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Quasiprobabilistic Imaginary-Time Evolution on Quantum Computers Cover

Quasiprobabilistic Imaginary-Time Evolution on Quantum Computers

Open Access
|Jun 2026

Abstract

Imaginary-time evolution plays an important role in algorithms for computing ground-state and thermal equilibrium properties of quantum systems, but can be challenging to simulate on classical computers. Many quantum algorithms for imaginary-time evolution have resource requirements that are prohibitive for current quantum devices and face performance issues due to noise. Here, we propose a new algorithm for computing imaginary-time evolved expectation values on quantum computers, inspired by probabilistic error cancellation, an error-mitigation technique. Our algorithm works by decomposing a Trotterization of imaginary-time evolution into a probabilistic linear combination of operations, each of which is then implemented on a quantum computer. The measurement data is then classically post-processed to obtain the expectation value of the imaginary-time evolved state. Our algorithm requires no ancillary qubits and can be made noise-resilient without additional error-mitigation. It is well-suited for estimating thermal expectation values by making use of the notion of a thermal pure quantum state. We demonstrate our algorithm by performing numerical simulations of thermal pure quantum state preparation for the 1D Heisenberg Hamiltonian on 8 qubits, and by using an IBM quantum computer to estimate the energy of the same Hamiltonian on 2 qubits. We observe promising results compared to the exact values, illustrating the potential of our algorithm for probing the physics of quantum many-body systems on current hardware.

DOI: https://doi.org/10.2478/qic-2026-0005 | Journal eISSN: 3106-0544 | Journal ISSN: 1533-7146
Language: English
Page range: 89 - 113
Submitted on: Jun 2, 2025
Accepted on: Dec 25, 2025
Published on: Jun 4, 2026
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2026 Annie Ray, Esha Swaroop, Ningping Cao, Michael Vasmer, Anirban Chowdhury, published by Cerebration Science Publishing Co., Limited
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.