Universal algorithm for transforming Hamiltonian eigenvalues

Manipulating Hamiltonians governing physical systems has found a broad range of applications, from quantum chemistry to semiconductor design. In this work, we provide a way of manipulating Hamiltonians, by transforming their eigenvalues while keeping their eigenstates unchanged. We develop a universal algorithm that deterministically implements any desired (suitably differentiable) function on the eigenvalues of any unknown Hamiltonian, whose positive-time and negative-time dynamics are given as a black box. Our algorithm uses correlated randomness to efficiently combine two subroutines—namely controlization and Fourier series simulation—exemplifying a general compilation procedure that we develop. The time complexity of our algorithm is significantly reduced via said compilation technique compared to a naïve concatenation of the subroutines and outperforms similar methods based on the quantum singular value transformation.

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Publication Details

Journal
Physical Review Research
Published
2025-03-31
DOI
https://doi.org/10.1103/physrevresearch.7.013331
Citations
1
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.52

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article

Universal algorithm for transforming Hamiltonian eigenvalues

Philip Taranto, Hlér Kristjánsson, Tatsuki Odake, Mio Murao
1 citations
Physical Review Research
Machine Learning in Materials Science
0.52
article

Universal algorithm for transforming Hamiltonian eigenvalues

Philip Taranto, Hlér Kristjánsson, Tatsuki Odake, Mio Murao
article en
1 citations

Abstract

Manipulating Hamiltonians governing physical systems has found a broad range of applications, from quantum chemistry to semiconductor design. In this work, we provide a way of manipulating Hamiltonians, by transforming their eigenvalues while keeping their eigenstates unchanged. We develop a universal algorithm that deterministically implements any desired (suitably differentiable) function on the eigenvalues of any unknown Hamiltonian, whose positive-time and negative-time dynamics are given as a black box. Our algorithm uses correlated randomness to efficiently combine two subroutines—namely controlization and Fourier series simulation—exemplifying a general compilation procedure that we develop. The time complexity of our algorithm is significantly reduced via said compilation technique compared to a naïve concatenation of the subroutines and outperforms similar methods based on the quantum singular value transformation.

Physical Review ResearchVol. 7(1)
Perimeter Institute (CA), University of Manchester (GB), Mila - Quebec Artificial Intelligence Institute (CA), Université de Montréal (CA), The University of Tokyo (JP)
International Business Machines Corporation, Ministry of Education, Culture, Sports, Science and Technology, Ministry of Colleges and Universities, Japan Society for the Promotion of Science
Openalex Percentile: Top 40%
Machine Learning in Materials Science
0.52
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Universal algorithm for transforming Hamiltonian eigenvalues — Philip Taranto, Hlér Kristjánsson, et al. · Physical Review Research (2025) | TGRS Research Map | TGRS