Thermal variational quantum simulation on a superconducting quantum processor

Abstract

Solving finite-temperature properties of quantum many-body systems is generally challenging to classical computers due to their high computational complexities. In this article, we present experiments to demonstrate a hybrid quantum-classical simulation of thermal quantum states. By combining a classical probabilistic model and a 5-qubit programmable superconducting quantum processor, we prepare Gibbs states and excited states of Heisenberg XY and XXZ models with high fidelity and compute thermal properties including the variational free energy, energy, and entropy with a small statistical error. Our approach combines the advantage of classical probabilistic models for sampling and quantum co-processors for unitary transformations. We show that the approach is scalable in the number of qubits, and has a self-verifiable feature, revealing its potentials in solving large-scale quantum statistical mechanics problems on near-term intermediate-scale quantum computers.

6 + 13 pages, 11 figures, 1 table

References in corpus (2)

• A Quantum Approximate Optimization Algorithm
• Quantum Hamiltonian-Based Models and the Variational Quantum Thermalizer Algorithm

Cited by in corpus (7)

• Quantum Mixed State Compiling
• Extending the Variational Quantum Eigensolver to Finite Temperatures
• The phase diagram of quantum chromodynamics in one dimension on a quantum computer
• Training Quantum Boltzmann Machines with the $\beta$-Variational Quantum Eigensolver
• Exploring thermal equilibria of the Fermi-Hubbard model with variational quantum algorithms
• Sequential quantum simulation of spin chains with a single circuit QED device
• Variational Quantum Algorithms for Gibbs State Preparation