mlsanityData to 7 Oct 2026

Toward Automated Quantum Variational Machine Learning

arXiv 2312.01567 ↗First posted 4 Dec 2023cs.LG cs.ET quant-phcited by 3

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Abstract (arXiv)

In this work, we address the problem of automating quantum variational machine learning. We develop a multi-locality parallelizable search algorithm, called MUSE, to find the initial points and the sets of parameters that achieve the best performance for quantum variational circuit learning. Simulations with five real-world classification datasets indicate that on average, MUSE improves the detection accuracy of quantum variational classifiers 2.3 times with respect to the observed lowest scores. Moreover, when applied to two real-world regression datasets, MUSE improves the quality of the predictions from negative coefficients of determination to positive ones. Furthermore, the classification and regression scores of the quantum variational models trained with MUSE are on par with the classical counterparts.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 21 of its 48 references that are in the map. First posted on arXiv 4 Dec 2023.

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21 of 48 references.

References · 21

  1. 2020Variational Quantum Algorithmscited by 687
  2. 2018Supervised learning with quantum enhanced feature spacescited by 654
  3. 2018Barren plateaus in quantum neural network training landscapescited by 624
  4. 2020The power of quantum neural networkscited by 377
  5. 2019Parameterized quantum circuits as machine learning modelscited by 330
  6. 2023Challenges and Opportunities in Quantum Machine Learningcited by 202
  7. 2021Supervised quantum machine learning models are kernel methodscited by 199
  8. 2020Entanglement Induced Barren Plateauscited by 101
  9. 2021QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuitscited by 97
  10. 2020Experimental Quantum Generative Adversarial Networks for Image Generationcited by 83
  11. 2020Quantum circuit architecture search for variational quantum algorithmscited by 81
  12. 2016Quantum-enhanced machine learningcited by 79
9 more
  1. 2021Reinforcement learning for optimization of variational quantum circuit architecturescited by 76
  2. 2020Reinforcement Learning with Quantum Variational Circuitscited by 74
  3. 2021Quantum Architecture Search via Deep Reinforcement Learningcited by 58
  4. 2022Escaping from the Barren Plateau via Gaussian Initializations in Deep Variational Quantum Circuitscited by 42
  5. 2022Quantum Neural Architecture Search with Quantum Circuits Metric and Bayesian Optimizationcited by 16
  6. 2022BEINIT: Avoiding Barren Plateaus in Variational Quantum Algorithmscited by 15
  7. 2023QUILT: Effective Multi-Class Classification on Quantum Computers Using an Ensemble of Diverse Quantum Classifierscited by 11
  8. 2022Towards AutoQML: A Cloud-Based Automated Circuit Architecture Search Frameworkcited by 7
  9. 2022Improving Quantum Classifier Performance in NISQ Computers by Voting Strategy from Ensemble Learningcited by 3

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Cited by year

Citing papers per year: 2024: 3.

3 papers.

  1. 2024Differentiable Quantum Architecture Search in Asynchronous Quantum Reinforcement Learningcited by 13
  2. 2024Quantum Machine Learning Architecture Search via Deep Reinforcement Learningcited by 12
  3. 2024Evolutionary Optimization for Designing Variational Quantum Circuits with High Model Capacitycited by 9