Quantum Physics
Exact stabilizer scars in two-dimensional $U(1)$ lattice gauge theory
Sabhyata Gupta, Piotr Sierant, Luis Santos et al. (+1 more)
The complexity of highly excited eigenstates is a central theme in nonequilibrium many-body physics, underpining questions of thermalization, classical simulability, and
quantum information structure. In this work, considering the paradigmatic Rokhsar-Kivelson model, we connect
quantum many-body scarring in Abelian
lattice gauge theories to an emergent stabilizer structure. We identify a distinct class of scarred eigenstates, termed sublattice scars, originating from gauge-invariant zero modes that form exact stabilizer states. Remarkably, although the underlying
Hamiltonian is not a stabilizer
Hamiltonian, its eigenspectrum intrinsically hosts exact stabilizer eigenstates. These sublattice scars exhibit vanishing stabilizer Rényi
entropy together with finite, highly structured
entanglement, enabling efficient classical simulation. Exploiting their stabilizer structure, we construct explicit Clifford
circuits that prepare these states in a two-dimensional
lattice gauge model. Our results demonstrate that the scarred subspace of the Rokhsar-Kivelson spectrum forms an intrinsic stabilizer
manifold, revealing a direct connection between stabilizer
quantum information,
lattice gauge constraints, and
quantum many-body scarring.
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All authors Sabhyata Gupta, Piotr Sierant, Luis Santos, Paolo Stornati
Quantum Physics · Cross-listed from cond-mat.mes-hall
Scaling of silicon spin qubits under correlated noise
Juan S. Rojas-Arias, Leon C. Camenzind, Yi-Hsien Wu et al. (+8 more)
The path to fault-tolerant
quantum computing hinges on hardware that scales while remaining compatible with
quantum error correction (QEC). Silicon
spin qubits are a leading hardware candidate because they combine industrial fabrication compatibility with a nanoscale footprint that could accommodate millions of
qubits on a chip. However, their suitability for QEC remains uncertain since spatially correlated noise naturally emerges from the resulting close proximity of
qubits. These correlations increase the likelihood of simultaneous errors and erode the redundancy that QEC depends on. Here we quantify the spatial extent of noise correlations in a five-
qubit silicon array and assess their impact on QEC. We identify two distinct sources of correlated noise: global
magnetic field drifts that generate perfectly correlated fluctuations, and charge noise from two-level fluctuators that produces short-range correlations decaying within neighboring
qubits. While
magnetic drifts represent a critical correlated noise source that can compromise QEC, they can be mitigated. In contrast, the measured charge noise correlations are moderate, electrically tunable, and compatible with fault-tolerant operation with minimal
qubit overhead. Our results establish quantitative
benchmarks for correlated noise and clarify how such correlations impact the viability of
quantum error correction in scalable
qubit arrays.
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All authors Juan S. Rojas-Arias, Leon C. Camenzind, Yi-Hsien Wu, Peter Stano, Akito Noiri, Kenta Takeda, Takashi Nakajima, Takashi Kobayashi, Giordano Scappucci, Daniel Loss, Seigo Tarucha
Quantum Physics
Alberto López-García, Aikaterini Vasilakou and Javier Cerrillo
We employ a
quantum computer to simulate the effect of
spin impurities on nitrogen-vacancy (NV) centers in diamond. As these defects operate as nanoscale
quantum sensors, modeling
quantum noise is crucial to identify limitations in precision. The analysis is performed by means of
quantum state
tomography on two transmon
qubits, representing respectively the NV center and a single
spin impurity, modeling either a
nuclear spin or an additional NV center. We demonstrate a versatile platform to simulate
benchmark protocols such as Ramsey or Hahn-echo. Although we focus on a two-
spin system, the same approach opens the door to using
quantum processors as scalable simulators of many-
spin environments, intractable in classical simulation due to the rapid exponential growth of the
Hilbert space. The results reveal the effect different
spin-sensor coupling regimes have on coherence, helping to identify detection schemes that maximize the sensitivity under the effect of
impurities. Moreover, the role of
entanglement generation is analyzed using the Peres-Horodecki criterion and CHSH inequalities. Although no violation of the latter is observed, the presence of
entanglement is confirmed.
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Quantum Physics
QFlowNet: Fast, Diverse, and Efficient Unitary Synthesis with Generative Flow Networks
Inhoe Koo, Hyunho Cha and Jungwoo Lee
Unitary Synthesis, the decomposition of a
unitary matrix into a sequence of
quantum gates, is a fundamental challenge in
quantum compilation. Prevailing reinforcement learning (RL) approaches are often hampered by sparse reward signals, which necessitate complex reward shaping or long training times, and typically converge to a single policy, lacking solution diversity. In this work, we propose QFlowNet, a novel framework that learns efficiently from sparse signals by pairing a Generative Flow Network (GFlowNet) with
Transformers. Our approach addresses two key challenges. First, the GFlowNet framework is fundamentally designed to learn a diverse policy that samples solutions proportional to their reward, overcoming the single-solution limitation of RL while offering faster inference than other generative models like
diffusion. Second, the
Transformers act as a powerful encoder, capturing the non-local structure of
unitary matrices and compressing a high-dimensional state into a dense latent representation for the policy network. Our
agent achieves an overall success rate of 99.7% on a 3-
qubit benchmark(lengths 1-12) and discovers a diverse set of compact
circuits, establishing QFlowNet as an efficient and diverse paradigm for
unitary synthesis.
