mlsanityData to 7 Oct 2026

Quantum approximate optimization via learning-based adaptive optimization

arXiv 2303.14877 ↗First posted 27 Mar 2023quant-ph cs.LGcited by 4

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Abstract (arXiv)

Combinatorial optimization problems are ubiquitous and computationally hard to solve in general. Quantum approximate optimization algorithm (QAOA), one of the most representative quantum-classical hybrid algorithms, is designed to solve combinatorial optimization problems by transforming the discrete optimization problem into a classical optimization problem over continuous circuit parameters. QAOA objective landscape is notorious for pervasive local minima, and its viability significantly relies on the efficacy of the classical optimizer. In this work, we design double adaptive-region Bayesian optimization (DARBO) for QAOA. Our numerical results demonstrate that the algorithm greatly outperforms conventional optimizers in terms of speed, accuracy, and stability. We also address the issues of measurement efficiency and the suppression of quantum noise by conducting the full optimization loop on a superconducting quantum processor as a proof of concept. This work helps to unlock the full power of QAOA and paves the way toward achieving quantum advantage in practical classical tasks.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 31 of its 80 references that are in the map. First posted on arXiv 27 Mar 2023.

Builds on

31 of 80 references.

References · 31

  1. 2018A Tutorial on Bayesian Optimizationcited by 734
  2. 2018Barren plateaus in quantum neural network training landscapescited by 624
  3. 2018GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Accelerationcited by 603
  4. 2019Scalable Global Optimization via Local Bayesian Optimizationcited by 359
  5. 2020Noise-Induced Barren Plateaus in Variational Quantum Algorithmscited by 192
  6. 2021High-Dimensional Bayesian Optimization with Sparse Axis-Aligned Subspacescited by 139
  7. 2014Bayesian Optimization with Unknown Constraintscited by 109
  8. 2021Combinatorial Optimization with Physics-Inspired Graph Neural Networkscited by 107
  9. 2017Constrained Bayesian Optimization with Noisy Experimentscited by 103
  10. 2020Entanglement Induced Barren Plateauscited by 101
  11. 2021Theory of overparametrization in quantum neural networkscited by 100
  12. 2020Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimizationcited by 89
19 more
  1. 2020Effect of barren plateaus on gradient-free optimizationcited by 79
  2. 2019Bayesian Optimization for Materials Design with Mixed Quantitative and Qualitative Variablescited by 64
  3. 2019Learning to learn with quantum neural networks via classical neural networkscited by 55
  4. 2021Variational Neural Annealingcited by 45
  5. 2022Robust Multi-Objective Bayesian Optimization Under Input Noisecited by 36
  6. 2022Modern graph neural networks do worse than classical greedy algorithms in solving combinatorial optimization problems like maximum independent setcited by 33
  7. 2019Learning to Optimize Variational Quantum Circuits to Solve Combinatorial Problemscited by 30
  8. 2017Practical Bayesian optimization in the presence of outlierscited by 25
  9. 2020Noisy-Input Entropy Search for Efficient Robust Bayesian Optimizationcited by 24
  10. 2022Inability of a graph neural network heuristic to outperform greedy algorithms in solving combinatorial optimization problems like Max-Cutcited by 19
  11. 2021Autonomous optimization of nonaqueous battery electrolytes via robotic experimentation and machine learningcited by 16
  12. 2021Graph neural network initialisation of quantum approximate optimisationcited by 13
  13. 2020Universal Effectiveness of High-Depth Circuits in Variational Eigenproblemscited by 10
  14. 2021Stochastic Gradient Line Bayesian Optimization for Efficient Noise-Robust Optimization of Parameterized Quantum Circuitscited by 7
  15. 2021Training Saturation in Layerwise Quantum Approximate Optimisationcited by 6
  16. 2022ODBO: Bayesian Optimization with Search Space Prescreening for Directed Protein Evolutioncited by 5
  17. 2020Accelerating Quantum Approximate Optimization Algorithm using Machine Learningcited by 4
  18. 2020Bridging Classical and Quantum with SDP initialized warm-starts for QAOAcited by 4
  19. 2023Quantum Annealing vs. QAOA: 127 Qubit Higher-Order Ising Problems on NISQ Computerscited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 1; 2026: 1.

4 papers.

  1. 2024Reinforcement learning with learned gadgets to tackle hard quantum problems on real hardwarecited by 7
  2. 2024Quantum-machine-assisted Drug Discoverycited by 0
  3. 2025Generalized Probabilistic Approximate Optimization Algorithmcited by 0
  4. 2026Quantum Gaussian processes for prediction of channel observationscited by 0