicated improved local ordering, while AFM showed reduced surface roughness after annealing. XPS revealed a gradual increase in the Nb/N ratio with higher annealing temperatures and durations. High-resolution XRD and STEM analyses showed lattice constant modifications in δ-NbN films, attributed to residual stress changes following annealing. Additionally, XRD phi-scans revealed sixfold symmetry in NbN films due to rotational domains relative to GaN. While Tc remained stable after annealing at 500 °C, increasing the annealing temperature to 950 °C degraded Tc to ~8K and reduced the residual resistivity ratio from 0.85 in as-deposited films to 0.29 after 30 minutes. The effects of gamma radiation (5 Mrad (Si)) were also studied, demonstrating minimal changes to crystallinity and superconducting performance, indicating excellent radiation resilience. These findings highlight the potential of NbN superconductors for integration into advanced quantum devices and their suitability for applications in radiation-intensive environments such as space, satellites, and nuclear power plants. △ Less

Submitted 28 May, 2025; v1 submitted 13 January, 2025; originally announced January 2025.

Journal ref: J. Vac. Sci. Technol. A 43, 042701 (2025)

Predicting Water Quality using Quantum Machine Learning: The Case of the Umgeni Catchment (U20A) Study Region

Authors: Muhammad Al-Zafar Khan, Jamal Al-Karaki, Marwan Omar

Abstract: In this study, we consider a real-world application of QML techniques to study water quality in the U20A region in Durban, South Africa. Specifically, we applied the quantum support vector classifier (QSVC) and quantum neural network (QNN), and we showed that the QSVC is easier to implement and yields a higher accuracy. The QSVC models were applied for three kernels: Linear, polynomial, and radial… ▽ More In this study, we consider a real-world application of QML techniques to study water quality in the U20A region in Durban, South Africa. Specifically, we applied the quantum support vector classifier (QSVC) and quantum neural network (QNN), and we showed that the QSVC is easier to implement and yields a higher accuracy. The QSVC models were applied for three kernels: Linear, polynomial, and radial basis function (RBF), and it was shown that the polynomial and RBF kernels had exactly the same performance. The QNN model was applied using different optimizers, learning rates, noise on the circuit components, and weight initializations were considered, but the QNN persistently ran into the dead neuron problem. Thus, the QNN was compared only by accraucy and loss, and it was shown that with the Adam optimizer, the model has the best performance, however, still less than the QSVC. △ Less

Submitted 27 November, 2024; originally announced November 2024.

Comments: 13 pages, 3 figures

Eavesdropping on the BB84 Protocol using Phase-Covariant Cloning: Experimental Results

Authors: Brian Pigott, Elizabeth Campolongo, Hardik Routray, Alex Khan

Abstract: Though the BB84 protocol has provable security over a noiseless quantum channel, the security is not proven over current noisy technology. The level of tolerable error on such systems is still unclear, as is how much information about a raw key may be obtained by an eavesdropper. We develop a reproducible test to determine the security--or lack thereof--of the protocol in practice. This enables us… ▽ More Though the BB84 protocol has provable security over a noiseless quantum channel, the security is not proven over current noisy technology. The level of tolerable error on such systems is still unclear, as is how much information about a raw key may be obtained by an eavesdropper. We develop a reproducible test to determine the security--or lack thereof--of the protocol in practice. This enables us to obtain an experimental estimate of the information that can be obtained using asymmetric phase-covariant cloning to eavesdrop on the BB84 protocol. △ Less

Submitted 24 September, 2024; originally announced September 2024.

Compressing Hamiltonians with ab initio downfolding for simulating strongly-correlated materials on quantum computers

Authors: Antonios M. Alvertis, Abid Khan, Norm M. Tubman

Abstract: The accurate first-principles description of strongly-correlated materials is an important and challenging problem in condensed matter physics. Ab initio downfolding has emerged as a way of deriving compressed many-body Hamiltonians that maintain the essential physics of strongly-correlated materials. The solution of these material-specific models is still exponentially difficult to generate on cl… ▽ More The accurate first-principles description of strongly-correlated materials is an important and challenging problem in condensed matter physics. Ab initio downfolding has emerged as a way of deriving compressed many-body Hamiltonians that maintain the essential physics of strongly-correlated materials. The solution of these material-specific models is still exponentially difficult to generate on classical computers, but quantum algorithms allow for a significant speed-up in obtaining the ground states of these compressed Hamiltonians. Here we demonstrate that utilizing quantum algorithms for obtaining the properties of downfolded Hamiltonians can indeed yield high-fidelity solutions. By combining ab initio downfolding and variational quantum eigensolvers, we correctly predict the antiferromagnetic state of one-dimensional cuprate $\text{Ca}_2\text{CuO}_3$, the excitonic ground state of monolayer $\text{WTe}_2$, and the charge-ordered state of correlated metal $\text{SrVO}_3$. Numerical simulations utilizing a classical tensor network implementation of variational quantum eigensolvers allow us to simulate large models with up to $54$ qubits and encompassing up to four bands in the correlated subspace, which is indicative of the complexity that our framework can address. Through these methods we demonstrate the potential of classical pre-optimization and downfolding techniques for enabling efficient materials simulation using quantum algorithms. △ Less

Submitted 15 April, 2025; v1 submitted 18 September, 2024; originally announced September 2024.

