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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5 Pith papers cite this work. Polarity classification is still indexing.
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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.
A divide-and-conquer framework using QAOA and neural network surrogates accelerates constrained MCMC by factors of 7.6 to 20.3 over classical methods.
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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Forecasts of CMB $E$-mode anomalies for AliCPT-1
A divide-and-conquer framework using QAOA and neural network surrogates accelerates constrained MCMC by factors of 7.6 to 20.3 over classical methods.
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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.