Quantum self-learning Monte Carlo with quantum Fourier transform sampler
arXiv:2005.14075 · doi:10.1103/PhysRevResearch.2.043442
Abstract
The self-learning Metropolis-Hastings algorithm is a powerful Monte Carlo method that, with the help of machine learning, adaptively generates an easy-to-sample probability distribution for approximating a given hard-to-sample distribution. This paper provides a new self-learning Monte Carlo method that utilizes a quantum computer to output a proposal distribution. In particular, we show a novel subclass of this general scheme based on the quantum Fourier transform circuit; this sampler is classically simulable while having a certain advantage over conventional methods. The performance of this "quantum inspired" algorithm is demonstrated by some numerical simulations.
arXiv comment11 pages, 7 figures
References in corpus (19)
- Hardware-efficient Variational Quantum Eigensolver for Small Molecules and Quantum Magnets
- Quantum Circuit Learning
- Quantum Amplitude Amplification and Estimation
- Boson Sampling on a Photonic Chip
- Experimental Boson Sampling
- Quantum Metropolis Sampling
- Classical simulation of commuting quantum computations implies collapse of the polynomial hierarchy
- Differentiable Learning of Quantum Circuit Born Machine
- Unsupervised Generative Modeling Using Matrix Product States
- Self-Learning Monte Carlo Method
- Amplitude estimation without phase estimation
- Accelerate Monte Carlo Simulations with Restricted Boltzmann Machines
- Flow-based generative models for Markov chain Monte Carlo in lattice field theory
- Instantaneous Quantum Computation
- The Born Supremacy: Quantum Advantage and Training of an Ising Born Machine
- 16-qubit IBM universal quantum computer can be fully entangled
- Self-learning Monte Carlo with Deep Neural Networks
- Efficient classical simulation of the semi-classical Quantum Fourier Transform
- Fourier Neural Networks: A Comparative Study
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