Abstract
We study two different methods to prepare excited states on a quantum computer, a key initial step to study nuclear dynamics within linear-response theory. The first method uses unitary evolution for a short time T=O(1-F) to approximate the action of an excitation operator Ô with fidelity F and success probability P≈1-F. The second method probabilistically applies the excitation operator using the linear combination of unitaries (LCU) algorithm. We benchmark these techniques on emulated and real quantum devices, using a toy model for thermal neutron-proton capture. Despite its larger-memory footprint, the LCU-based method is efficient even on current generation noisy devices and can be implemented at a lower gate cost than a naive analysis would suggest. These findings show that quantum techniques designed to achieve good asymptotic scaling on fault-tolerant quantum devices might also provide practical benefits on devices with limited connectivity and gate fidelity.
| Original language | English |
|---|---|
| Article number | 064624 |
| Journal | Physical Review C |
| Volume | 102 |
| Issue number | 6 |
| DOIs | |
| State | Published - Dec 28 2020 |
| Externally published | Yes |
Funding
We thank J. Carlson and L. Cincio for useful discussions. The work was supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research (ASCR) quantum algorithm teams program, under field work proposal number ERKJ333, by the U.S. Department of Energy, Office of Science, Office of Nuclear Physics, under Awards No. DE-FG02-00ER41132, No. DE-FG02-96ER40963, No. DE-SC0019478, No. DE-SC0018223, and No. DE-AC52-06NA25396 and by the U.S. Department of Energy HEP QuantISED Grant No. KA2401032. Oak Ridge National Laboratory is supported by the Office of Science of the Department of Energy under Contract No. DE-AC05-00OR22725. We acknowledge the use of the IBM Q for this work. The views expressed are those of the authors and do not reflect the official policy or position of IBM or the IBM Q team. The work was supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research (ASCR) quantum algorithm teams program, under field work proposal number ERKJ333, by the U.S. Department of Energy, Office of Science, Office of Nuclear Physics, under Awards No. DE-FG02-00ER41132, No. DE-FG02-96ER40963, No. DE-SC0019478, No. DE-SC0018223, and No. DE-AC52-06NA25396 and by the U.S. Department of Energy HEP QuantISED Grant No. KA2401032. Oak Ridge National Laboratory is supported by the Office of Science of the Department of Energy under Contract No. DE-AC05-00OR22725. We acknowledge the use of the IBM Q for this work. The views expressed are those of the authors and do not reflect the official policy or position of IBM or the IBM Q team.