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Quantum Physics
Motion-induced directionality of collective emission in a non-chiral waveguide
Yoan Spahn, Jens Hartmann, Benedikt Saalfrank et al. (+3 more)
We report the experimental observation of motion-induced directionality in collective atomic emission within a hollow-core waveguide, establishing a general principle: directional interactions can emerge from collective phase engineering alone. Remarkably, neither single-emitter asymmetry nor any asymmetry in the
geometric arrangement of the system is required - both the atom-field coupling and the spontaneous emission are fully isotropic in our system. Instead, Raman-induced effective two-level emitters with spatially oscillating transition dipole phases and atomic motion give rise to controllable directionality, reaching values up to 0.89(1). We study the correlations of the superfluorescent bursts close to and well above the threshold to collective emission; we find thermal statistics below and a buildup of coherence above it. Numerical simulations based on the Truncated Wigner Approximation for
spins yield good agreement. Additionally we present a simple model based on position uncertainty capable of reproducing the observed directionality. Our results open a new route to directional interactions in non-
chiral systems, with direct implications for the design of directional metamaterials and photonic structures built from isotropic constituents.
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All authors Yoan Spahn, Jens Hartmann, Benedikt Saalfrank, Michael Fleischhauer, Thomas Halfmann, Thorsten Peters
Quantum Physics
Analytical Quantum Full-Wave Analysis of Few-Photon Transport Through a Superconducting Cavity Qubit
Soomin Moon and Thomas E. Roth
A promising way to scale up superconducting
quantum computers is to link different devices together using propagating
photons. Correspondingly, accurately modeling the
quantum information transfer in such
quantum interconnects is critical to advancing this emerging technology. To accomplish this, a full-wave
quantum numerical model is essential for describing the few-
photon transport characteristics of various components. Unfortunately, validating the accuracy of such numerical models remains a difficult challenge due to the lack of appropriate analytical solutions for standard component types. Recently, progress has been made on creating the first-ever analytical
quantum full-wave solutions for a superconducting
circuit quantum device. These efforts considered the case of two-
photon transport through an empty rectangular waveguide cavity and the interactions of
photons inside a closed rectangular waveguide cavity with a transmon
qubit formed by a Josephson junction connected across the terminals of a small wire dipole
antenna. Here, we advance these efforts by considering the one- and two-
photon transport properties through a rectangular waveguide cavity containing a
qubit in this form when the cavity is interfaced with via two coaxial ports. Such devices can be used in various ways for
quantum interconnects, such as to form parts of a
quantum memory or a
photon source. We perform this analysis leveraging a
quantum input-output theory formalism to derive the relevant single- and two-
photon transport characteristics of interest. We then examine the signatures of the nonlinear
quantum scattering effects in the good and bad cavity regimes of cavity
quantum electrodynamics. In the future, these analytical results can be used to validate numerical full-wave
quantum solvers for modeling
quantum interconnects.
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Quantum Physics
QAOA-Predictor: Forecasting Success Probabilities and Minimal Depths for Efficient Fixed-Parameter Optimization
Rodrigo Coelho, Georg Kruse and Jeanette Miriam Lorenz
Quantum Computing promises to solve complex combinatorial optimization problems more efficiently than classical methods, with the
Quantum Approximate Optimization
Algorithm (QAOA) being a leading candidate. Recent fixed-parameter variations of QAOA eliminate costly run-time optimization, but determining their optimal initialization as well as the number of required layers (p) for a target solution remains a critical, unsolved challenge. In this work, we propose a novel approach using a Graph Neural Network (GNN) to predict QAOA performance: Based on a graph representation of the problem, the GNN forecasts the probability of the optimal solution in the resulting distribution across different parameter initializations and layer depths for a wide variety of combinatorial optimization problems. We demonstrate that the GNN accurately predicts QAOA performance within a 10% margin of the true values. Furthermore, the model exhibits strong generalization capabilities across unseen problem classes, larger problem sizes, and higher layer counts. Our approach allows to identify viable problem instances for QAOA and to select an adequate parameter initialization strategy with minimal layer depth, without the need of costly parameter optimization.
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Quantum Physics
Nuclear interference by electronic de-orthogonalisation
Matisse Wei-Yuan Tu, Angel Rubio and E.K.U. Gross
Interference is a universal consequence of superposition, yet in composite
quantum systems it can encode correlations between subsystems. We show that in coupled electron-
nuclear dynamics, interference in the
nuclear density can arise dynamically even when it is initially absent. Starting from a superposition of orthogonal Born-Oppenheimer electronic states, we demonstrate within the exact factorisation framework that genuine non-adiabatic electron-
nuclear correlations induce de-orthogonalisation of the electronic factors, thereby generating interference terms in the
nuclear density. Such interference has no counterpart in adiabatic
evolution. Unlike conventional
nuclear wave-packet interference or interference that merely reflects electronic coherence in a chosen basis, the effect identified here is a manifestation of the compositeness of the full electron-
nuclear state.
