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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Fourier-based LCU decomposes diagonal and non-diagonal unitaries into hardware-friendly forms for QAOA-style optimization, trading circuit depth for sampling overhead with performance guarantees.
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.
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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Efficient Fourier-Based Linear Combination of Unitaries and Applications in Quantum Optimization
Fourier-based LCU decomposes diagonal and non-diagonal unitaries into hardware-friendly forms for QAOA-style optimization, trading circuit depth for sampling overhead with performance guarantees.
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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.