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.
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MPS energy landscapes lack poor local minima because gauge freedom induces effective local overparametrization, proven via invariance under orthogonality center moves and confirmed by numerics on random Hamiltonians.
A programmable silicon photonic chip excited with single photons implements quantum reservoir computing for quantum state tomography, entanglement measurement via negativity, and classical tasks, with an imperfection mitigation technique that improves accuracy over the classical regime.
Zero-noise extrapolation has a finite-shot help-harm boundary below which it increases local mean-squared error due to variance penalties outweighing bias reduction.
AI coding agents evolve simple ground-state protocols into improved versions for VQE, DMRG, and AFQMC on spin models and molecules by using executable energy scores under fixed compute budgets.
QRSI spans degenerate quantum eigenspaces almost surely by conjugating the Hamiltonian with random unitaries on g parallel branches and using subspace estimation, while exactly preserving the spectral gap.
Quantum PINNs using tensor-rank polynomials solve the Merton portfolio optimization PDE more accurately and with far fewer parameters than classical neural networks.
Fragment classification is efficiently learnable by quantum neural networks under suitable conditions but resists known classical dequantization techniques.
Quantum circuits for coherent multilayer neural network inference achieve quadratic to polylogarithmic speedups over classical methods depending on quantum data access models for inputs and weights.
A layer-by-layer classical variational disentanglement algorithm compiles preparation circuits for matrix product states by minimizing bipartite entanglement to reduce bond dimensions.
Classical RNNs trained on small instances provide parameter initializations for QAOA and VQE that reduce total optimization iterations and generalize across problem sizes.
The work constructs a permutation-equivariant quantum GNN that implements message passing at selectable Weisfeiler-Leman levels, supports pre-training on small graphs, and demonstrates readout scalability with simulations up to 56 qubits on synthetic, molecular, and TSP datasets.
QC-AFQMC per-step scaling reduced from O(N^5.5) to O(N^4.5) via Aitken's block transformation for singular Pfaffians and algorithmic differentiation for force bias, with demonstrations on H8 from real quantum data and Li2O4.
Coupling-Grouped XY-QAOA enables joint anomaly-feature selection via a constraint-preserving grouped-angle QAOA variant, achieving 45.9-61.3% circuit depth reduction and larger feasible executions (64 qubits at p=2) on IBM Heron hardware compared to standard approaches.
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.
Independent quantum signal injection into graph DEQs yields higher test accuracy and fewer solver iterations than state-dependent or backbone-dependent injection and classical equilibrium models on NCI1, PROTEINS, and MUTAG benchmarks.
QTL unifies expectation-value minimization with CVaR and Gibbs heuristics under one tunable operator, amplifying gradients in structured cases while preserving global minima and shifting the bottleneck to measurement variance.
Structure-aware VQE ansatze for long-range Ising models cut required circuit layers by 2.5x to 3.8x in non-local regimes while two-qubit gate counts scale quadratically with system size, consistent with the number of Hamiltonian terms.
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.
Absence of simple slow operators implies that typical low-complexity states thermalize in quantum systems.
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.
A single-ancilla Power-Cosine QSP filter on time-evolution operators achieves deterministic many-body ground state preparation with exponential excited-state suppression and O(Δ^{-2} log(1/ε)) depth scaling.
VarQEC uses a distinguishability loss as a machine-learning objective to variationally discover resource-efficient encoding circuits optimized for given noise models.
Conic extensions of parameterized quantum circuits enable jumps from barren plateaus to fertile valleys via non-unitary operations and ancilla, reducing optimal jump selection to a generalized eigenvalue problem and improving QAOA sampling in simulations.
citing papers explorer
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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.
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Absence of poor local minima in matrix product states
MPS energy landscapes lack poor local minima because gauge freedom induces effective local overparametrization, proven via invariance under orthogonality center moves and confirmed by numerics on random Hamiltonians.
-
Quantum and classical processing with photonic quantum machine learning
A programmable silicon photonic chip excited with single photons implements quantum reservoir computing for quantum state tomography, entanglement measurement via negativity, and classical tasks, with an imperfection mitigation technique that improves accuracy over the classical regime.
