Quantum Gaussian process regression

Abstract

In this paper, a quantum algorithm based on gaussian process regression model is proposed. The proposed quantum algorithm consists of three sub-algorithms. One is the first quantum subalgorithm to efficiently generate mean predictor. The improved HHL algorithm is proposed to obtain the sign of outcomes. Therefore, the terrible situation that results is ambiguous in terms of original HHL algorithm is avoided, which makes whole algorithm more clear and exact. The other is to product covariance predictor with same method. Thirdly, the squared exponential covariance matrices are prepared that annihilation operator and generation operator are simulated by the unitary linear decomposition Hamiltonian simulation and kernel function vectors is generated with blocking coding techniques on covariance matrices. In addition, it is shown that the proposed quantum gaussian process regression algorithm can achieve quadratic faster over the classical counterpart.

References in corpus (20)

• Quantum Computation with Quantum Dots
• Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer
• Quantum Machine Learning
• Elementary gates for quantum computation
• Quantum Convolutional Neural Networks
• Quantum Amplitude Amplification and Estimation
• Quantum principal component analysis
• Quantum fingerprinting
• Quantum random access memory
• Synthesis of Quantum Logic Circuits
• Prediction by linear regression on a quantum computer
• Review of Entangled Coherent States
• Coherent Communication of Classical Messages
• Quantum assisted Gaussian process regression
• Quantum algorithm for association rules mining
• Quantum algorithm for association rules mining
• Quantum K-nearest neighbor classification algorithm based on Hamming distance
• On the role of dealing with quantum coherence in amplitude amplification
• The Wilson-Racah Quantum System
• Data classification by quantum radial basis function networks

Cited by in corpus (1)

• Quantum Gaussian Process Regression for Bayesian Optimization