n-bit anticoncentrated distributions can be generated from O(log n) qubits via a holographic protocol of interleaved random unitaries and mid-circuit measurements.
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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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Anticoncentrated $n$-bit distribution from $\log(n)$ qubits
n-bit anticoncentrated distributions can be generated from O(log n) qubits via a holographic protocol of interleaved random unitaries and mid-circuit measurements.
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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.