Journal ref: Phys. Rev. Applied 23, 044028, 2025

A neural processing approach to quantum state discrimination

Authors: Saeed A. Khan, Fangjun Hu, Gerasimos Angelatos, Michael Hatridge, Hakan E. Türeci

Abstract: Although linear quantum amplification has proven essential to the processing of weak quantum signals, extracting higher-order quantum features such as correlations in principle demands nonlinear operations. However, nonlinear processing of quantum signals is often associated with non-idealities and excess noise, and absent a general framework to harness nonlinearity, such regimes are typically avo… ▽ More Although linear quantum amplification has proven essential to the processing of weak quantum signals, extracting higher-order quantum features such as correlations in principle demands nonlinear operations. However, nonlinear processing of quantum signals is often associated with non-idealities and excess noise, and absent a general framework to harness nonlinearity, such regimes are typically avoided. Here we present a framework to uncover general quantum signal processing principles of a broad class of bosonic quantum nonlinear processors (QNPs), inspired by a remarkably analogous paradigm in nature: the processing of environmental stimuli by nonlinear, noisy neural ensembles, to enable perception. Using a quantum-coherent description of a QNP monitoring a quantum signal source, we show that quantum nonlinearity can be harnessed to calculate higher-order features of an incident quantum signal, concentrating them into linearly-measurable observables, a transduction not possible using linear amplifiers. Secondly, QNPs provide coherent nonlinear control over quantum fluctuations including their own added noise, enabling noise suppression in an observable without suppressing transduced information, a paradigm that bears striking similarities to optimal neural codings that allow perception even under highly stochastic neural dynamics. Unlike the neural case, we show that QNP-engineered noise distributions can exhibit non-classical correlations, providing a new means to harness resources such as entanglement. Finally, we show that even simple QNPs in realistic measurement chains can provide enhancements of signal-to-noise ratio for practical tasks such as quantum state discrimination. Our work provides pathways to utilize nonlinear quantum systems as general computation devices, and enables a new paradigm for nonlinear quantum information processing. △ Less

Submitted 9 July, 2025; v1 submitted 5 September, 2024; originally announced September 2024.

Comments: 25+39 pages, 10+7 figures, and 97 references

Classical Benchmarks for Variational Quantum Eigensolver Simulations of the Hubbard Model

Authors: Antonios M. Alvertis, Abid Khan, Thomas Iadecola, Peter P. Orth, Norm Tubman

Abstract: Simulating the Hubbard model is of great interest to a wide range of applications within condensed matter physics, however its solution on classical computers remains challenging in dimensions larger than one. The relative simplicity of this model, embodied by the sparseness of the Hamiltonian matrix, allows for its efficient implementation on quantum computers, and for its approximate solution us… ▽ More Simulating the Hubbard model is of great interest to a wide range of applications within condensed matter physics, however its solution on classical computers remains challenging in dimensions larger than one. The relative simplicity of this model, embodied by the sparseness of the Hamiltonian matrix, allows for its efficient implementation on quantum computers, and for its approximate solution using variational algorithms such as the variational quantum eigensolver. While these algorithms have been shown to reproduce the qualitative features of the Hubbard model, their quantitative accuracy in terms of producing true ground state energies and other properties, and the dependence of this accuracy on the system size and interaction strength, the choice of variational ansatz, and the degree of spatial inhomogeneity in the model, remains unknown. Here we present a rigorous classical benchmarking study, demonstrating the potential impact of these factors on the accuracy of the variational solution of the Hubbard model on quantum hardware, for systems with up to $32$ qubits. We find that even when using the most accurate wavefunction ansätze for the Hubbard model, the error in its ground state energy and wavefunction plateaus for larger lattices, while stronger electronic correlations magnify this issue. Concurrently, spatially inhomogeneous parameters and the presence of off-site Coulomb interactions only have a small effect on the accuracy of the computed ground state energies. Our study highlights the capabilities and limitations of current approaches for solving the Hubbard model on quantum hardware, and we discuss potential future avenues of research. △ Less

Submitted 12 May, 2025; v1 submitted 1 August, 2024; originally announced August 2024.

Journal ref: Quantum 9, 1748 (2025)

Quantum Hardware-Enabled Molecular Dynamics via Transfer Learning

Authors: Abid Khan, Prateek Vaish, Yaoqi Pang, Nikhil Kowshik, Michael S. Chen, Clay H. Batton, Grant M. Rotskoff, J. Wayne Mullinax, Bryan K. Clark, Brenda M. Rubenstein, Norm M. Tubman

Abstract: The ability to perform ab initio molecular dynamics simulations using potential energies calculated on quantum computers would allow virtually exact dynamics for chemical and biochemical systems, with substantial impacts on the fields of catalysis and biophysics. However, noisy hardware, the costs of computing gradients, and the number of qubits required to simulate large systems present major cha… ▽ More The ability to perform ab initio molecular dynamics simulations using potential energies calculated on quantum computers would allow virtually exact dynamics for chemical and biochemical systems, with substantial impacts on the fields of catalysis and biophysics. However, noisy hardware, the costs of computing gradients, and the number of qubits required to simulate large systems present major challenges to realizing the potential of dynamical simulations using quantum hardware. Here, we demonstrate that some of these issues can be mitigated by recent advances in machine learning. By combining transfer learning with techniques for building machine-learned potential energy surfaces, we propose a new path forward for molecular dynamics simulations on quantum hardware. We use transfer learning to reduce the number of energy evaluations that use quantum hardware by first training models on larger, less accurate classical datasets and then refining them on smaller, more accurate quantum datasets. We demonstrate this approach by training machine learning models to predict a molecule's potential energy using Behler-Parrinello neural networks. When successfully trained, the model enables energy gradient predictions necessary for dynamics simulations that cannot be readily obtained directly from quantum hardware. To reduce the quantum resources needed, the model is initially trained with data derived from low-cost techniques, such as Density Functional Theory, and subsequently refined with a smaller dataset obtained from the optimization of the Unitary Coupled Cluster ansatz. We show that this approach significantly reduces the size of the quantum training dataset while capturing the high accuracies needed for quantum chemistry simulations. △ Less

Submitted 12 June, 2024; originally announced June 2024.

