Abstract
The quantum simulation of entangled spin systems can play a central role in quantum magnetic materials discovery. Additionally, the simulation of spectroscopic signatures, such as the dynamical structure factor accessed in inelastic neutron scattering (INS), necessitates a long timescale for circuit evolution. This is because the energy resolution is directly related to the time over which the circuit could be meaningfully evolved. However, canonical Trotterization requires deep circuits precluding such long-time evolution - even for a small number of qubits. Here we demonstrate "direct"resource efficient fast-forwarding (REFF) measurements with short-depth circuits that can be used to capture longer time dynamics of spin Hamiltonians. We showcase the results of the dynamics of a quantum spin dimer, the basic quantum unit of emergent many-body spin systems, whose density of states we simulate accurately. The long temporal evolution and measurement of the two-spin correlation functions enable the calculation of the dynamical structure factor S(Q=0,ω) measured in the neutron scattering cross-section. We exhibit the clarity of the triplet gap and the triplet splitting of the quantum dimer with class-leading fidelity that enables comparison to experimental neutron data. Our results on current circuit hardware outline an important workflow to predict and benchmark against the outputs of INS experiments of quantum magnets.
| Original language | English |
|---|---|
| Article number | 184414 |
| Journal | Physical Review B |
| Volume | 110 |
| Issue number | 18 |
| DOIs | |
| State | Published - Nov 1 2024 |
| Externally published | Yes |
Funding
We would like to thank Nobuyuki Kurita, Hidekazu Tanaka, et al. for giving us access to the experimental data they obtained in Ref. . We would like to thank Travis Humble for his overall support for this project and the early discussions toward its inception. All authors, except for G.H., and the research as a whole were supported by the Quantum Science Center (QSC), a National Quantum Science Initiative of the Department Of Energy (DOE), managed by Oak Ridge National Laboratory (ORNL). G.H. was supported by the DOE Office of Science, Basic Energy Sciences, under Contract No. DE-SC0022986. Z.H. acknowledges initial support from the LANL Mark Kac Fellowship and subsequent support from the Sandoz Family Foundation-Monique de Meuron program for Academic Promotion. P.K. additionally thanks Travis Humble for funding through the DOE Early Career Award (DOE-ECA). N.M.E. wants to thank IBM Research for their support through an internship over the summer of 2021. We acknowledge the use of IBM Quantum services for this work. This research used resources from the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract No. DE-AC05-00OR22725. We thank Ryan Landfield (ORNL) for facilitating the process of reserving time on IBM-Q backends for the production of the results. We would like to thank Travis Humble for his overall support for this project and the early discussions toward its inception. All authors, except for G.H., and the research as a whole were supported by the Quantum Science Center (QSC), a National Quantum Science Initiative of the Department Of Energy (DOE), managed by Oak Ridge National Laboratory (ORNL). G.H. was supported by the DOE Office of Science, Basic Energy Sciences, under Contract No. DE-SC0022986. Z.H. acknowledges initial support from the LANL Mark Kac Fellowship and subsequent support from the Sandoz Family Foundation-Monique de Meuron program for Academic Promotion. P.K. additionally thanks Travis Humble for funding through the DOE Early Career Award (DOE-ECA). N.M.E. wants to thank IBM Research for their support through an internship over the summer of 2021. We acknowledge the use of IBM Quantum services for this work. This research used resources from the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract No. DE-AC05-00OR22725. We thank Ryan Landfield (ORNL) for facilitating the process of reserving time on IBM-Q backends for the production of the results.