A coherence law based on the readout-visible aligned coherence rate (a Rayleigh quotient of the noise generator) predicts gradient survival in noisy U(1)-equivariant QNNs, with simulations confirming R²=0.979 and a special channel test showing no loss where predicted.
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B-VQE is a biorthogonal variational quantum eigensolver with exceptional-point detection and importance sampling that simulates non-Hermitian many-body models on NISQ hardware with reported energy errors below 5e-3.
A bilevel optimization method turns QAOA into a fully connected QBM that achieves 0.9559 target state probability noiseless and retains top probability under realistic noise levels.
Local tensor-train surrogates approximate quantum machine learning models via Taylor polynomials and tensor networks, delivering polynomial parameter scaling and explicit generalization bounds controlled by patch radius.
Integrating amplitude estimation into QNN readout achieves O(1/N) estimation error with one shot instead of the usual O(1/sqrt(N)) Monte Carlo scaling.
A quantum machine learning surrogate based on parameterized circuits with data re-uploading approximates the full BGK collision dynamics in LBM across all admissible relaxation parameters and is validated on Taylor-Green vortex and double shear layer benchmarks.
Hybrid quantum-classical FBPINN for acoustic FWI achieves lower L1 velocity error than classical baselines in ~8x fewer iterations with ~33% fewer parameters on anomaly and checkerboard benchmarks.
QML-PipeGuard is a framework for runtime behavioral fingerprinting of QML pipelines that absorbs benign drift while detecting adversarial channel substitution via informationally complete measurements.
Hybrid quantum-classical optimization for unit commitment uses Pauli-Correlation Encoding to solve multi-period schedules with up to 312 binary variables while satisfying load, ramping, and reserve constraints.
Fractional OAM charge ℓ=1.5 induces an optimal 67.5° GKP lattice rotation that reduces error rate 23.9× with <0.2% loss in Fisher information and yields 41% higher metrological capacity.
A compact 2-qubit QNN approximates Black-Scholes-Merton option prices with usable accuracy when executed on multiple commercial NISQ quantum processors.
A necessary condition for variational quantum circuits to reach exact ground states requires matching module projection norms between input and solution, enabling classical O(n^5) exact solvers for problems like MaxCut.
A 4-qubit QCNN classifies entanglement thresholds from fermion density profiles in the Thirring model more effectively than comparable classical CNNs.
A new QNN architecture with unified graph, HAL, and ONNX pipeline enables cross-framework and cross-hardware QML with training time within 8% of native implementations and identical accuracy on Iris, Wine, and MNIST-4 tasks.
Bayesian PSR with Gaussian processes and GradCoRe accelerates VQE SGD by reusing observations and minimizing per-step costs while reducing to standard PSR in special cases.
A variational quantum autoencoder detects anomalies in brain MRI by scoring resistance to compression, reporting slice-level ROC-AUC of 0.95 and outperforming classical autoencoders and PCA on public datasets.
Survey of quantum feature encoding families with a cost-expressivity-robustness taxonomy, closed-form NISQ bounds, and a five-regime decision framework that recommends shallow angle encodings when gate error rate p is at or above 10^-3.
Gradient-descent optimization of eight circuit parameters in a Strawberry Fields model yields CFI gains of 153% to 1775% and 8x to 133x more useful events per pulse versus Afek et al. (2010) for N=2-5, reaching 82% of Heisenberg limit at N=2 and 58% at N=5.
COM-QEL integrates conservative objective models with quantum extremal learning to produce more reliable solutions than standard QEL on offline benchmark optimization tasks.
The paper introduces Recursive QLSTM via metacore recursion, numerically tests variants on sequence lengths, and offers theoretical arguments for better temporal propagation.
Self-Modulating QFWP adds adaptive modulation to quantum fast-weight updates and memory to improve stability and performance on sequential learning tasks.
citing papers explorer
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A Coherence Law for Trainability in Noisy Equivariant Quantum Neural Networks
A coherence law based on the readout-visible aligned coherence rate (a Rayleigh quotient of the noise generator) predicts gradient survival in noisy U(1)-equivariant QNNs, with simulations confirming R²=0.979 and a special channel test showing no loss where predicted.
-
Exceptional-Point-Anchored Variational Quantum Eigensolver for Non-Hermitian Many-Body Phase Diagrams: Bridging Skin-Effect Topology and Entanglement Criticality on NISQ Hardware
B-VQE is a biorthogonal variational quantum eigensolver with exceptional-point detection and importance sampling that simulates non-Hermitian many-body models on NISQ hardware with reported energy errors below 5e-3.
-
Breaking QAOA's Fixed Target Hamiltonian Barrier: A Fully Connected Quantum Boltzmann Machine via Bilevel Optimization
A bilevel optimization method turns QAOA into a fully connected QBM that achieves 0.9559 target state probability noiseless and retains top probability under realistic noise levels.
