Investigating Quantum Many-Body Systems with Tensor Networks, Machine Learning and Quantum Computers

TL;DR

This paper combines tensor network simulations, machine learning, and quantum computing to map phase diagrams of quantum many-body systems, demonstrating methods on classical and IBM quantum hardware.

Contribution

It introduces a unified framework integrating tensor networks, deep learning, and quantum variational anomaly detection for studying quantum phases.

Findings

Successful classical simulations of 1D and 2D systems using tensor networks.

Implementation of quantum variational anomaly detection on IBM quantum hardware.

Demonstration of a combined classical and quantum approach for phase diagram mapping.

Abstract

We perform quantum simulation on classical and quantum computers and set up a machine learning framework in which we can map out phase diagrams of known and unknown quantum many-body systems in an unsupervised fashion. The classical simulations are done with state-of-the-art tensor network methods in one and two spatial dimensions. For one dimensional systems, we utilize matrix product states (MPS) that have many practical advantages and can be optimized using the efficient density matrix renormalization group (DMRG) algorithm. The data for two dimensional systems is obtained from entangled projected pair states (PEPS) optimized via imaginary time evolution. Data in form of observables, entanglement spectra, or parts of the state vectors from these simulations, is then fed into a deep learning (DL) pipeline where we perform anomaly detection to map out the phase diagram. We extend this notion to quantum computers and introduce quantum variational anomaly detection. Here, we first simulate the ground state and then process it in a quantum machine learning (QML) manner. Both simulation and QML routines are performed on the same device, which we demonstrate both in classical simulation and on a physical quantum computer hosted by IBM.