Comments: 1- pages, 12 figures

Quantum operations for Kramers-Wannier duality

Authors: Maaz Khan, Syed Anausha Bin Zakir Khan, Arif Mohd

Abstract: We study the Kramers-Wannier duality for the transverse-field Ising lattice on a ring. A careful consideration of the ring boundary conditions shows that the duality has to be implemented with a proper treatment of different charge sectors of both the twisted and untwisted Ising and the dual-Ising Hilbert spaces. We construct a superoperator that explicitly maps the Ising operators to the dual-Isi… ▽ More We study the Kramers-Wannier duality for the transverse-field Ising lattice on a ring. A careful consideration of the ring boundary conditions shows that the duality has to be implemented with a proper treatment of different charge sectors of both the twisted and untwisted Ising and the dual-Ising Hilbert spaces. We construct a superoperator that explicitly maps the Ising operators to the dual-Ising operators. The superoperator naturally acts on the tensor product of the Ising and the dual-Ising Hilbert space. We then show that the relation between our superoperator and the Kramers-Wannier duality operator that maps the Ising Hilbert space to the dual-Ising Hilbert space is naturally provided by quantum operations and the duality can be understood as a quantum operation that we construct. We provide the operator-sum representation for the Kramers-Wannier quantum operations and reproduce the well-known fusion rules. In addition to providing the quantum information perspective on the Kramers-Wannier duality, our explicit protocol will also be useful in implementing the Kramers-Wannier duality on a quantum computer. △ Less

Submitted 15 May, 2024; originally announced May 2024.

Comments: 7 pages, 1 table, 4 figures

On the role of chirping in pulsed single photon spectroscopy

Authors: Elnaz Darsheshdar, Aiman Khan, Francesco Albarelli, Animesh Datta

Abstract: We investigate the precision of estimating the interaction strength between a two-level system (TLS) and a single-photon pulse when the latter is subject to chirping. We consider linear, quadratic, and sinusoidal temporal phases applied to Gaussian and exponential temporal profiles. At the asymptotic time, when the TLS has fully decayed to its ground state, the fundamental precision depends solely… ▽ More We investigate the precision of estimating the interaction strength between a two-level system (TLS) and a single-photon pulse when the latter is subject to chirping. We consider linear, quadratic, and sinusoidal temporal phases applied to Gaussian and exponential temporal profiles. At the asymptotic time, when the TLS has fully decayed to its ground state, the fundamental precision depends solely on the magnitude of its spectral amplitude. For quadratically phase-modulated Gaussian pulses, this is entirely determined by the spectral bandwidth. We provide expressions for evaluating the fundamental precision for general temporal profiles and phase modulations. Finally, we show that experimentally feasible mode-resolved measurements are optimal, or close to it, for chirped, pulsed single photon spectroscopy. △ Less

Submitted 4 May, 2024; originally announced May 2024.

Journal ref: Phys. Rev. A 110, 043710 (2024)

Constant-depth preparation of matrix product states with adaptive quantum circuits

Authors: Kevin C. Smith, Abid Khan, Bryan K. Clark, S. M. Girvin, Tzu-Chieh Wei

Abstract: Adaptive quantum circuits, which combine local unitary gates, midcircuit measurements, and feedforward operations, have recently emerged as a promising avenue for efficient state preparation, particularly on near-term quantum devices limited to shallow-depth circuits. Matrix product states (MPS) comprise a significant class of many-body entangled states, efficiently describing the ground states of… ▽ More Adaptive quantum circuits, which combine local unitary gates, midcircuit measurements, and feedforward operations, have recently emerged as a promising avenue for efficient state preparation, particularly on near-term quantum devices limited to shallow-depth circuits. Matrix product states (MPS) comprise a significant class of many-body entangled states, efficiently describing the ground states of one-dimensional gapped local Hamiltonians and finding applications in a number of recent quantum algorithms. Recently, it was shown that the AKLT state -- a paradigmatic example of an MPS -- can be exactly prepared with an adaptive quantum circuit of constant-depth, an impossible feat with local unitary gates due to its nonzero correlation length [Smith et al., PRX Quantum 4, 020315 (2023)]. In this work, we broaden the scope of this approach and demonstrate that a diverse class of MPS can be exactly prepared using constant-depth adaptive quantum circuits, outperforming optimal preparation protocols that rely on unitary circuits alone. We show that this class includes short- and long-ranged entangled MPS, symmetry-protected topological (SPT) and symmetry-broken states, MPS with finite Abelian, non-Abelian, and continuous symmetries, resource states for MBQC, and families of states with tunable correlation length. Moreover, we illustrate the utility of our framework for designing constant-depth sampling protocols, such as for random MPS or for generating MPS in a particular SPT phase. We present sufficient conditions for particular MPS to be preparable in constant time, with global on-site symmetry playing a pivotal role. Altogether, this work demonstrates the immense promise of adaptive quantum circuits for efficiently preparing many-body entangled states and provides explicit algorithms that outperform known protocols to prepare an essential class of states. △ Less

Submitted 15 October, 2024; v1 submitted 24 April, 2024; originally announced April 2024.

Comments: 25 pages, 5 figures

FedQNN: Federated Learning using Quantum Neural Networks

Authors: Nouhaila Innan, Muhammad Al-Zafar Khan, Alberto Marchisio, Muhammad Shafique, Mohamed Bennai

Abstract: In this study, we explore the innovative domain of Quantum Federated Learning (QFL) as a framework for training Quantum Machine Learning (QML) models via distributed networks. Conventional machine learning models frequently grapple with issues about data privacy and the exposure of sensitive information. Our proposed Federated Quantum Neural Network (FedQNN) framework emerges as a cutting-edge sol… ▽ More In this study, we explore the innovative domain of Quantum Federated Learning (QFL) as a framework for training Quantum Machine Learning (QML) models via distributed networks. Conventional machine learning models frequently grapple with issues about data privacy and the exposure of sensitive information. Our proposed Federated Quantum Neural Network (FedQNN) framework emerges as a cutting-edge solution, integrating the singular characteristics of QML with the principles of classical federated learning. This work thoroughly investigates QFL, underscoring its capability to secure data handling in a distributed environment and facilitate cooperative learning without direct data sharing. Our research corroborates the concept through experiments across varied datasets, including genomics and healthcare, thereby validating the versatility and efficacy of our FedQNN framework. The results consistently exceed 86% accuracy across three distinct datasets, proving its suitability for conducting various QML tasks. Our research not only identifies the limitations of classical paradigms but also presents a novel framework to propel the field of QML into a new era of secure and collaborative innovation. △ Less

Submitted 19 September, 2024; v1 submitted 16 March, 2024; originally announced March 2024.

