2022 journal article
Quantum Markov chain Monte Carlo with digital dissipative dynamics on quantum computers
Quantum Science and Technology, 2.
author keywords: quantum algorithm; thermal state preparation; algorithmic cooling
TL;DR:
A digital quantum algorithm is developed that simulates interaction with an environment using a small number of ancilla qubits and is able to mimic interaction with a large environment and generate thermal states of interacting many-body systems.
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open access via arxiv.org (repository)
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9. Industry, Innovation and Infrastructure
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Source: ORCID
Added: February 12, 2022
Abstract Modeling the dynamics of a quantum system connected to the environment is critical for advancing our understanding of complex quantum processes, as most quantum processes in nature are affected by an environment. Modeling a macroscopic environment on a quantum simulator may be achieved by coupling independent ancilla qubits that facilitate energy exchange in an appropriate manner with the system and mimic an environment. This approach requires a large, and possibly exponential number of ancillary degrees of freedom which is impractical. In contrast, we develop a digital quantum algorithm that simulates interaction with an environment using a small number of ancilla qubits. By combining periodic modulation of the ancilla energies, or spectral combing, with periodic reset operations, we are able to mimic interaction with a large environment and generate thermal states of interacting many-body systems. We evaluate the algorithm by simulating preparation of thermal states of the transverse Ising model. Our algorithm can also be viewed as a quantum Markov chain Monte Carlo process that allows sampling of the Gibbs distribution of a multivariate model. To demonstrate this we evaluate the accuracy of sampling Gibbs distributions of simple probabilistic graphical models using the algorithm.