Neural Network Operations and Susuki-Trotter evolution of Neural Network States

Abstract

It was recently proposed to leverage the representational power of artificial neural networks, in particular Restricted Boltzmann Machines, in order to model complex quantum states of many-body systems [Science, 355(6325), 2017]. States represented in this way, called Neural Network States (NNSs), were shown to display interesting properties like the ability to efficiently capture long-range quantum correlations. However, identifying an optimal neural network representation of a given state might be challenging, and so far this problem has been addressed with stochastic optimization techniques. In this work we explore a different direction. We study how the action of elementary quantum operations modifies NNSs. We parametrize a family of many body quantum operations that can be directly applied to states represented by Unrestricted Boltzmann Machines, by just adding hidden nodes and updating the network parameters. We show that this parametrization contains a set of universal quantum gates, from which it follows that the state prepared by any quantum circuit can be expressed as a Neural Network State with a number of hidden nodes that grows linearly with the number of elementary operations in the circuit. This is a powerful representation theorem (which was recently obtained with different methods) but that is not directly useful, since there is no general and efficient way to extract information from this unrestricted description of quantum states. To circumvent this problem, we propose a step-wise procedure based on the projection of Unrestricted quantum states to Restricted quantum states. In turn, two approximate methods to perform this projection are discussed. In this way, we show that it is in principle possible to approximately optimize or evolve Neural Network States without relying on stochastic methods such as Variational Monte Carlo, which are computationally expensive.

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• Self-learning projective quantum Monte Carlo simulations guided by restricted Boltzmann machines
• Machine Learning Quantum States -- Extensions to Fermion-Boson Coupled Systems and Excited-State Calculations
• Correlation-Enhanced Neural Networks as Interpretable Variational Quantum States
• Transforming Generalized Ising Model into Boltzmann Machine
• Sampling scheme for neuromorphic simulation of entangled quantum systems
• Compact Neural-network Quantum State representations of Jastrow and Stabilizer states
• Neural-network Quantum States for Spin-1 systems: spin-basis and parameterization effects on compactness of representations
• Boltzmann machines and quantum many-body problems
• An Empirical Study of Quantum Dynamics as a Ground State Problem with Neural Quantum States