Comments: Accepted for presentation at IJCNN 2024

Journal ref: 2024 International Joint Conference on Neural Networks (IJCNN), Yokohama, Japan, 2024, pp. 1-9

Brain Tumor Diagnosis Using Quantum Convolutional Neural Networks

Authors: Muhammad Al-Zafar Khan, Abdullah Al Omar Galib, Nouhaila Innan, Mohamed Bennai

Abstract: Accurate classification of brain tumors from MRI scans is critical for effective treatment planning. This study presents a Hybrid Quantum Convolutional Neural Network (HQCNN) that integrates quantum feature-encoding circuits with depth-wise separable convolutional layers to analyze images from a publicly available brain tumor dataset. Evaluated on this dataset, the HQCNN achieved 99.16% training a… ▽ More Accurate classification of brain tumors from MRI scans is critical for effective treatment planning. This study presents a Hybrid Quantum Convolutional Neural Network (HQCNN) that integrates quantum feature-encoding circuits with depth-wise separable convolutional layers to analyze images from a publicly available brain tumor dataset. Evaluated on this dataset, the HQCNN achieved 99.16% training accuracy and 91.47% validation accuracy, demonstrating robust performance across varied imaging conditions. The quantum layers capture complex, non-linear relationships, while separable convolutions ensure computational efficiency. By reducing both parameter count and circuit depth, the architecture is compatible with near-term quantum hardware and resource-constrained clinical environments. These results establish a foundation for integrating quantum-enhanced models into medical-imaging workflows with minimal changes to existing software platforms. Future work will extend evaluation to multi-center cohorts, assess real-time inference on quantum simulators and hardware, and explore integration with surgical-planning systems. △ Less

Submitted 25 May, 2025; v1 submitted 28 January, 2024; originally announced January 2024.

Comments: 10 pages, 8 figures

Overcoming the Coherence Time Barrier in Quantum Machine Learning on Temporal Data

Authors: Fangjun Hu, Saeed A. Khan, Nicholas T. Bronn, Gerasimos Angelatos, Graham E. Rowlands, Guilhem J. Ribeill, Hakan E. Türeci

Abstract: Practical implementation of many quantum algorithms known today is limited by the coherence time of the executing quantum hardware and quantum sampling noise. Here we present a machine learning algorithm, NISQRC, for qubit-based quantum systems that enables inference on temporal data over durations unconstrained by decoherence. NISQRC leverages mid-circuit measurements and deterministic reset oper… ▽ More Practical implementation of many quantum algorithms known today is limited by the coherence time of the executing quantum hardware and quantum sampling noise. Here we present a machine learning algorithm, NISQRC, for qubit-based quantum systems that enables inference on temporal data over durations unconstrained by decoherence. NISQRC leverages mid-circuit measurements and deterministic reset operations to reduce circuit executions, while still maintaining an appropriate length persistent temporal memory in quantum system, confirmed through the proposed Volterra Series analysis. This enables NISQRC to overcome not only limitations imposed by finite coherence, but also information scrambling in monitored circuits and sampling noise, problems that persist even in hypothetical fault-tolerant quantum computers that have yet to be realized. To validate our approach, we consider the channel equalization task to recover test signal symbols that are subject to a distorting channel. Through simulations and experiments on a 7-qubit quantum processor we demonstrate that NISQRC can recover arbitrarily long test signals, not limited by coherence time. △ Less

Submitted 30 August, 2024; v1 submitted 26 December, 2023; originally announced December 2023.

Comments: 15+17 pages, 4+7 figures

Journal ref: Nature Communications 15, 7491 (2024)

Linear-scale simulations of quench dynamics

Authors: Niaz Ali Khan, Wen Chen, Munsif Jan, Gao Xianlong

Abstract: The accurate description and robust computational modeling of the nonequilibrium properties of quantum systems remain a challenge in condensed matter physics. In this work, we develop a linear-scale computational simulation technique for the non-equilibrium dynamics of quantum quench systems. In particular, we report a polynomial-expansion of the Loschmidt echo to describe the dynamical quantum ph… ▽ More The accurate description and robust computational modeling of the nonequilibrium properties of quantum systems remain a challenge in condensed matter physics. In this work, we develop a linear-scale computational simulation technique for the non-equilibrium dynamics of quantum quench systems. In particular, we report a polynomial-expansion of the Loschmidt echo to describe the dynamical quantum phase transitions of noninteracting quantum quench systems. An expansion-based method allows us to efficiently compute the Loschmidt echo for infinitely large systems without diagonalizing the system Hamiltonian. To demonstrate its utility, we highlight quantum quenching dynamics under tight-binding quasicrystals and disordered lattices in one spatial dimension. In addition, the role of the wave vector on the quench dynamics under lattice models is addressed. We observe wave vector-independent dynamical phase transitions in self-dual localization models. △ Less

Submitted 6 February, 2024; v1 submitted 15 November, 2023; originally announced November 2023.