Nuclear density interference thus emerges as a direct dynamical signature of correlated
quantum motion in composite systems.
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Quantum Physics
Layer-wise QUBO-Based Training of CNN Classifiers for Quantum Annealing
Mostafa Atallah and Rebekah Herrman
Variational
quantum circuits for image classification suffer from barren plateaus, while
quantum kernel methods scale quadratically with
dataset size. We propose an iterative framework based on Quadratic Unconstrained Binary Optimization (QUBO) for training the classifier head of convolutional neural networks (CNNs) via
quantum annealing, entirely avoiding gradient-based
circuit optimization. Following the Extreme Learning Machine paradigm, convolutional filters are randomly initialized and frozen, and only the fully connected layer is optimized. At each iteration, a convex quadratic surrogate derived from the feature Gram
matrix replaces the non-quadratic cross-
entropy loss, yielding an iteration-stable
curvature proxy. A per-output decomposition splits the $C$-class problem into $C$ independent QUBOs, each with $(d+1)K$ binary variables, where $d$ is the feature dimension and $K$ is the bit precision, so that problem size depends on the image resolution and bit precision, not on the number of training samples. We evaluate the method on six image-classification
benchmarks (sklearn digits, MNIST, Fashion-MNIST, CIFAR-10, EMNIST, KMNIST). A precision study shows that accuracy improves monotonically with bit resolution, with 10 bits representing a practical minimum for effective optimization; the 15-bit formulation remains within the
qubit and coupler limits of current D-Wave Advantage hardware. The 20-bit formulation matches or exceeds classical
stochastic gradient descent on MNIST, Fashion-MNIST, and EMNIST, while remaining competitive on CIFAR-10 and KMNIST. All experiments use simulated annealing, establishing a baseline for direct deployment on
quantum annealing hardware.
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Quantum Physics
Improved Grid-Based Simulation of Coulombic Dynamics
Xiaoning Feng, Hans Hon Sang Chan and David P. Tew
Accurate time-dependent
quantum dynamics of Coulombic systems on grid-based representations remains computationally demanding due to the singularity of the Coulomb potential, which necessitates extremely fine spatial grids to mitigate discretisation errors. We propose two complementary correction schemes that, under identical resource budgets, consistently outperform the uncorrected counterparts. The first scheme modifies the potential operator to incorporate grid-basis structure into its representation, while the second introduces a corrected initial wavefunction inspired by analytical solutions of softened Coulomb potentials. Applied to hydrogenic systems, these corrections deliver improved energy accuracy and time fidelity across long evolutions. Beyond classical simulations, the proposed framework aligns naturally with
quantum computing architectures, where the corrected operators and states can be encoded through truncated Walsh and
Fourier series expansions. A resource analysis for the representative 2D hydrogen system yields a
circuit depth of $1.5\times10^{8}$ gates over 6,000 Trotter steps. This study thus establishes practical strategies toward high-accuracy Coulombic dynamics on both classical and emerging
quantum platforms.
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Quantum Physics
Fingerprint Recognition of Partial Discharge Signals in Deep Learning Enhanced Rydberg Atomic Sensors
Yi-Ming Yin, Qi-Feng Wang, Yu Ma et al. (+16 more)
Partial discharge originates from microscopic insulation imperfections in high-voltage apparatus and is widely considered a critical marker of incipient deterioration. Conventional partial discharge detection methods are typically constrained by limited bandwidth and often rely on predefined feature extraction, which impedes reliable recognition of broadband transient signals. In this work, we employ a Rydberg atomic sensor to directly capture time-domain responses of partial discharge emissions and construct distinctive spectral fingerprints for different types. A 1D ResNet deep learning model is then applied to recognize these fingerprints from time-domain signals without manual feature engineering. Under increased source-
antenna distances, where spectral features are significantly attenuated, the model attains a recognition accuracy of approximately 94% across four partial discharge categories, demonstrating robustness to attenuation and noise. We further validate the approach in a simulated early-warning scenario, where partial discharge signals mixed with noise are analyzed and the model successfully generates predictive alarms. These results underscore the potential of integrating Rydberg-based broadband sensing with data-driven analysis for non-invasive, high-sensitivity diagnostics of electrical insulation systems.