-
The finite-shot help-harm boundary of zero-noise extrapolation
Zero-noise extrapolation has a finite-shot help-harm boundary below which it increases local mean-squared error due to variance penalties outweighing bias reduction.
-
Optimizing ground state preparation protocols with autoresearch
AI coding agents evolve simple ground-state protocols into improved versions for VQE, DMRG, and AFQMC on spin models and molecules by using executable energy scores under fixed compute budgets.
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Quantum Randomized Subspace Iteration
QRSI spans degenerate quantum eigenspaces almost surely by conjugating the Hamiltonian with random unitaries on g parallel branches and using subspace estimation, while exactly preserving the spectral gap.
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Learning PDEs for Portfolio Optimization with Quantum Physics-Informed Neural Networks
Quantum PINNs using tensor-rank polynomials solve the Merton portfolio optimization PDE more accurately and with far fewer parameters than classical neural networks.
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Fragmentation is Efficiently Learnable by Quantum Neural Networks
Fragment classification is efficiently learnable by quantum neural networks under suitable conditions but resists known classical dequantization techniques.
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Accelerating Inference for Multilayer Neural Networks with Quantum Computers
Quantum circuits for coherent multilayer neural network inference achieve quadratic to polylogarithmic speedups over classical methods depending on quantum data access models for inputs and weights.
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Preparation Circuits for Matrix Product States by Classical Variational Disentanglement
A layer-by-layer classical variational disentanglement algorithm compiles preparation circuits for matrix product states by minimizing bipartite entanglement to reduce bond dimensions.
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Learning to learn with quantum neural networks via classical neural networks
Classical RNNs trained on small instances provide parameter initializations for QAOA and VQE that reduce total optimization iterations and generalize across problem sizes.
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Scalable Message-Passing Quantum Graph Neural Networks in the Weisfeiler-Leman Hierarchy
The work constructs a permutation-equivariant quantum GNN that implements message passing at selectable Weisfeiler-Leman levels, supports pre-training on small graphs, and demonstrates readout scalability with simulations up to 56 qubits on synthetic, molecular, and TSP datasets.
-
Quantum-Classical Auxiliary-Field Quantum Monte Carlo at the Edge of Practicability
QC-AFQMC per-step scaling reduced from O(N^5.5) to O(N^4.5) via Aitken's block transformation for singular Pfaffians and algorithmic differentiation for force bias, with demonstrations on H8 from real quantum data and Li2O4.
-
Coupling-Grouped XY-QAOA for Joint Anomaly-Feature Selection
Coupling-Grouped XY-QAOA enables joint anomaly-feature selection via a constraint-preserving grouped-angle QAOA variant, achieving 45.9-61.3% circuit depth reduction and larger feasible executions (64 qubits at p=2) on IBM Heron hardware compared to standard approaches.
-
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.
-
Quantum Injection Pathways for Implicit Graph Neural Networks
Independent quantum signal injection into graph DEQs yields higher test accuracy and fewer solver iterations than state-dependent or backbone-dependent injection and classical equilibrium models on NCI1, PROTEINS, and MUTAG benchmarks.
-
Quantum Tilted Loss in Variational Optimization: Theory and Applications
QTL unifies expectation-value minimization with CVaR and Gibbs heuristics under one tunable operator, amplifying gradients in structured cases while preserving global minima and shifting the bottleneck to measurement variance.
-
Scaling of Quantum Resources for Simulating a Long-Range System
Structure-aware VQE ansatze for long-range Ising models cut required circuit layers by 2.5x to 3.8x in non-local regimes while two-qubit gate counts scale quadratically with system size, consistent with the number of Hamiltonian terms.
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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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Simple slow operators and quantum thermalization
Absence of simple slow operators implies that typical low-complexity states thermalize in quantum systems.
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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.
-
Deterministic Ground State Preparation via Power-Cosine Filtering of Time Evolution Operators
A single-ancilla Power-Cosine QSP filter on time-evolution operators achieves deterministic many-body ground state preparation with exponential excited-state suppression and O(Δ^{-2} log(1/ε)) depth scaling.