Comments: 10 pages; 9figures

Journal ref: Computer Physics Communications 299, 109132. (2024)

Fixed-point Grover Adaptive Search for Quadratic Binary Optimization Problems

Authors: Ákos Nagy, Jaime Park, Cindy Zhang, Atithi Acharya, Alex Khan

Abstract: We study a Grover-type method for Quadratic Unconstrained Binary Optimization (QUBO) problems. For an $n$-dimensional QUBO problem with $m$ nonzero terms, we construct a marker oracle for such problems with a tuneable parameter, $Λ\in \left[ 1, m \right] \cap \mathbb{Z}$. At $d \in \mathbb{Z}_+$ precision, the oracle uses $O (n + Λd)$ qubits, has total depth of… ▽ More We study a Grover-type method for Quadratic Unconstrained Binary Optimization (QUBO) problems. For an $n$-dimensional QUBO problem with $m$ nonzero terms, we construct a marker oracle for such problems with a tuneable parameter, $Λ\in \left[ 1, m \right] \cap \mathbb{Z}$. At $d \in \mathbb{Z}_+$ precision, the oracle uses $O (n + Λd)$ qubits, has total depth of $O \left( \tfrac{m}Λ \log_2 (n) + \log_2 (d) \right)$, and non-Clifford depth of $O \left( \tfrac{m}Λ \right)$. Moreover, each qubit required to be connected to at most $O \left( \log_2 (Λ+ d) \right)$ other qubits. In the case of a maximum graph cuts, as $d = 2 \left\lceil \log_2 (n) \right\rceil$ always suffices, the depth of the marker oracle can be made as shallow as $O (\log_2 (n))$. For all values of $Λ$, the non-Clifford gate count of these oracles is strictly lower (at least by a factor of $\sim 2$) than previous constructions. Furthermore, we introduce a novel \textit{Fixed-point Grover Adaptive Search for QUBO Problems}, using our oracle design and a hybrid Fixed-point Grover Search, motivated by the works of Boyer et al. and Li et al. This method has better performance guarantees than previous Grover Adaptive Search methods. Some of our results are novel and useful for any method based on Fixed-point Grover Search. Finally, we give a heuristic argument that, with high probability and in $O \left( \tfrac{\log_2 (n)}{\sqrtε} \right)$ time, this adaptive method finds a configuration that is among the best $ε2^n$ ones. △ Less

Submitted 19 October, 2024; v1 submitted 9 November, 2023; originally announced November 2023.

Comments: 25 pages, 5 figures, 1 table. Accepted by IEEE Transactions on Quantum Engineering

Elimination of Static Hazards in Asynchronous Sequential Circuits using Quantum dot Cellular Automata

Authors: Angshuman Khan, Chiradeep Mukherjee, Ankan Kumar Chakraborty, Ratna Chakrabarty, Debashis De

Abstract: There is nowhere else in emerging technology, but in Quantum-dot Cellular Automata, one can find high speed, low power operation, and high packaging density, which deals with electrostatic interaction between electrons within a cell. Literature survey lacks in hazards free design of QCA circuit. Hazards create ambiguous and unpredictable output, which can be avoided. This work considers both hazar… ▽ More There is nowhere else in emerging technology, but in Quantum-dot Cellular Automata, one can find high speed, low power operation, and high packaging density, which deals with electrostatic interaction between electrons within a cell. Literature survey lacks in hazards free design of QCA circuit. Hazards create ambiguous and unpredictable output, which can be avoided. This work considers both hazards and hazards-free asynchronous sequential circuits; both are compared in terms of kink energy, and a better one has been proposed. The circuit simulation has been verified in the QCADesigner tool. △ Less

Submitted 30 October, 2023; originally announced October 2023.

Comments: In Proc. 2015 2nd International Conference on Microelectronics, Circuits and Systems (Micro 2015), Kolkata, India, 2015, vol. II, pp. 140-145

Practical Trainable Temporal Postprocessor for Multistate Quantum Measurement

Authors: Saeed A. Khan, Ryan Kaufman, Boris Mesits, Michael Hatridge, Hakan E. Türeci

Abstract: We develop and demonstrate a trainable temporal post-processor (TPP) harnessing a simple but versatile machine learning algorithm to provide optimal processing of quantum measurement data subject to arbitrary noise processes, for the readout of an arbitrary number of quantum states. We demonstrate the TPP on the essential task of qubit state readout, which has historically relied on temporal proce… ▽ More We develop and demonstrate a trainable temporal post-processor (TPP) harnessing a simple but versatile machine learning algorithm to provide optimal processing of quantum measurement data subject to arbitrary noise processes, for the readout of an arbitrary number of quantum states. We demonstrate the TPP on the essential task of qubit state readout, which has historically relied on temporal processing via matched filters in spite of their applicability only for specific noise conditions. Our results show that the TPP can reliably outperform standard filtering approaches under complex readout conditions, such as high power readout. Using simulations of quantum measurement noise sources, we show that this advantage relies on the TPP's ability to learn optimal linear filters that account for general quantum noise correlations in data, such as those due to quantum jumps, or correlated noise added by a phase-preserving quantum amplifier. Furthermore, we derive an exact analytic form for the optimal TPP weights: this positions the TPP as a linearly-scaling generalization of matched filtering, valid for an arbitrary number of states under the most general readout noise conditions, all while preserving a training complexity that is essentially negligible in comparison to that of training neural networks for processing temporal quantum measurement data. The TPP can be autonomously and reliably trained on measurement data and requires only linear operations, making it ideal for FPGA implementations in cQED for real-time processing of measurement data from general quantum systems. △ Less

Submitted 27 July, 2024; v1 submitted 27 October, 2023; originally announced October 2023.

Comments: 15+24 pages, 6+9 figures, and 61 references; updated to published version

Journal ref: PRX Quantum 5, 020364 (2024)

Pre-optimizing variational quantum eigensolvers with tensor networks

Authors: Abid Khan, Bryan K. Clark, Norm M. Tubman

Abstract: The variational quantum eigensolver (VQE) is a promising algorithm for demonstrating quantum advantage in the noisy intermediate-scale quantum (NISQ) era. However, optimizing VQE from random initial starting parameters is challenging due to a variety of issues including barren plateaus, optimization in the presence of noise, and slow convergence. While simulating quantum circuits classically is ge… ▽ More The variational quantum eigensolver (VQE) is a promising algorithm for demonstrating quantum advantage in the noisy intermediate-scale quantum (NISQ) era. However, optimizing VQE from random initial starting parameters is challenging due to a variety of issues including barren plateaus, optimization in the presence of noise, and slow convergence. While simulating quantum circuits classically is generically difficult, classical computing methods have been developed extensively, and powerful tools now exist to approximately simulate quantum circuits. This opens up various strategies that limit the amount of optimization that needs to be performed on quantum hardware. Here we present and benchmark an approach where we find good starting parameters for parameterized quantum circuits by classically simulating VQE by approximating the parameterized quantum circuit (PQC) as a matrix product state (MPS) with a limited bond dimension. Calling this approach the variational tensor network eigensolver (VTNE), we apply it to the 1D and 2D Fermi-Hubbard model with system sizes that use up to 32 qubits. We find that in 1D, VTNE can find parameters for PQC whose energy error is within 0.5% relative to the ground state. In 2D, the parameters that VTNE finds have significantly lower energy than their starting configurations, and we show that starting VQE from these parameters requires non-trivially fewer operations to come down to a given energy. The higher the bond dimension we use in VTNE, the less work needs to be done in VQE. By generating classically optimized parameters as the initialization for the quantum circuit one can alleviate many of the challenges that plague VQE on quantum computers. △ Less

Submitted 19 October, 2023; originally announced October 2023.

