Divide-and-conquer QAOA samples and Hamming-weight-conditioned neural network surrogates accelerate MCMC mixing for constrained Ising problems by average factors of 20.3 and 7.6 over classical pair-flip baselines.
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The authors introduce MuTA as a universal quantum neural network for MBQC and numerically demonstrate its ability to learn gates, classify quantum states, and process data under noise, including photonic hardware constraints.
QUEST is a new adaptive framework for quantum state engineering that constructs states one Pauli rotation at a time to satisfy multiple expectation-value targets simultaneously.
Variational compression of Trotterized circuits preserves reaction rate coefficients in nonadiabatic dynamics simulations while reducing circuit depth.
citing papers explorer
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Divide-and-Conquer Neural Network Surrogates for Quantum Sampling: Accelerating Markov Chain Monte Carlo in Large-Scale Constrained Optimization Problems
Divide-and-conquer QAOA samples and Hamming-weight-conditioned neural network surrogates accelerate MCMC mixing for constrained Ising problems by average factors of 20.3 and 7.6 over classical pair-flip baselines.
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Measurement-based quantum machine learning
The authors introduce MuTA as a universal quantum neural network for MBQC and numerically demonstrate its ability to learn gates, classify quantum states, and process data under noise, including photonic hardware constraints.
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Quantum State Engineering Under Multiple Expectation-Value Constraints
QUEST is a new adaptive framework for quantum state engineering that constructs states one Pauli rotation at a time to satisfy multiple expectation-value targets simultaneously.
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Variationally Compressing Quantum Circuits to Approximate Nonadiabatic Molecular Quantum Dynamics
Variational compression of Trotterized circuits preserves reaction rate coefficients in nonadiabatic dynamics simulations while reducing circuit depth.