-
Local tensor-train surrogates for quantum learning models
Local tensor-train surrogates approximate quantum machine learning models via Taylor polynomials and tensor networks, delivering polynomial parameter scaling and explicit generalization bounds controlled by patch radius.
-
Single-shot quantum neural networks with amplitude estimation
Integrating amplitude estimation into QNN readout achieves O(1/N) estimation error with one shot instead of the usual O(1/sqrt(N)) Monte Carlo scaling.
-
A Quantum-Classical Surrogate Model for the Collision Operator of the Lattice Boltzmann Method
A quantum machine learning surrogate based on parameterized circuits with data re-uploading approximates the full BGK collision dynamics in LBM across all admissible relaxation parameters and is validated on Taylor-Green vortex and double shear layer benchmarks.
-
Accelerating physics-informed neural networks for full waveform inversion using a hybrid quantum-classical finite-basis architecture
Hybrid quantum-classical FBPINN for acoustic FWI achieves lower L1 velocity error than classical baselines in ~8x fewer iterations with ~33% fewer parameters on anomaly and checkerboard benchmarks.
-
QML-PipeGuard: Drift-Aware Behavioral Fingerprinting for Quantum Machine Learning Pipeline Integrity
QML-PipeGuard is a framework for runtime behavioral fingerprinting of QML pipelines that absorbs benign drift while detecting adversarial channel substitution via informationally complete measurements.
-
Scaling Quantum Optimization for Unit Commitment via Pauli Correlation Encoding
Hybrid quantum-classical optimization for unit commitment uses Pauli-Correlation Encoding to solve multi-period schedules with up to 312 binary variables while satisfying load, ramping, and reserve constraints.
-
OAM-Induced Lattice Rotation Reveals a Fractional Optimum in Fault-Tolerant GKP Quantum Sensing
Fractional OAM charge ℓ=1.5 induces an optimal 67.5° GKP lattice rotation that reduces error rate 23.9× with <0.2% loss in Fisher information and yields 41% higher metrological capacity.
-
Option Pricing on Noisy Intermediate-Scale Quantum Computers: A Quantum Neural Network Approach
A compact 2-qubit QNN approximates Black-Scholes-Merton option prices with usable accuracy when executed on multiple commercial NISQ quantum processors.
-
Reachability Constraints in Variational Quantum Circuits: Optimization within Polynomial Group Module
A necessary condition for variational quantum circuits to reach exact ground states requires matching module projection norms between input and solution, enabling classical O(n^5) exact solvers for problems like MaxCut.
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Quantum Machine Learning for particle scattering entanglement classification
A 4-qubit QCNN classifies entanglement thresholds from fermion density profiles in the Thirring model more effectively than comparable classical CNNs.
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Eliminating Vendor Lock-In in Quantum Machine Learning via Framework-Agnostic Neural Networks
A new QNN architecture with unified graph, HAL, and ONNX pipeline enables cross-framework and cross-hardware QML with training time within 8% of native implementations and identical accuracy on Iris, Wine, and MNIST-4 tasks.
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Bayesian Parameter Shift Rule in Variational Quantum Eigensolvers
Bayesian PSR with Gaussian processes and GradCoRe accelerates VQE SGD by reusing observations and minimizing per-step costs while reducing to standard PSR in special cases.
-
Compression-Driven Anomaly Detection in Brain MRI Using an Interpretable Quantum Autoencoder
A variational quantum autoencoder detects anomalies in brain MRI by scoring resistance to compression, reporting slice-level ROC-AUC of 0.95 and outperforming classical autoencoders and PCA on public datasets.
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Feature Encoding in Quantum Machine Learning: A Survey and Practical Guidelines
Survey of quantum feature encoding families with a cost-expressivity-robustness taxonomy, closed-form NISQ bounds, and a five-regime decision framework that recommends shallow angle encodings when gate error rate p is at or above 10^-3.
-
Quantum-Enhanced Single-Parameter Phase Estimation with Adaptive NOON States
Gradient-descent optimization of eight circuit parameters in a Strawberry Fields model yields CFI gains of 153% to 1775% and 8x to 133x more useful events per pulse versus Afek et al. (2010) for N=2-5, reaching 82% of Heisenberg limit at N=2 and 58% at N=5.
-
Conservative quantum offline model-based optimization
COM-QEL integrates conservative objective models with quantum extremal learning to produce more reliable solutions than standard QEL on offline benchmark optimization tasks.
-
Recursive QLSTM with Dynamic Variational Quantum Circuit Adaptation
The paper introduces Recursive QLSTM via metacore recursion, numerically tests variants on sequence lengths, and offers theoretical arguments for better temporal propagation.
-
Self-Modulating Quantum Fast-Weight Programmers for Efficient Adaptive Sequential Learning
Self-Modulating QFWP adds adaptive modulation to quantum fast-weight updates and memory to improve stability and performance on sequential learning tasks.
- Learning PDEs for Portfolio Optimization with Quantum Physics-Informed Neural Networks