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)).
Quantum machine learning.Nature, 549(7671):195–202, 2017
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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.
HHL algorithm achieves provably good worst-case performance assuming only average-case correct QFT, via a strengthened Linden-de Wolf protocol applied across three scenarios.
Hybrid algorithm classically diagonalizes Hamiltonian tensor factors to construct block-encodings for quantum simulation via QSVD, with extensions for commuting time-dependent cases.
Hybrid LSTM-QCBM model outperforms classical LSTM on SSE Composite and CSI 300 volatility forecasting and supports quantum-assisted training followed by fully classical inference.
Quantum-kernel ridge regression with four inputs achieved R² 0.62 and RMSE 4.41 mg for tibialis anterior muscle weight, outperforming a matched classical baseline at R² 0.56.
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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Worst-case Harrow-Hassidim-Lloyd algorithm with average-case correct quantum Fourier transform
HHL algorithm achieves provably good worst-case performance assuming only average-case correct QFT, via a strengthened Linden-de Wolf protocol applied across three scenarios.
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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-Classical Framework for Financial Volatility Forecasting Based on Quantum Circuit Born Machines
Hybrid LSTM-QCBM model outperforms classical LSTM on SSE Composite and CSI 300 volatility forecasting and supports quantum-assisted training followed by fully classical inference.
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Geometric and Quantum Kernel Methods for Predicting Skeletal Muscle Outcomes in chronic obstructive pulmonary disease
Quantum-kernel ridge regression with four inputs achieved R² 0.62 and RMSE 4.41 mg for tibialis anterior muscle weight, outperforming a matched classical baseline at R² 0.56.
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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.