Generalising quantum imaginary time evolution to solve linear partial differential equations

The quantum imaginary time evolution (QITE) methodology was developed to overcome a critical issue as regards non-unitarity in the implementation of imaginary time evolution on a quantum computer. QITE has since been used to approximate ground states of various physical systems. In this paper, we demonstrate a practical application of QITE as a quantum numerical solver for linear partial differential equations. Our algorithm takes inspiration from QITE in that the quantum state follows the same normalised trajectory in both algorithms. However, it is our QITE methodology's ability to track the scale of the state vector over time that allows our algorithm to solve differential equations. We demonstrate our methodology with numerical simulations and use it to solve the heat equation in one and two dimensions using six and ten qubits, respectively.

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Publication Details

Journal
Scientific Reports
Published
2024-08-30
DOI
https://doi.org/10.1038/s41598-024-70423-5
Citations
6
Primary Topic
Quantum Computing Algorithms and Architecture
Type
article
Field-Weighted Citation Impact
2.08

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article

Generalising quantum imaginary time evolution to solve linear partial differential equations

Colin Wilmott, Swagat Kumar
6 citations
Scientific Reports
Quantum Computing Algorithms and Architecture
2.08
article

Generalising quantum imaginary time evolution to solve linear partial differential equations

Colin Wilmott, Swagat Kumar
article en
6 citations

Abstract

The quantum imaginary time evolution (QITE) methodology was developed to overcome a critical issue as regards non-unitarity in the implementation of imaginary time evolution on a quantum computer. QITE has since been used to approximate ground states of various physical systems. In this paper, we demonstrate a practical application of QITE as a quantum numerical solver for linear partial differential equations. Our algorithm takes inspiration from QITE in that the quantum state follows the same normalised trajectory in both algorithms. However, it is our QITE methodology's ability to track the scale of the state vector over time that allows our algorithm to solve differential equations. We demonstrate our methodology with numerical simulations and use it to solve the heat equation in one and two dimensions using six and ten qubits, respectively.

Scientific ReportsVol. 14(1)
Nottingham Trent University (GB)
Engineering and Physical Sciences Research Council
Openalex Percentile: Top 11%
Quantum Computing Algorithms and Architecture
2.08
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Generalising quantum imaginary time evolution to solve linear partial differential equations — Colin Wilmott, Swagat Kumar · Scientific Reports (2024) | TGRS Research Map | TGRS