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All authors Yi-Ming Yin, Qi-Feng Wang, Yu Ma, Tian-Yu Han, Jia-Dou Nan, Zheng-Yuan Zhang, Han-Chao Chen, Xin Liu, Shi-Yao Shao, Jun Zhang, Qing Li, Ya-Jun Wang, Dong-Yang Zhu, Qiao-Qiao Fang, Chao Yu, Bang Liu, Li-Hua Zhang, Dong-Sheng Ding, Bao-Sen Shi
Quantum Physics
Toward multi-purpose quantum communication networks: from theory to protocol implementation
Lucas Hanouz, Marc Kaplan, Jean-Sébastien Kersaint Tournebize et al. (+2 more)
Most
quantum communication networks around the world are used for a single task:
quantum key distribution. In order to initiate the transition to multi-purpose
quantum communication networks, we demonstrate the implementation of two different tasks on the same
quantum key distribution hardware. Specifically, we focus on
quantum oblivious transfer and
quantum tokens. Our main contribution is to establish a methodology that greatly simplifies the expertise required to achieve the deployment, assess its performance, and evaluate its feasibility at a large scale. The implementation that we present is full-stack. It is based on a development framework that allows running user-defined applications both with simulated or real
quantum communication backend. The hardware used for the implementation is VeriQloud's Qline. The simulation backend reproduces exactly the inputs and outputs of the real hardware, but also its losses and errors. It can therefore be used to validate the implementation before running it on the real hardware. The sources of the software that we use are fully open, making our research reproducible. The security of the implementations on real hardware are discussed with respect to security bounds previously known in the literature. We also discuss the engineering choices that we made in order to make the implementations feasible. By establishing a methodology to evaluate the performance and security of
quantum communication protocols, we take a significant step towards industrializing and deploying large-scale, multi-purpose
quantum communication networks.
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All authors Lucas Hanouz, Marc Kaplan, Jean-Sébastien Kersaint Tournebize, Chin-te Liao, Anne Marin
Quantum Physics · Cross-listed from cond-mat.mes-hall
Jarosław Pawłowski and Mateusz Krawczyk
We introduce a generalizable framework for identifying effective
Hamiltonians directly from experimental data in solid-state
quantum systems. Our unsupervised autoencoder-based approach incorporates the governing physics (here, the S-
matrix formalism) directly into the decoder, enabling physically meaningful
Hamiltonian inference without labeled
Hamiltonian parameters. Through numerical experiments on a triple
quantum dot chain, we demonstrate automated characterization of programmable solid-state simulators from transport measurements. The model accurately infers
Hamiltonian parameters and generalizes beyond the training domain, while exhibiting finite capacity as the parameter space becomes increasingly broad or diverse. We further demonstrate robustness to noisy measurements and show that the model can be trained on transport data generated outside the
Hamiltonian family assumed by the physics decoder, while still identifying meaningful effective parameters.
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Quantum Physics
Discrete-modulation continuous-variable quantum key distribution with probabilistic amplitude shaping over a linear quantum channel
Emanuele Parente, Michele N. Notarnicola, Stefano Olivares et al. (+3 more)
The practical implementation difficulties arising from the Gaussian modulation of the GG02 protocol lead us to investigate the possibilities offered by the combination of probabilistic amplitude shaping technique and quadrature amplitude modulation formats in the context of continuous variable
quantum key distribution systems. Our interest comes from the fact that quadrature amplitude modulation and probabilistic shaping can be implemented with current technologies and are widely used in classical telecom equipment. In this treatment, we assume to work in the scenario of a linear
quantum channel and we analyze maximum achievable secure key rates, maximum reachable distances and the resilience to noise of our discrete-modulation based protocol with respect to GG02, which is taken as a
benchmark. In particular, we deal with the infinite key size regime, consider a homodyne detection scheme, and analyze what happens for different cardinalities of the input alphabet at different distances, in the case of collective attacks and in the reverse reconciliation picture. We find that our protocol, beyond being easily reproducible in the laboratory, provides a way to closely approach the theoretical performance offered by GG02 and, at the same time, preserves the ability to assure an unconditional security level.
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All authors Emanuele Parente, Michele N. Notarnicola, Stefano Olivares, Enrico Forestieri, Luca Potì, Marco Secondini
Quantum Physics
An Extensible Quantum Network Simulator Built on ns-3: Q2NS Design and Evaluation
Adam Pearson, Francesco Mazza, Marcello Caleffi et al. (+1 more)
As
quantum networking hardware remains costly and not yet widely accessible, simulation tools are essential for the design and evaluation of
quantum network architectures and protocols. However, designing a scalable and computationally efficient
quantum network simulator is intrinsically challenging: i)
quantum dynamics must be emulated on classical computing platforms while capturing the stateful and non-local nature of
entanglement, a
quantum resource without any classical networking analog; ii)
quantum networking is inherently hybrid, as protocol execution also fundamentally depends on classical signaling. This makes a tight and faithful co-simulation of
quantum operations and classical message exchanges a core requirement. In this light, we present Q2NS, a modular and extensible
quantum network simulator, built on top of ns-3, designed to seamlessly integrate
quantum-network primitives with ns-3's established classical protocol stack. Q2NS adopts a modular architecture that decouples protocol control logic from node- and channel-level operations, enabling rapid prototyping and adaptation across heterogeneous and evolving
Quantum Internet scenarios. Q2NS natively supports multiple
quantum state representations through a unified interface, allowing interchangeable state-vector, density-
matrix, and stabilizer backends. We validate Q2NS through realistic use-case studies and comprehensive
benchmarks, demonstrating superior computational efficiency over representative state-of-the-art alternatives, while preserving modeling flexibility. Finally, we provide a dedicated visualization tool that jointly captures physical and
entanglement-enabled connectivity and supports entangled-state manipulations, facilitating an intuitive interpretation of
entanglement dynamics and protocol behavior. Q2NS offers a flexible, open, and scalable simulation platform for advancing
Quantum Internet research.
