Quantum‐Classical Simulation of Quantum Field Theory by Quantum Circuit Learning
[1]
- Department of Physics University of Massachusetts Boston Boston MA 02125 USA, Center for Nuclear Theory Department of Physics and Astronomy Stony Brook University Stony Brook New York 11794‐3800 USA, Co‐design Center for Quantum Advantage Department of Physics and Astronomy Stony Brook University Stony Brook New York 11794‐3800 USA
Abstract Quantum circuit learning is employed to simulate quantum field theories (QFTs). Typically, when simulating QFTs with quantum computers, significant challenges are encountered due to the technical limitations of quantum devices when implementing the Hamiltonian using Pauli spin matrices. To address this challenge, quantum circuit learning is leveraged, employing a compact configuration of qubits and low‐depth quantum circuits to predict real‐time dynamics in quantum field theories. The key advantage of this approach is that a single‐qubit measurement can accurately forecast various physical parameters, including fully‐connected operators. To demonstrate the effectiveness of this method, it is used to predict quench dynamics, chiral dynamics and jet production in a 1+1‐dimensional model of quantum electrodynamics. It is found that our predictions closely align with the results of rigorous classical calculations, exhibiting a high degree of accuracy. This hybrid quantum‐classical approach illustrates the feasibility of efficiently simulating large‐scale QFTs on cutting‐edge quantum devices.
- Sponsoring Organization:
- USDOE
- OSTI ID:
- 2530490
- Journal Information:
- Annalen der Physik (Leipzig), Journal Name: Annalen der Physik (Leipzig); ISSN 0003-3804
- Publisher:
- Wiley Blackwell (John Wiley & Sons)Copyright Statement
- Country of Publication:
- Germany
- Language:
- English
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