Quantum Optimisation of Complex Systems with a Quantum Annealer
Not on the map. Why?
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
Built on by
Citing papers per year: 2022: 3; 2023: 3; 2024: 0; 2025: 0; 2026: 1.
7 papers.
- 2023Q-Seg: Quantum Annealing-Based Unsupervised Image Segmentationcited by 11
- 2022Classical versus Quantum: comparing Tensor Network-based Quantum Circuits on LHC datacited by 7
- 2022Completely Quantum Neural Networkscited by 7
- 2023mathbbZ2× mathbbZ2 Equivariant Quantum Neural Networks: Benchmarking against Classical Neural Networkscited by 4
- 2022Qade: Solving Differential Equations on Quantum Annealerscited by 1
- 2023Training Neural Networks with Universal Adiabatic Quantum Computingcited by 1
- 2026Generalization of Transformer-Based Neural Quantum States via In-Context Learningcited by 0
- 2026Generalization of Transformer-Based Neural Quantum States via In-Context Learning2 Oct 2026
- 2023mathbbZ2× mathbbZ2 Equivariant Quantum Neural Networks: Benchmarking against Classical Neural Networks30 Nov 2023
- 2023Q-Seg: Quantum Annealing-Based Unsupervised Image Segmentation21 Nov 2023
- 2023Training Neural Networks with Universal Adiabatic Quantum Computing24 Aug 2023
- 2022Qade: Solving Differential Equations on Quantum Annealers7 Apr 2022
- 2022Completely Quantum Neural Networks23 Feb 2022
- 2022Classical versus Quantum: comparing Tensor Network-based Quantum Circuits on LHC data21 Feb 2022