mlsanityData to 14 Oct 2026

Quantum assisted Gaussian process regression

arXiv 1512.03929 ↗First posted 12 Dec 2015quant-ph cs.LG stat.MLcited by 17

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Abstract (arXiv)

Gaussian processes (GP) are a widely used model for regression problems in supervised machine learning. Implementation of GP regression typically requires O(n3) logic gates. We show that the quantum linear systems algorithm [Harrow et al., Phys. Rev. Lett. 103, 150502 (2009)] can be applied to Gaussian process regression (GPR), leading to an exponential reduction in computation time in some instances. We show that even in some cases not ideally suited to the quantum linear systems algorithm, a polynomial increase in efficiency still occurs.

Key facts. Cited by 17 papers in the map (data through 14 Oct 2026). First posted on arXiv 12 Dec 2015.

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Citing papers per year: 2021: 2; 2022: 1; 2023: 6; 2024: 0; 2025: 4; 2026: 4.

17 papers.

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  1. 2023Towards provably efficient quantum algorithms for large-scale machine-learning modelscited by 32
  2. 2023A survey on the complexity of learning quantum statescited by 32
  3. 2023Quantum Machine Learning on Near-Term Quantum Devices: Current State of Supervised and Unsupervised Techniques for Real-World Applicationscited by 14
  4. 2021The Hintons in your Neural Network: a Quantum Field Theory View of Deep Learningcited by 9
  5. 2023Quantum Gaussian Process Regression for Bayesian Optimizationcited by 9
  6. 2023Advantage of Quantum Machine Learning from General Computational Advantagescited by 4
  7. 2022Training Hybrid Classical-Quantum Classifiers via Stochastic Variational Optimizationcited by 3
  8. 2023sQUlearn -- A Python Library for Quantum Machine Learningcited by 3
  9. 2025Quantum advantage for learning shallow neural networks with natural data distributionscited by 3
  10. 2021Quantum Machine Learning For Classical Datacited by 2
  11. 2025Assessing Quantum Advantage for Gaussian Process Regressioncited by 2
  12. 2025Old Rules in a New Game: Mapping Uncertainty Quantification to Quantum Machine Learningcited by 2
5 more
  1. 2026Non-variational supervised quantum kernel methods: a reviewcited by 2
  2. 2025Quantum-assisted Gaussian process regression using random Fourier featurescited by 1
  3. 2026Distributed Quantum Gaussian Processes for Multi-Agent Systemscited by 0
  4. 2026Spectral Anatomy of Quantum Gaussian Process Kernelscited by 0
  5. 2026A Quantum-Inspired Dequantization Method for Diagonally Weighted Matrix Functions: Application to Learning with Optimized Random Featurescited by 0