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.
(via Semantic Scholar)

UN Sustainable Development Goal Categories

7. Affordable and Clean Energy
(OpenAlex)

9. Industry, Innovation and Infrastructure
(Web of Science)

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.