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All authors Adam Pearson, Francesco Mazza, Marcello Caleffi, Angela Sara Cacciapuoti
Quantum Physics
Identification of quantum generative circuits with parallel quantum neural network
Zheping Wu, Xiaopeng Huang, Hengyue Jia et al. (+2 more)
The rapid emergence of
quantum technology has raised new challenges in distinguishing various
quantum circuits of similar functions. In this work, we propose parallel
quantum embedding neural network (ParaQuanNet) for the efficient identification of
quantum generative
circuits via classifications of the corresponding output data. Specifically, we generated W-like states with eight generative
quantum circuits realizing the generative
quantum denoising
diffusion probabilistic models (QDDPM). Our ParaQuanNet can classify these eight classes of generated
quantum data with an accuracy of {$99.5\%$}, even though all of them are trained to generate the same types of
quantum data. With a novel design of parallel
quantum embedding unit (PQEU) in our neural networks, our ParaQuanNet enables the
quantum kernel
circuit parallelly process all the receptive fields of
quantum data, which empowers the
quantum data processing efficiency. We also integrate the mutually unbiased measurements into our ParaQuanNet and further improve its performance. We apply our ParaQuanNet on the classification of classical data sets and demonstrate a good performance of
quantum neural networks on these tasks. Our approach demonstrates good robustness to noisy data and the
circuit-level noise with a Python realization in a classical GPU. Our results highlight ParaQuanNet as a scalable and effective framework for
quantum circuits identification, contributing to the broader development of
quantum machine intelligence.
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All authors Zheping Wu, Xiaopeng Huang, Hengyue Jia, Haobin Shi, Wei-Wei Zhang
Quantum Physics
Charging power enhancement at the phase transition of a non-integrable quantum battery
D. Farina, M. Sassetti, V. Cataudella et al. (+2 more)
Exploiting many-body interaction and critical phenomena to improve the performance of
quantum batteries is an emerging and promising line of research. A central question in this direction is whether
quantum phase transitions can enhance the charging energy or the power. While preliminary works have addressed this problem in fine-tuned integrable models, its characterization in non-integrable systems remains limited due to the demanding numerical requirements. Here, we investigate a one-dimensional Axial Next-Nearest-Neighbor Ising model as an example of non-integrable
quantum battery charged via a
quantum-quench protocol. In contrast to integrable cases, we find that criticality in this setting can lead to a pronounced enhancement of the charging power. Our findings inform
quantum-battery design of many-
qubit systems and are amenable to experimental verification on current
quantum-simulation platforms, including neutral-atom arrays.
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All authors D. Farina, M. Sassetti, V. Cataudella, D. Ferraro, N. Traverso Ziani
Quantum Physics
Merged amplitude encoding for Chebyshev quantum Kolmogorov-Arnold networks
Hikaru Wakaura
Quantum Kolmogorov–Arnold networks evaluate each edge activation function as a
quantum inner product, creating a trade-off between
qubit count and the number of
circuit executions per forward pass. We introduce merged amplitude encoding for the Chebyshev-edge classical-to-classical QKAN (CCQKAN), which packs the element-wise products of all $n$ input-edge vectors of an output node into a single amplitude state, reducing
circuit executions by a factor of $n$ while using no more
qubits than the sequential SWAP-test baseline once its measurement registers are counted. The merged and original
circuits compute the same quantity exactly; we characterize what this consolidation costs in trainability. Across 10 network configurations and 16 random seeds, the two
circuits are indistinguishable under ideal conditions. Under finite-shot training with a hardware-standard SPSA optimizer at matched measurement budget, the merged
circuit incurs a small systematic loss deficit that grows with network width (significant in 6 of 10 configurations), consistent with amplified
estimator noise in the merged state; under depolarizing noise no systematic difference is detected. A parameter-matched single-
qubit data re-uploading baseline reaches substantially higher loss on the same tasks while using fewer
qubits and executions, delineating complementary resource regimes. No
quantum advantage is claimed.
