1157 papers
This paper demonstrates the generation of 12.1 ± 0.2 dB squeezed light from a broadband waveguide optical parametric amplifier by employing a machine-learning-optimized spatial light modulator in the local oscillator path to minimize spatial mode mismatch loss.
This paper proposes a memory-efficient classical simulation method for quantum machine learning that fuses consecutive gates in both forward and backward paths to minimize global memory access, achieving up to 30 times higher throughput and enabling the training of large-scale deep quantum circuits on consumer GPUs.
This paper introduces merged amplitude encoding for Chebyshev quantum Kolmogorov–Arnold networks, a technique that reduces circuit executions by a factor of at the cost of only 1–2 additional qubits, and provides empirical evidence that this resource-efficient encoding preserves trainability comparable to the original sequential baseline under various simulation conditions.
This study demonstrates that quantum phase transitions significantly enhance the charging power of non-integrable quantum batteries based on the Axial Next-Nearest-Neighbor Ising model, contrasting with integrable cases where such effects are absent at short timescales and providing insights for the design of practical many-qubit energy storage devices.
This paper proposes ParaQuanNet, a parallel quantum neural network framework that efficiently identifies quantum generative circuits by classifying their output data with high accuracy and robustness through novel parallel quantum embedding units and mutual unbiased measurements.
This paper presents Q2NS, a modular and extensible quantum network simulator built on ns-3 that seamlessly integrates quantum and classical primitives with interchangeable state representation backends, demonstrating superior computational efficiency and visualization capabilities compared to state-of-the-art alternatives.
This paper investigates a discrete-modulation continuous-variable quantum key distribution protocol employing probabilistic amplitude shaping with QAM over a linear quantum channel, demonstrating that it closely approaches the performance of the Gaussian-modulated GG02 benchmark in terms of secure key rates and distance while overcoming practical implementation difficulties.
This paper introduces a physics-informed neural network framework that enables the unsupervised learning of effective Hamiltonian parameters directly from experimental transport data in solid-state quantum simulators, utilizing an autoencoder architecture with a physics-decoder to ensure physically meaningful representations, robust generalization, and noise resilience.
This paper presents a full-stack methodology and experimental implementation of quantum oblivious transfer and quantum tokens on the same hardware used for quantum key distribution, demonstrating the feasibility of transitioning from single-purpose networks to versatile, multi-purpose quantum communication systems.
This study integrates Rydberg-atom-based broadband sensing with a 1D ResNet deep learning model to construct spectral fingerprints of partial discharge signals, achieving robust recognition and early warning capabilities for electrical insulation diagnostics without manual feature engineering.
This paper proposes two complementary correction schemes involving potential operator modification and initial wavefunction correction to mitigate discretisation errors in grid-based Coulombic dynamics, significantly improving simulation accuracy and fidelity on both classical and quantum platforms.
This paper proposes an iterative QUBO-based framework for training CNN classifier heads via quantum annealing that freezes convolutional filters and utilizes a convex quadratic surrogate to optimize the fully connected layer, achieving competitive image classification accuracy without gradient-based circuit optimization.
This paper demonstrates within the exact factorisation framework that non-adiabatic electron-nuclear correlations induce electronic de-orthogonalisation, dynamically generating interference in the nuclear density even when starting from a superposition of orthogonal electronic states.
This paper introduces QAOA-Predictor, a Graph Neural Network model that accurately forecasts the success probabilities and minimal layer depths for Linear Ramp QAOA across diverse combinatorial optimization problems, enabling efficient fixed-parameter optimization without the need for costly runtime parameter tuning.
This paper presents an analytical quantum full-wave solution for single- and two-photon transport through a superconducting cavity-qubit system interfaced with coaxial ports, providing benchmark results to validate numerical solvers for modeling quantum interconnects.
This paper reports the experimental observation of motion-induced directionality in the collective emission of atoms confined within a non-chiral waveguide, demonstrating that thermal atomic motion can induce effective asymmetry to achieve controllable non-reciprocal interactions without chiral coupling.
This paper presents QFlowNet, a novel framework integrating Generative Flow Networks and Transformers to enable fast, diverse, and efficient exact unitary synthesis by learning a general policy from sparse terminal rewards.
This study employs transmon-based quantum processors to simulate nitrogen-vacancy (NV) center interactions with spin impurities, utilizing quantum state tomography to characterize coherence dynamics and confirm entanglement generation across different coupling regimes.
By quantifying spatial noise correlations in a five-qubit silicon spin array, this work demonstrates that while global magnetic drifts require mitigation, short-range charge noise correlations are compatible with fault-tolerant quantum error correction, establishing quantitative benchmarks for scalable silicon qubits.
This paper demonstrates that the two-dimensional Rokhsar-Kivelson lattice gauge model intrinsically hosts exact stabilizer eigenstates known as sublattice scars, establishing a direct link between lattice gauge constraints, quantum many-body scarring, and stabilizer quantum information theory.