Comments: 10 pages

Design of a fault free Inverter Circuit using Minimum number of cells & related Kink Energy Calculation in Quantum dot Cellular Automata

Authors: Ratna Chakrabarty, Angshuman Khan

Abstract: Quantum dot Cellular Automata (QCA) is the most promising nanotechnology in the field of microelectronics and VLSI systems. QCA-based circuits require less power with a high switching speed of operation compared to CMOS technology. QCA inverter is one of the basic building blocks of QCA circuit design. The conventional QCA inverter design requires many cells. In this paper, we design the QCA inver… ▽ More Quantum dot Cellular Automata (QCA) is the most promising nanotechnology in the field of microelectronics and VLSI systems. QCA-based circuits require less power with a high switching speed of operation compared to CMOS technology. QCA inverter is one of the basic building blocks of QCA circuit design. The conventional QCA inverter design requires many cells. In this paper, we design the QCA inverter circuit using a lesser number of cells. We showed the kink energy calculation for the QCA-implemented inverters as well as the polarization of the circuits. △ Less

Submitted 16 October, 2023; originally announced October 2023.

Comments: In Proc. of 2013 International Conference on Computation and Communication Advancement (IC3A), Kalyani, India, 2013

Behaviors of QCA Inverter due to Cell Displacement and Temperature Variation

Authors: Angshuman Khan, Surajit Sur, Chiradeep Mukherjee, Aninda Sankar Sukla, Ratna Chakrabarty

Abstract: Quantum dot Cellular Automata (QCA) is the emerging area in the field of nanotechnology. Inverter is a fundamental logic primitive in QCA. Molecular, semiconductor, magnetic, and metallic QCA are main methodology in the fabrication of quantum cell. While all types of QCA work on room temperature, metallic one is not suitable in normal temperature. So temperature plays a significant role in QCA cir… ▽ More Quantum dot Cellular Automata (QCA) is the emerging area in the field of nanotechnology. Inverter is a fundamental logic primitive in QCA. Molecular, semiconductor, magnetic, and metallic QCA are main methodology in the fabrication of quantum cell. While all types of QCA work on room temperature, metallic one is not suitable in normal temperature. So temperature plays a significant role in QCA circuit. In this paper, the effect of temperature in two-cell conventional inverter and recently proposed three-cell high polarized inverter has been discussed. The polarization and Kink energy of QCA circuit is influenced due to the change of distance between two cells. This paper clearly mentioned the variation of polarization and kink energy of QCA inverter due to cell displacement. Finally this paper makes a comparison between the conventional two-cell inverter and recently proposed three-cell inverter. The simulation tool QCADesigner has been used to study the effects of QCA △ Less

Submitted 15 October, 2023; originally announced October 2023.

Comments: In Proc. of 2015 2nd International Conference on Nanotechnology (ICNT 2015)

Gate-tunable Superconductivity in Hybrid InSb-Pb Nanowires

Authors: Yan Chen, David van Driel, Charalampos Lampadaris, Sabbir A Khan, Khalifah Alattallah, Lunjie Zeng, Eva Olsson, Tom Dvir, Peter Krogstrup, Yu Liu

Abstract: We present a report on hybrid InSb-Pb nanowires that combine high spin-orbit coupling with a high critical field and a large superconducting gap. Material characterization indicates the Pb layer of high crystal quality on the nanowire side facets. Hard induced superconducting gaps and gate-tunable supercurrent are observed in the hybrid nanowires. These results showcase the promising potential of… ▽ More We present a report on hybrid InSb-Pb nanowires that combine high spin-orbit coupling with a high critical field and a large superconducting gap. Material characterization indicates the Pb layer of high crystal quality on the nanowire side facets. Hard induced superconducting gaps and gate-tunable supercurrent are observed in the hybrid nanowires. These results showcase the promising potential of this material combination for a diverse range of applications in hybrid quantum transport devices. △ Less

Submitted 26 September, 2023; v1 submitted 15 September, 2023; originally announced September 2023.

Comments: 11 pages, 3 figures

Report number: NBI QDEV 2023

Journal ref: Appl. Phys. Lett. 123, 082601 (2023)

Financial Fraud Detection using Quantum Graph Neural Networks

Authors: Nouhaila Innan, Abhishek Sawaika, Ashim Dhor, Siddhant Dutta, Sairupa Thota, Husayn Gokal, Nandan Patel, Muhammad Al-Zafar Khan, Ioannis Theodonis, Mohamed Bennai

Abstract: Financial fraud detection is essential for preventing significant financial losses and maintaining the reputation of financial institutions. However, conventional methods of detecting financial fraud have limited effectiveness, necessitating the need for new approaches to improve detection rates. In this paper, we propose a novel approach for detecting financial fraud using Quantum Graph Neural Ne… ▽ More Financial fraud detection is essential for preventing significant financial losses and maintaining the reputation of financial institutions. However, conventional methods of detecting financial fraud have limited effectiveness, necessitating the need for new approaches to improve detection rates. In this paper, we propose a novel approach for detecting financial fraud using Quantum Graph Neural Networks (QGNNs). QGNNs are a type of neural network that can process graph-structured data and leverage the power of Quantum Computing (QC) to perform computations more efficiently than classical neural networks. Our approach uses Variational Quantum Circuits (VQC) to enhance the performance of the QGNN. In order to evaluate the efficiency of our proposed method, we compared the performance of QGNNs to Classical Graph Neural Networks using a real-world financial fraud detection dataset. The results of our experiments showed that QGNNs achieved an AUC of $0.85$, which outperformed classical GNNs. Our research highlights the potential of QGNNs and suggests that QGNNs are a promising new approach for improving financial fraud detection. △ Less

Submitted 3 September, 2023; originally announced September 2023.