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Quantum Physics
Fast and memory-efficient classical simulation of quantum machine learning via forward and backward gate fusion
Yoshiaki Kawase
While real
quantum devices have been increasingly used to conduct research focused on achieving
quantum advantage or
quantum utility in recent years, executing deep
quantum circuits or performing
quantum machine learning with large-scale data on current noisy intermediate-scale
quantum devices remains challenging, making classical simulation essential for
quantum machine learning research. However, such classical simulation often suffers from the cost of gradient calculations, requiring enormous
memory or computational time. To address these problems, we propose a method to fuse multiple consecutive gates in each of the forward and backward paths to improve throughput by minimizing global
memory accesses. As a result, we achieved approximately $20$ times throughput improvement for a Hardware-Efficient Ansatz with $12$ or more
qubits, reaching over $30$ times improvement on a mid-range consumer GPU with limited
memory bandwidth. By combining our proposed method with gradient checkpointing, we drastically reduced
memory usage, making it possible to train a large-scale
quantum machine learning model, a $20$-
qubit, $1{,}000$-layer model with $60{,}000$ parameters, using $1{,}000$ samples in approximately $20$ minutes per epoch. This implies that we can train the model on large
datasets, comprising tens of thousands of samples, like MNIST or CIFAR-10, within a realistic time frame (e.g., $20$ hours per epoch). Thus, our proposed method significantly accelerates such classical simulations, making a significant contribution to advancing research in
quantum machine learning and variational
quantum algorithms, such as verifying
algorithms on large
datasets or investigating learning theories of deep
quantum circuits like barren plateaus.
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Quantum Physics
Generation of 12 dB squeezed light from a waveguide optical parametric amplifier using a machine-learning-controlled spatial light modulator
Gyeongmin Ha, Kazuki Hirota, Takahiro Kashiwazaki et al. (+5 more)
We demonstrate the generation of $12.1 \pm 0.2$ dB squeezed light from a periodically poled lithium niobate (PPLN) waveguide optical parametric amplifier (OPA). While single-pass OPAs offer squeezed light with THz-order bandwidths, loss from spatial mode mismatch between the squeezed light and the local oscillator (LO) previously capped the squeezing level at $\sim$10 dB [K. Hirota et al., Opt. Express 34, 7958 (2026)]. In this work, we minimize this loss by introducing a machine-learning-optimized spatial light modulator (SLM) in the path of the LO. Specifically, we employed a double-reflection configuration to increase the spatial degrees of freedom, and directly used the measured squeezing level as the optimization's objective function.
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All authors Gyeongmin Ha, Kazuki Hirota, Takahiro Kashiwazaki, Takumi Suzuki, Akito Kawasaki, Warit Asavanant, Mamoru Endo, Akira Furusawa
Quantum Physics · Cross-listed from physics.atom-ph
Geometric mechanisms enabling spin- and enantio-sensitive observables in one photon ionization of chiral molecules
Philip Caesar M. Flores, Stefanos Carlström, Serguei Patchkovskii et al. (+3 more)
We examine
spin-resolved photoionization of randomly oriented
chiral molecules via circularly polarized light, and revisit earlier predictions of Cherepkov (J. Phys. B: Atom. Mol. Phys. 16, 1543, 1983). We will show that the dynamical origin of
spin- and enantio-sensitive observables arise from two intrinsic mechanisms that are quantified by two pseudovectors stemming from the
geometric properties of the photoionization dipoles in
spin space and in real space, and an extrinsic mechanism which is a directional bias introduced by the well-defined direction of light polarization. These mechanisms arise solely from electric dipole interactions. Consequently, this means that the ten independent parameters that was earlier predicted by Cherepkov to fully describe
spin-resolved photoionization of
chiral molecules can be reduced as moments of these three pseudovectors. We also find that the molecular pseudoscalars describing the
spin- and enantio-sensitive components of the yield can be described by the flux of these pseudovectors through the energy shell, which changes sign upon switching enantiomers. Our results provide compact expressions for these observables which provide an intuitive picture on what determines the strength of these
spin- and enantio-sensitive observables. The approach can be readily generalized to photoexcitation, multiphoton processes, and arbitrary field polarizations. Regardless of the specific driving conditions, the resulting
spin- and enantio-sensitive observables are still controlled by the same three pseudovectors, underscoring their universal role as the primary generators of chirality-induced
spin asymmetries, emphasizing their fundamental
geometric origin and the universality of the mechanism identified here.
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All authors Philip Caesar M. Flores, Stefanos Carlström, Serguei Patchkovskii, Misha Ivanov, Andres F. Ordonez, Olga Smirnova
Quantum Physics
Non-commutative integration method and generalized coherent states
A. I. Breev and D. M. Gitman
The relationship between states obtained by the non-commutative integration method of the Schrödinger equation on Lie groups and generalized coherent states is investigated. It is shown that such solutions belong to the class of generalized coherent states when the corresponding {\lambda}-representation is real.
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Quantum Physics
Correction scheme for molecular total energies from quantum phase estimation under limited qubit resources
Nobuki Inoue and Hisao Nakamura
We propose a practical method for accurately evaluating molecular total energies using a hybrid approach that integrates fault-tolerant
quantum computers with classical computing. Our scheme consists of two complementary components:
quantum dominant orbital selection (QDOS) and subspace dynamical correlation (SDC). QDOS extracts only the essential active orbitals from the complete active space (CAS) configuration interaction (CI) state on a
quantum computer, yielding a compact active space suitable for classical CASCI calculations. SDC then evaluates dynamical-correlation corrections for the CASCI energy using this compact state, which remains tractable on classical machines. To demonstrate that the CAS energy obtained on a
quantum computer can be post-corrected by SDC, we examine two frameworks: multireference perturbation theory and tailored coupled-cluster theory. Our scheme enables effective treatment of relatively large molecular systems by combining limited
quantum and classical resources.
