Optimizing quantum heuristics with meta-learning
Open accessAbstract
Variational quantum algorithms, a class of quantum heuristics, are promising candidates for the demonstration of useful quantum computation. Finding the best way to amplify the performance of these methods on hardware is an important task. Here, we evaluate the optimization of quantum heuristics with an existing class of techniques called “meta-learners.” We compare the performance of a meta-learner to evolutionary strategies, L-BFGS-B and Nelder-Mead approaches, for two quantum heuristics (quantum alternating operator ansatz and variational quantum eigensolver), on three problems, in three simulation environments. We show that the meta-learner comes near to the global optima more frequently than all other optimizers we tested in a noisy parameter setting environment. We also find that the meta-learner is generally more resistant to noise, for example, seeing a smaller reduction in performance in Noisy and Sampling environments, and performs better on average by a “gain” metric than its closest comparable competitor L-BFGS-B. Finally, we present evidence that indicates the meta-learner trained on small problems will generalize to larger problems. These results are an important indication that meta-learning and associated machine learning methods will be integral to the useful application of noisy near-term quantum computers.
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The experimental results of machine learning approaches for parameter optimization suggest a high degree of transferability and robustness to noise as well as an increased solution quality.
“The experimental results suggest a high degree of transferability and robustness to noise as well as an increased solution quality.”
From Quantum Combinatorial Optimization in the NISQ Era: A Systematic Mapping Study (2024) · cites this paper as [143]
Section 5.2 reports that the meta-learner was more resilient to simulated noise than other optimizers, and Section 5.1 reports competitive solution quality on small instances. Section 5.5 offers limited evidence of transfer from smaller to larger problems, but explicitly says the demonstration is not extensive enough for serious conclusions; the claimed high degree of transferability overstates that finding. The noise results concern simulations, not quantum hardware. (LLM, conf=0.84)
Experimental — accuracy not yet measuredMethods to conduct classical parameter optimization of a VQA beyond standard classical optimizers comprise machine learning approaches and sequential minimal optimization.
“Methods to conduct the classical parameter optimization of a VQA that go beyond the standard classical optimizers (cf. [166]) comprise machine learning approaches [51, 75, 76, 141, 143, 146] and sequential minimal optimization [99].”
From Quantum Combinatorial Optimization in the NISQ Era: A Systematic Mapping Study (2024) · cites this paper as [143]
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BackgroundMethodInfluential
- Authors
- Wilson, M., Stromswold, R., Wudarski, F., Hadfield, S., Tubman, N. M., & Rieffel, E. G.
- Affiliations
- Ames Research Center (US)
- Corresponding author
- Wilson, Max
- Venue
- Quantum Machine Intelligence
- Published
- 2021-04-13
- Volume
- 3
- Issue
- 1
- ISSN
- 2524-4906 (ISSN-L)
- 2524-4914
- OpenAlex
- W2965090307
- Cited by
- 79
- References
- 72
- Citations by year
- 2026: 7 · 2025: 13 · 2024: 10 · 2023: 14 · 2022: 14 · 2021: 9 · 2020: 12
- FWCI
- 4.12
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Quantum Machine Intelligence (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- AFRL (F4HBKC4162G001)
- office of the director of national intelligence (IAA 145483)
- NASA Academic Mission Services (NNA16BD14C.)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum many-body systems
- Keywords
- meta-learning, variational quantum algorithms, variational quantum eigensolver, Quantum Alternating Operator Ansatz, optimizer comparison, noisy quantum hardware, noise robustness, L-BFGS-B, Nelder-Mead
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