Introduces a dilation framework for quantum simulation of linear DAEs, applied to structure-preserving discretizations of unsteady Stokes flow yielding simulation cost scaling as O(h^{-2} sqrt(t)).
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Quantum machine learning.Nature, 549(7671):195–202
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CRiSP uses neural-guided MCTS and curriculum learning to insert Clifford prefixes before parameterized rotations in VQAs, yielding mean 3.17x and max 45x gains in energy accuracy on 22-qubit QAOA benchmarks versus prior Clifford initializers.
Adversaries perturbing shared entanglement in distributed VQAs can manipulate a new Kraus expressibility metric to keep gradients large but steer training to incorrect solutions.
Introduces QASM-Eval, the first dataset targeting OpenQASM-3 hardware-facing features for LLM training and evaluation, with an extended verifier for syntax, states, and timelines.
A strengthened average-case QFT verification condition that controls inter-eigenspace coherences guarantees worst-case correctness of the HHL algorithm in natural settings.
Hybrid algorithm classically diagonalizes Hamiltonian tensor factors to construct block-encodings for quantum simulation via QSVD, with extensions for commuting time-dependent cases.
LSTM trained with a QCBM generative prior beats a classical LSTM on SSE/CSI 300 realized volatility and retains much of that edge under zero-weight inference via Drop-Prior training.
On a 213-animal COPD cohort, quantum kernel ridge on four biomarkers gave the lowest muscle-weight RMSE (4.41 vs 4.70 mg for matched ridge), but the paper's abstract and results tables contradict each other on protocol, effect size, and the force endpoint.
Outlines a quantum-enhanced framework with channel-adaptive semantic communication, multimodal fusion, model transfer via quantum RL, and federated aggregation modules for 6G V2X.
A position and survey paper that identifies convergence between neuroscience, AGI, and neuromorphic computing and outlines four key integration challenges.
citing papers explorer
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Quantum Simulation of Differential-Algebraic Equations with Applications to Unsteady Stokes Flow
Introduces a dilation framework for quantum simulation of linear DAEs, applied to structure-preserving discretizations of unsteady Stokes flow yielding simulation cost scaling as O(h^{-2} sqrt(t)).
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Classical State Preparation for Variational Quantum Algorithms via Reinforcement Learning
CRiSP uses neural-guided MCTS and curriculum learning to insert Clifford prefixes before parameterized rotations in VQAs, yielding mean 3.17x and max 45x gains in energy accuracy on 22-qubit QAOA benchmarks versus prior Clifford initializers.
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Adversarial Effects on Expressibility and Trainability in Distributed Variational Quantum Algorithms
Adversaries perturbing shared entanglement in distributed VQAs can manipulate a new Kraus expressibility metric to keep gradients large but steer training to incorrect solutions.
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QASM-Eval: A Dataset to Train and Evaluate LLMs on OpenQASM-3 Beyond Quantum Circuits
Introduces QASM-Eval, the first dataset targeting OpenQASM-3 hardware-facing features for LLM training and evaluation, with an extended verifier for syntax, states, and timelines.
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Worst-case Harrow-Hassidim-Lloyd algorithm with average-case correct quantum Fourier transform
A strengthened average-case QFT verification condition that controls inter-eigenspace coherences guarantees worst-case correctness of the HHL algorithm in natural settings.
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Hybrid Quantum-Classical Algorithm for Hamiltonian Simulation
Hybrid algorithm classically diagonalizes Hamiltonian tensor factors to construct block-encodings for quantum simulation via QSVD, with extensions for commuting time-dependent cases.
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A Hybrid Quantum Circuit Born Machine Framework for Financial Volatility Forecasting: Quantum-Assisted Training and Classical Inference
LSTM trained with a QCBM generative prior beats a classical LSTM on SSE/CSI 300 realized volatility and retains much of that edge under zero-weight inference via Drop-Prior training.
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Geometric and Quantum Kernel Methods for Predicting Skeletal Muscle Outcomes in chronic obstructive pulmonary disease
On a 213-animal COPD cohort, quantum kernel ridge on four biomarkers gave the lowest muscle-weight RMSE (4.41 vs 4.70 mg for matched ridge), but the paper's abstract and results tables contradict each other on protocol, effect size, and the force endpoint.
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Quantum Machine Learning-based 6G edge Network: Enabling Adaptive Communication and Model Aggregation
Outlines a quantum-enhanced framework with channel-adaptive semantic communication, multimodal fusion, model transfer via quantum RL, and federated aggregation modules for 6G V2X.
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Bridging Brains and Machines: A Unified Frontier in Neuroscience, Artificial Intelligence, and Neuromorphic Systems
A position and survey paper that identifies convergence between neuroscience, AGI, and neuromorphic computing and outlines four key integration challenges.