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Quantum Physics
Neural quantum support vector data description for one-class classification
Changjae Im, Hyeondo Oh and Daniel K. Park
One-class classification (OCC) is a fundamental problem in machine learning with numerous applications, such as anomaly detection and quality control. With the increasing complexity and dimensionality of modern
datasets, there is a growing demand for advanced OCC techniques with better expressivity and efficiency. We introduce Neural
Quantum Support Vector Data Description (NQSVDD), a classical-
quantum hybrid framework for OCC that performs end-to-end optimized hierarchical representation learning. NQSVDD integrates a classical neural network with trainable
quantum data encoding and a variational
quantum circuit, enabling the model to learn nonlinear feature transformations tailored to the OCC objective. The hybrid architecture maps input data into an intermediate high-dimensional feature space and subsequently projects it into a compact latent space defined through
quantum measurements. Importantly, both the feature
embedding and the latent representation are jointly optimized such that normal data form a compact cluster, for which a minimum-volume enclosing hypersphere provides an effective decision boundary. Experimental evaluations on
benchmark datasets demonstrate that NQSVDD achieves competitive or superior AUC performance compared to classical Deep SVDD and
quantum baselines, while maintaining parameter efficiency and robustness under realistic noise conditions.
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Quantum Physics
Qudit Designs and Where to Find Them
Namit Anand, Jeffrey Marshall, Jason Saied et al. (+2 more)
Unitary t-designs are some of the most versatile tools in
quantum information theory. Their applications range from randomized benchmarking and shadow
tomography, to more fundamental ones such as emulating
quantum chaos and establishing exponential separations between classical and
quantum query complexity. While
unitary designs originating from a group structure, such as the Clifford group, have proven to be incredibly useful for
qubit systems, unfortunately, this is no longer true for qudits. In fact, the classification of finite-group representations rules out the existence of
unitary 2-designs for arbitrary qudit dimensions. This severely limits the applicability of standard
quantum information primitives when it comes to qudit systems. We overcome these limitations with a three-fold contribution. First, we introduce a general technique to construct families of weighted state t-designs in arbitrary qudit dimensions. These weighted state-designs generalize classical shadow
tomography protocol from
qubits to qudits. Second, we introduce a Clifford character RB that allows us to
benchmark the qudit Clifford group in any dimension, including non-
prime-power dimensions. And third, we establish bounds on the
quantum circuit complexity of generating approximate
unitary-designs from native gates in existing
quantum hardware such as high-
spin and cavity-QED qudits. Our work further highlights the analogy between
spin and optical coherent states by proving that
spin-GKP codewords form a state 2-design while
spin coherent states do not; in direct analogy with the optical case. This work is structured as a pedagogical and self-contained introduction to
unitary designs and their applications to qudit systems.
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All authors Namit Anand, Jeffrey Marshall, Jason Saied, Eleanor Rieffel, Andrea Morello
Quantum Physics
Quantum Algorithms for Approximate Graph Isomorphism Testing
Prateek P. Kulkarni
The graph isomorphism problem asks whether two graphs are identical up to vertex relabeling. While the exact problem admits quasi-
polynomial-time classical
algorithms, many applications in molecular comparison, noisy network analysis, and pattern recognition require a flexible notion of structural similarity. We study the
quantum query complexity of approximate graph isomorphism testing, where two graphs on $n$ vertices drawn from the Erdős–Rényi distribution $\mathcal{G} (n,1/2)$ are considered approximately isomorphic if they can be made isomorphic by at most $k$ edge edits. We present a
quantum algorithm based on MNRS
quantum walk search over the product graph $\Gamma(G,H)$ of the two input graphs. When the graphs are approximately isomorphic, the
quantum walk search detects vertex pairs belonging to a dense near isomorphic matching set; candidate pairings are then reconstructed via local consistency propagation and verified via a Grover-accelerated consistency check. We prove that this approach achieves query complexity $\mathcal{O}(n^{3/2} \log n/\varepsilon)$, where $\varepsilon$ parameterizes the approximation threshold. We complement this with an $\Omega(n^2)$ classical lower bound for constant approximation, establishing a genuine
polynomial quantum speedup in the query model. We extend the framework to spectral similarity measures based on graph
Laplacian eigenvalues, as well as weighted and attributed graphs. Small-scale simulation results on
quantum simulators for graphs with up to twenty vertices demonstrate compatibility with near-term
quantum devices.
