aiqbench analysis
This research presents a hardware demonstration of neural-surrogated quantum-enhanced Markov chain Monte Carlo (QeMCMC) using the IBM Quantum Heron r2 processor to accelerate Boltzmann sampling for large-scale systems. By combining QAOA samples with neural networks and a divide-and-conquer strategy, the study achieves significantly faster autocorrelation decay in systems exceeding one thousand variables, though it highlights challenges in scaling neural surrogate learning for larger blocks.
Hardware Demonstration of Neural-Surrogated Quantum-Enhanced MCMC for Large-Scale Constrained Boltzmann Sampling
arXiv:2611.05883v1 Announce Type: new Abstract: Neural-surrogated quantum-enhanced Markov chain Monte Carlo (QeMCMC) uses neural networks trained on samples from the quantum approximate optimization algorithm (QAOA) to accelerate Boltzmann sampling. A divide-and-conquer strategy extends this approach to larger systems, but independently optimizing QAOA parameters for each subproblem is costly. In this paper, we develop a hardware-efficient implementation of neural-surrogated QeMCMC and demonstrate it using an IBM Quantum Heron r2 processor. Experiments with $N=144$ binary variables reveal faster autocorrelation decay for appropriate subproblem sizes while exposing challenges in neural-surrogate learning for larger blocks. We then extend the demonstration to a system with $N=1064$, where the autocorrelation decay rate per MCMC step is approximately $5.2$ and $12.3$ times those of global and local Kawasaki dynamics, respectively. These results demonstrate faster autocorrelation decay in a system exceeding one thousand variables using samples generated on quantum hardware.
Abstract:Neural-surrogated quantum-enhanced Markov chain Monte Carlo (QeMCMC) uses neural networks trained on samples from the quantum approximate optimization algorithm (QAOA) to accelerate Boltzmann sampling. A divide-and-conquer strategy extends this approach to larger systems, but independently optimizing QAOA parameters for each subproblem is costly. In this paper, we develop a hardware-efficient implementation of neural-surrogated QeMCMC and demonstrate it using an IBM Quantum Heron r2 processor. Experiments with $N=144$ binary variables reveal faster autocorrelation decay for appropriate subproblem sizes while exposing challenges in neural-surrogate learning for larger blocks. We then extend the demonstration to a system with $N=1064$, where the autocorrelation decay rate per MCMC step is approximately $5.2$ and $12.3$ times those of global and local Kawasaki dynamics, respectively. These results demonstrate faster autocorrelation decay in a system exceeding one thousand variables using samples generated on quantum hardware.
arXiv:2611.05883 [quant-ph]
(or arXiv:2611.05883v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2611.05883 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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