Comments: 15 pages, 18 figures, 4 tables

Journal ref: Quantum Mach. Intell. 6, 7 (2024)

Quantum State Tomography using Quantum Machine Learning

Authors: Nouhaila Innan, Owais Ishtiaq Siddiqui, Shivang Arora, Tamojit Ghosh, Yasemin Poyraz Koçak, Dominic Paragas, Abdullah Al Omar Galib, Muhammad Al-Zafar Khan, Mohamed Bennai

Abstract: Quantum State Tomography (QST) is a fundamental technique in Quantum Information Processing (QIP) for reconstructing unknown quantum states. However, the conventional QST methods are limited by the number of measurements required, which makes them impractical for large-scale quantum systems. To overcome this challenge, we propose the integration of Quantum Machine Learning (QML) techniques to enha… ▽ More Quantum State Tomography (QST) is a fundamental technique in Quantum Information Processing (QIP) for reconstructing unknown quantum states. However, the conventional QST methods are limited by the number of measurements required, which makes them impractical for large-scale quantum systems. To overcome this challenge, we propose the integration of Quantum Machine Learning (QML) techniques to enhance the efficiency of QST. In this paper, we conduct a comprehensive investigation into various approaches for QST, encompassing both classical and quantum methodologies; We also implement different QML approaches for QST and demonstrate their effectiveness on various simulated and experimental quantum systems, including multi-qubit networks. Our results show that our QML-based QST approach can achieve high fidelity (98%) with significantly fewer measurements than conventional methods, making it a promising tool for practical QIP applications. △ Less

Submitted 20 August, 2023; originally announced August 2023.

Comments: 18 pages, 19 figures

Journal ref: Quantum Mach. Intell. 6, 28 (2024)

Financial Fraud Detection: A Comparative Study of Quantum Machine Learning Models

Authors: Nouhaila Innan, Muhammad Al-Zafar Khan, Mohamed Bennai

Abstract: In this research, a comparative study of four Quantum Machine Learning (QML) models was conducted for fraud detection in finance. We proved that the Quantum Support Vector Classifier model achieved the highest performance, with F1 scores of 0.98 for fraud and non-fraud classes. Other models like the Variational Quantum Classifier, Estimator Quantum Neural Network (QNN), and Sampler QNN demonstrate… ▽ More In this research, a comparative study of four Quantum Machine Learning (QML) models was conducted for fraud detection in finance. We proved that the Quantum Support Vector Classifier model achieved the highest performance, with F1 scores of 0.98 for fraud and non-fraud classes. Other models like the Variational Quantum Classifier, Estimator Quantum Neural Network (QNN), and Sampler QNN demonstrate promising results, propelling the potential of QML classification for financial applications. While they exhibit certain limitations, the insights attained pave the way for future enhancements and optimisation strategies. However, challenges exist, including the need for more efficient Quantum algorithms and larger and more complex datasets. The article provides solutions to overcome current limitations and contributes new insights to the field of Quantum Machine Learning in fraud detection, with important implications for its future development. △ Less

Submitted 9 August, 2023; originally announced August 2023.

Comments: 30 pages, 15 figures, and 2 tables

Machine learning unveils multiple Pauli blockades in the transport spectroscopy of bilayer graphene double-quantum dots

Authors: Anuranan Das, Adil Khan, Ankan Mukherjee, Bhaskaran Muralidharan

Abstract: Recent breakthroughs in the transport spectroscopy of 2-D material quantum-dot platforms have engendered a fervent interest in spin-valley qubits. In this context, Pauli blockades in double quantum dot structures form an important basis for multi-qubit initialization and manipulation. Focusing on double quantum dot structures, and the experimental results, we first build theoretical models to capt… ▽ More Recent breakthroughs in the transport spectroscopy of 2-D material quantum-dot platforms have engendered a fervent interest in spin-valley qubits. In this context, Pauli blockades in double quantum dot structures form an important basis for multi-qubit initialization and manipulation. Focusing on double quantum dot structures, and the experimental results, we first build theoretical models to capture the intricate interplay between externally fed gate voltages and the physical properties of the 2-D system in such an architecture, allowing us to effectively simulate Pauli blockades. Employing the master equations for transport and considering extrinsic factors such as electron-photon interactions, we thoroughly investigate all potential occurrences of Pauli blockades. Notably, our research reveals two remarkable phenomena: (i) the existence of multiple resonances within a bias triangle, and (ii) the occurrence of multiple Pauli blockades. Leveraging our model to train a machine learning algorithm, we successfully develop an automated method for real-time detection of multiple Pauli blockade regimes. Through numerical predictions and validations against test data, we identify where and how many Pauli blockades are likely to occur. We propose that our model can effectively detect the generic class of Pauli blockades in practical experimental setups and hence serves as the foundation for future experiments on qubits that utilize 2-D material platforms. △ Less

Submitted 9 August, 2023; originally announced August 2023.

