mlsanityData to 14 Oct 2026

Quantum Optimisation of Complex Systems with a Quantum Annealer

arXiv 2105.13945 ↗First posted 28 May 2021quant-ph cond-mat.stat-mech cs.LGcited by 7

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Abstract (arXiv)

We perform an in-depth comparison of quantum annealing with several classical optimisation techniques, namely thermal annealing, Nelder-Mead, and gradient descent. We begin with a direct study of the 2D Ising model on a quantum annealer, and compare its properties directly with those of the thermal 2D Ising model. These properties include an Ising-like phase transition that can be induced by either a change in 'quantum-ness' of the theory, or by a scaling the Ising couplings up or down. This behaviour is in accord with what is expected from the physical understanding of the quantum system. We then go on to demonstrate the efficacy of the quantum annealer at minimising several increasingly hard two dimensional potentials. For all the potentials we find the general behaviour that Nelder-Mead and gradient descent methods are very susceptible to becoming trapped in false minima, while the thermal anneal method is somewhat better at discovering the true minimum. However, and despite current limitations on its size, the quantum annealer performs a minimisation very markedly better than any of these classical techniques. A quantum anneal can be designed so that the system almost never gets trapped in a false minimum, and rapidly and successfully minimises the potentials.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 1 of its 61 references that are in the map. First posted on arXiv 28 May 2021.

Builds on

1 of 61 references.

References · 1

  1. 2021Elvet -- a neural network-based differential equation and variational problem solvercited by 7

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 3; 2023: 3; 2024: 0; 2025: 0; 2026: 1.

7 papers.