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.
Authors
- Philip Taranto (ORCID: https://orcid.org/0000-0002-4247-3901)
- Hlér Kristjánsson (ORCID: https://orcid.org/0000-0003-4465-2863)
- Tatsuki Odake (ORCID: https://orcid.org/0009-0009-6844-9959)
- Mio Murao (ORCID: https://orcid.org/0000-0001-7861-1774)
Institutions
- Perimeter Institute (CA)
- University of Manchester (GB)
- Mila - Quebec Artificial Intelligence Institute (CA)
- Université de Montréal (CA)
- The University of Tokyo (JP)
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
Funders
- International Business Machines Corporation
- Ministry of Education, Culture, Sports, Science and Technology
- Ministry of Colleges and Universities
- Japan Society for the Promotion of Science