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Learning Encodings by Maximizing State Distinguishability: Variational Quantum Error Correction
VarQEC uses a distinguishability loss as a machine-learning objective to variationally discover resource-efficient encoding circuits optimized for given noise models.
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From barren plateaus through fertile valleys: Conic extensions of parameterised quantum circuits
Conic extensions of parameterized quantum circuits enable jumps from barren plateaus to fertile valleys via non-unitary operations and ancilla, reducing optimal jump selection to a generalized eigenvalue problem and improving QAOA sampling in simulations.
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JGRA: Jacobian Geometry Robustness Assessment in NISQ Noise-Aware Quantum Neural Networks
JGRA framework extracts geometric descriptors from noise-conditioned Jacobians in QNNs after entropy-matched calibration and noise-aware training, and empirically shows these descriptors predict robustness under unseen noise.
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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.
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Expressibility, Noise, and Error Mitigation in VQE Ansatz Selection
Error mitigation does not restore expressibility as a reliable predictor of VQE performance under noise, while simple circuit topology metrics like two-qubit gate count predict PEC degradation better in tested cases.
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Hybrid quantum-classical physics-informed neural networks for solving nonlinear PDEs: when and where hybridization is effective?
HQPINNs reduce relative L2 error by roughly fourfold on Burgers' equation and fivefold on Allen-Cahn equation versus classical PINNs, with smoother training and largest gains in stiff regimes.
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Quantum Algorithm for Distributed Reduction of Entanglements (QADR): A Trainable and Simulation-Efficient QML Framework
QADR decomposes n-qubit VQCs into local sub-circuits to reduce memory from O(2^n) to O(n * 2^{2d+1}) and mitigate barren plateaus, scaling to 2000 features on MNIST and wind turbine diagnostics while matching classical models.
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Wavelet Variance Equipartition as a Threshold for World-Model Quality and Quantum Kernel TN-Simulability
Wavelet scaling α = 1/2 separates classically simulable area-law from volume-law phases for quantum kernels in world-model latents, with empirical VideoMAE latents and a Θ(d^{-2}) variance bound implying simulation hardness and quadratic measurement costs.
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A Resource-Efficient Variational Quantum Framework for the Traveling Salesman Problem
Compact binary-register encoding and divide-and-conquer execution enable high-success variational quantum solutions to small TSP instances with reduced qubit overhead.
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Swap Network Augmented Ans\"atze on Arbitrary Connectivity
By augmenting quantum circuit ansatze with optimized swap networks, the work achieves better performance in ground-state energy calculations using fewer resources on devices with arbitrary qubit connectivity.
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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.
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Noise and Configuration Recovery Impact on Quantum Selected Configuration Interaction
Noise in LUCJ sampling for QSCI on N2 expands the configuration space beyond the ideal ansatz and, when paired with recovery, produces more accurate CI energies than noiseless sampling.
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Entanglement is Half the Story: Post-Selection vs. Partial Traces
A hybrid tensor network framework interpolates between classical and quantum models via controllable post-selection, with a trainable hyperparameter that complements bond dimension to enhance quantum machine learning.
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Quantum Machine Learning for Colorectal Cancer Data: Anastomotic Leak Classification and Risk Factors
Quantum neural networks achieve 83.3% sensitivity for anastomotic leak classification versus 66.7% for classical baselines on 14% prevalence clinical data.
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Benchmarking Swarm Optimization Algorithms for Parameter Initialization in the Quantum Approximate Optimization Algorithm
Swarm methods such as PSO, FIPSO, and QPSO yield lower approximation gaps and more stable convergence than Adam, COBYLA, or SPSA when tuning QAOA parameters on weighted MaxCut instances, especially under noise and limited shots.
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Quantum Optimization Algorithms for Strongly Correlated Many-Body Systems
Perspective review comparing variational and feedback quantum algorithms for simulating phase transitions in quantum many-body systems, highlighting barren plateaus and advocating physics-informed hybridization.