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This research presents an efficient Monte Carlo sampling technique for simulating quantum annealing on 3D lattices, addressing computational bottlenecks in tensor network methods. By adapting the Metropolis-Hastings algorithm, the study enables faster evaluation of residual energy and longer annealing times to test the Kibble-Zurek mechanism in finite systems. The approach offers a practical trade-off between speed and accuracy that is critical for scaling quantum simulations on hardware like D-Wave Advantage.
Monte Carlo sampling helps model quantum annealing on 3D lattices

Quantum annealing in the three dimensional random Ising model is simulated using a 3D tensor network, represented as a projected entangled-pair state (PEPS). Monte Carlo sampling is introduced as a more efficient approach for finite lattices with open boundaries, evaluating the residual energy as a function of annealing time.
Jacek Dziarmaga of Jagiellonian University is simulating quantum annealing on a three-dimensional cubic lattice, moving from a paramagnetic to a spin-glass phase using a tensor network approach. The work tests the Kibble-Zurek power law, a mechanism originally proposed to describe cosmological phase transitions, by evaluating residual energy across a range of annealing times. Researchers introduce a new Monte Carlo sampling approach for finite lattices, complementing a deterministic method used for infinite lattices, as the residual energy consistently approaches the KZ power law with increased annealing time. This simulation offers new opportunities to explore the quantum version of the Kibble-Zurek mechanism in higher dimensions. A new Monte Carlo sampling approach accelerates the simulation of quantum annealing on three-dimensional lattices, offering a significant efficiency gain over deterministic methods when evaluating energy in finite systems. This advancement addresses a key bottleneck in tensor-network simulations, allowing for the exploration of…
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