Quantum-classical simulation of quantum field theory by quantum circuit learning
This work addresses the problem of efficiently simulating large-scale QFTs for quantum computing researchers, offering a hybrid quantum-classical method that is incremental in improving simulation feasibility.
The authors tackled the challenge of simulating quantum field theories (QFTs) on quantum computers by using quantum circuit learning with compact qubit configurations and low-depth circuits to predict dynamics like quench, chiral, and jet production in a 1+1D model, achieving high accuracy that closely matches classical calculations.
We employ quantum circuit learning to simulate quantum field theories (QFTs). Typically, when simulating QFTs with quantum computers, we encounter significant challenges due to the technical limitations of quantum devices when implementing the Hamiltonian using Pauli spin matrices. To address this challenge, we leverage quantum circuit learning, 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 our method, we use it to predict quench dynamics, chiral dynamics and jet production in a 1+1-dimensional model of quantum electrodynamics. We find 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.