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Quantum Physics
Rate-Fidelity Tradeoffs in All-Photonic and Memory-Equipped Quantum Switches
Panagiotis Promponas, Leonardo Bacciottini, Paul Polakos et al. (+3 more)
Quantum entanglement switches are a key building block for early
quantum networks, and a central design question is whether near-term devices should use only flying
photons or also incorporate
quantum memories. We compare two architectures: an all-photonic
entanglement generation switch (EGS) that repeatedly attempts Bell-state measurements (BSM) without storing
qubits, and a
quantum memory-equipped switch that buffers
entanglement and triggers measurements only when heralded connectivity is available (herald-then-swap control). These two designs trade off simple, memoryless operation that avoids decoherence and
memory-induced latency against heralding-based control that buffers
entanglement to use BSMs more efficiently. We formalize both models under a common hardware abstraction and characterize their achievable rate-fidelity regions, yielding a benchmarking methodology that translates hardware and protocol parameters into network-level performance. Numerical evaluation quantifies the rate-fidelity tradeoffs of both models, identifies operating regions in which each architecture dominates, and shows how hardware and protocol knobs can be tuned to meet application-specific targets.
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All authors Panagiotis Promponas, Leonardo Bacciottini, Paul Polakos, Gayane Vardoyan, Don Towsley, Leandros Tassiulas
Quantum Physics · Cross-listed from physics.optics
Ultra-low loss piezo-optomechanical low-confinement silicon nitride platform for visible wavelength quantum photonic circuits
Mayank Mishra, Gwangho Choi, Wenhua He et al. (+7 more)
Realizing photonic
quantum computing at scale requires integrated
circuits that combine ultra-low loss with fast, low-power, low-hysteresis, and low-crosstalk reconfiguration. These requirements are particularly challenging at visible wavelengths, where many
quantum resource-state generators, including single-
photon sources and
quantum memories, naturally operate. Low-confinement silicon nitride waveguides offer the requisite loss performance, but conventional thermo-optic modulators dissipate significant static power, driving thermal crosstalk and precluding cryogenic operation. Visible-wavelength piezo-optomechanical
circuits avoid these drawbacks, but existing demonstrations rely either on high-confinement waveguides with propagation losses of 35-100 dB/m, or on low-confinement platforms using foundry-incompatible PZT. Here we combine piezo-optomechanical actuation with a CMOS-foundry-fabricated, low-confinement silicon nitride platform, achieving 2.6 dB/m propagation loss at 780 nm, megahertz-scale modulation bandwidth, a half-wave voltage-length product of 2.8 Vm, and negligible hysteresis. We demonstrate reconfigurable Mach-Zehnder interferometers with 0.63 dB loss per spiral phase shifter, enabling deep, actively reconfigurable visible-wavelength
quantum photonic
circuits.
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All authors Mayank Mishra, Gwangho Choi, Wenhua He, Gina M. Talcott, Katherine Kearney, Michael Gehl, Andrew Leenheer, Daniel Dominguez, Nils T. Otterstrom, Matt Eichenfield
Quantum Physics
Steering paths mid-flight for fault-tolerance in measurement-based holonomic gates
Anirudh Lanka, Juan Garcia-Nila and Todd A. Brun
Continuous measurement-based holonomic
quantum computation provides a route to universal logical computation in
quantum error correcting codes. We introduce a fault-tolerant framework for implementing measurement-based holonomic gates that leverages continuous measurements with real-time feedback. We show that non-Markovian decoherence is intrinsically suppressed through the
quantum Zeno effect, while Markovian errors are identified by the decoding of measurement records to reveal the rotated syndrome subspace populated during the
evolution. This information enables steering holonomic paths mid-flight to ensure that the final
evolution realizes the target logical gate. We further demonstrate that non-adiabatic effects give rise to measurement-induced errors, and we show that these can also be corrected by an analogous protocol. This approach relaxes the stringent adiabaticity requirement and enables faster implementation of holonomic gates.
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Quantum Physics
Measurement of a quantum system using spin-mechanical conversion
A. A. Wood, D. S. Rice, T. Xie et al. (+5 more)
Levitated macroscopic particles exhibiting
quantum mechanical effects are garnering increased
attention as a means for precision sensing and testing
quantum mechanics. Defects in diamond, such as the nitrogen-vacancy (NV) centre possess optically-addressable
spins with long coherence times at room temperature and offer an intriguing system to examine
quantum spin dynamics coupled to a macroscopic classical particle. In this work, we convert the outcome of a
quantum measurement on an ensemble of
spins into a macroscopic rotation of the host particle via
spin-mechanical coupling. Following a sequence of green
laser and microwave control pulses,
spin-mechanical coupling between the final
qubit spin state and the host particle – an electrically-levitated diamond – exerts a torque on the particle that deflects a weak near-infra-red
laser beam. We measure
spin readout contrast in excess of 70%, and demonstrate pulsed mechanical detection of coherent Rabi oscillations,
spin-echo interferometry and $T_1$-induced relaxation. We directly measure with temporal resolution the particle reorientation from a 60\,attonewton-metre
spin torque induced by flipping the
spins. Our results open up interesting new opportunities for levitated
spin-mechanical systems using pulsed control, from improved sensing to the prospect of realising macroscopic
quantum superposition states.
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All authors A. A. Wood, D. S. Rice, T. Xie, F. H. Cassells, R. M. Goldblatt, T. Delord, G. Hétet, A. M. Martin