Comments: 11 pages, 7 figures, Comments welcome

Tackling Sampling Noise in Physical Systems for Machine Learning Applications: Fundamental Limits and Eigentasks

Authors: Fangjun Hu, Gerasimos Angelatos, Saeed A. Khan, Marti Vives, Esin Türeci, Leon Bello, Graham E. Rowlands, Guilhem J. Ribeill, Hakan E. Türeci

Abstract: The expressive capacity of physical systems employed for learning is limited by the unavoidable presence of noise in their extracted outputs. Though present in physical systems across both the classical and quantum regimes, the precise impact of noise on learning remains poorly understood. Focusing on supervised learning, we present a mathematical framework for evaluating the resolvable expressive… ▽ More The expressive capacity of physical systems employed for learning is limited by the unavoidable presence of noise in their extracted outputs. Though present in physical systems across both the classical and quantum regimes, the precise impact of noise on learning remains poorly understood. Focusing on supervised learning, we present a mathematical framework for evaluating the resolvable expressive capacity (REC) of general physical systems under finite sampling noise, and provide a methodology for extracting its extrema, the eigentasks. Eigentasks are a native set of functions that a given physical system can approximate with minimal error. We show that the REC of a quantum system is limited by the fundamental theory of quantum measurement, and obtain a tight upper bound for the REC of any finitely-sampled physical system. We then provide empirical evidence that extracting low-noise eigentasks can lead to improved performance for machine learning tasks such as classification, displaying robustness to overfitting. We present analyses suggesting that correlations in the measured quantum system enhance learning capacity by reducing noise in eigentasks. The applicability of these results in practice is demonstrated with experiments on superconducting quantum processors. Our findings have broad implications for quantum machine learning and sensing applications. △ Less

Submitted 30 October, 2023; v1 submitted 29 July, 2023; originally announced July 2023.

Comments: 37 pages, 10 figures, 1 table. Significantly expanded version of arXiv:2301.00042 [quant-ph], covering physical systems operating in the classical and quantum regimes

Journal ref: Phys. Rev. X 13, 041020 (2023)

Does entanglement enhance single-molecule pulsed biphoton spectroscopy?

Authors: Aiman Khan, Francesco Albarelli, Animesh Datta

Abstract: It depends. For a single molecule interacting with one mode of a biphoton probe, we show that the spectroscopic information has three contributions, only one of which is a genuine two-photon contribution. When all the scattered light can be measured, solely this contribution exists and can be fully extracted using unentangled measurements. Furthermore, this two-photon contribution can, in principl… ▽ More It depends. For a single molecule interacting with one mode of a biphoton probe, we show that the spectroscopic information has three contributions, only one of which is a genuine two-photon contribution. When all the scattered light can be measured, solely this contribution exists and can be fully extracted using unentangled measurements. Furthermore, this two-photon contribution can, in principle, be matched by an optimised but unentangled single-photon probe. When the matter system spontaneously emits into inaccessible modes, an advantage due to entanglement can not be ruled out. In practice, time-frequency entanglement does enhance spectroscopic performance of the oft-studied weakly-pumped spontaneous parametric down conversion (PDC) probes. For two-level systems and coupled dimers, more entangled PDC probes yield more spectroscopic information, even in the presence of emission into inaccessible modes. Moreover, simple, unentangled measurements can capture between 60% - 90% of the spectroscopic information. We thus establish that biphoton spectroscopy using source-engineered PDC probes and unentangled measurements can provide tangible quantum enhancement. Our work underscores the intricate role of entanglement in single-molecule spectroscopy using quantum light. △ Less

Submitted 5 July, 2023; originally announced July 2023.

Comments: 18+14 pages, 12+14 figures, 1+0 tables

Journal ref: Quantum Sci. Technol. 9 035004 (2024)

Dynamics of Bright Soliton Under Cubic-Quartic Interactions in Quasi One-Dimensional Geometry

Authors: Argha Debnath, Ayan Khan, Prasanta K Panigrahi

Abstract: Recent inspection of liquid-like state in ultracold atomic gases due to the stabilization mechanism through the delicate balance between effective mean-field and beyond mean-field (BMF) interactions, has motivated us to study the modified/extended Gross-Pitaevskii (eGP) equation which includes the BMF contribution. In this article, we focus on variational analysis of solitonic regime with eGP equa… ▽ More Recent inspection of liquid-like state in ultracold atomic gases due to the stabilization mechanism through the delicate balance between effective mean-field and beyond mean-field (BMF) interactions, has motivated us to study the modified/extended Gross-Pitaevskii (eGP) equation which includes the BMF contribution. In this article, we focus on variational analysis of solitonic regime with eGP equation while the soliton is subjected to an obstacle. This reveals different scattering scenarios of the soliton with explicit dependence of the BMF interaction. The results show the existence of tunneling, partial and complete trappings, in different parameter domains. These observations are further corroborated by the fast-Fourier transform method. In the later part we also extend our analysis to trapped systems. The controlled trapping in defect potential and its release can be potentially useful for quantum information storage. △ Less

Submitted 3 October, 2023; v1 submitted 22 May, 2023; originally announced May 2023.

Comments: 11 pages, 27 figures; To be published in European Physical Journal Plus

Journal ref: Eur. Phys. J. Plus 138, 954 (2023)

Conservation Laws for the Nonlinear Klein-Gordon Equation in (1+1)-, (2+1), and (3+1)-dimensions

Authors: Muhammad Al-Zafar Khan

Abstract: We study soliton solutions to the Klein-Gordon equation via Lie symmetries and the travelling-wave ansatz. It is shown, by taking a linear combination of the spatial and temporal Lie point symmetries, that soliton solutions naturally exist, and the resulting field lies in the complex plane. We normalize the field over a finite spatial interval, and thereafter, specify one of the integration consta… ▽ More We study soliton solutions to the Klein-Gordon equation via Lie symmetries and the travelling-wave ansatz. It is shown, by taking a linear combination of the spatial and temporal Lie point symmetries, that soliton solutions naturally exist, and the resulting field lies in the complex plane. We normalize the field over a finite spatial interval, and thereafter, specify one of the integration constants in terms of the other. Solutions to a specific type of nonlinear Klein-Gordon equation are studied via the sine-cosine method, and a real soliton wave is obtained. Lastly, the multiplier method is used to construct conservation laws for this particular nonlinear Klein-Gordon equation in (3 + 1)-dimensions. △ Less

Submitted 16 May, 2023; originally announced May 2023.

Comments: 11 pages, 3 figures