Longtime simulations for fixed input states on quantum hardware
Abstract
Publicly accessible quantum computers open the exciting possibility of experimental dynamical quantum simulations. While rapidly improving, current devices have short coherence times, restricting the viable circuit depth. Despite these limitations, we demonstrate longtime, high fidelity simulations on current hardware. Specifically, we simulate an XYmodel spin chain on Rigetti and IBM quantum computers, maintaining a fidelity over 0.9 for 150 times longer than is possible using the iterated Trotter method. Our simulations use an algorithm we call fixed state Variational Fast Forwarding (fsVFF). Recent work has shown an approximate diagonalization of a short time evolution unitary allows a fixeddepth simulation. fsVFF substantially reduces the required resources by only diagonalizing the energy subspace spanned by the initial state, rather than over the total Hilbert space. We further demonstrate the viability of fsVFF through large numerical simulations, and provide an analysis of the noise resilience and scaling of simulation errors.
 Publication:

npj Quantum Information
 Pub Date:
 2022
 DOI:
 10.1038/s41534022006250
 arXiv:
 arXiv:2102.04313
 Bibcode:
 2022npjQI...8..135G
 Keywords:

 Quantum Physics;
 Computer Science  Machine Learning;
 Statistics  Machine Learning
 EPrint:
 Main text: 14 pages, 11 Figures. Appendices: 10 pages, 1 Figure