mlsanityData to 14 Oct 2026

Quantum Approximate Optimization Algorithm applied to the binary perceptron

arXiv 2112.10219 ↗First posted 19 Dec 2021quant-ph cond-mat.dis-nn cs.LGcited by 3

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Abstract (arXiv)

We apply digitized Quantum Annealing (QA) and Quantum Approximate Optimization Algorithm (QAOA) to a paradigmatic task of supervised learning in artificial neural networks: the optimization of synaptic weights for the binary perceptron. At variance with the usual QAOA applications to MaxCut, or to quantum spin-chains ground state preparation, the classical Hamiltonian is characterized by highly non-local multi-spin interactions. Yet, we provide evidence for the existence of optimal smooth solutions for the QAOA parameters, which are transferable among typical instances of the same problem, and we prove numerically an enhanced performance of QAOA over traditional QA. We also investigate on the role of the QAOA optimization landscape geometry in this problem, showing that the detrimental effect of a gap-closing transition encountered in QA is also negatively affecting the performance of our implementation of QAOA.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 12 of its 67 references that are in the map. First posted on arXiv 19 Dec 2021.

Builds on

12 of 67 references.

References · 12

  1. 2020Variational Quantum Algorithmscited by 692
  2. 2018Barren plateaus in quantum neural network training landscapescited by 630
  3. 2019Parameterized quantum circuits as machine learning modelscited by 332
  4. 2016Quantum Machine Learningcited by 326
  5. 2013Ising formulations of many NP problemscited by 207
  6. 2021Variational Neural Annealingcited by 47
  7. 2016Unreasonable Effectiveness of Learning Neural Networks: From Accessible States and Robust Ensembles to Basic Algorithmic Schemescited by 40
  8. 2017Inverse statistical problems: from the inverse Ising problem to data sciencecited by 36
  9. 2021Learning through atypical "phase transitions" in overparameterized neural networkscited by 21
  10. 2014Simulating a perceptron on a quantum computercited by 16
  11. 2021Layer VQE: A Variational Approach for Combinatorial Optimization on Noisy Quantum Computerscited by 14
  12. 2017Efficiency of quantum versus classical annealing in non-convex learning problemscited by 8

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Cited by year

Citing papers per year: 2022: 1; 2023: 2.

3 papers.

  1. 2023Quantum HyperNetworks: Training Binary Neural Networks in Quantum Superpositioncited by 8
  2. 2022Quark: A Gradient-Free Quantum Learning Framework for Classification Taskscited by 4
  3. 2023Physics-Informed Neural Networks for an optimal counterdiabatic quantum computationcited by 0