Quantum-classical simulation of quantum field theory by quantum circuit
learning
- URL: http://arxiv.org/abs/2311.16297v1
- Date: Mon, 27 Nov 2023 20:18:39 GMT
- Title: Quantum-classical simulation of quantum field theory by quantum circuit
learning
- Authors: Kazuki Ikeda
- Abstract summary: We employ quantum circuit learning to simulate quantum field theories (QFTs)
We find that our predictions closely align with the results of rigorous classical calculations.
This hybrid quantum-classical approach illustrates the feasibility of efficiently simulating large-scale QFTs on cutting-edge quantum devices.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
Related papers
- Quantum Equilibrium Propagation for efficient training of quantum systems based on Onsager reciprocity [0.0]
Equilibrium propagation (EP) is a procedure that has been introduced and applied to classical energy-based models which relax to an equilibrium.
Here, we show a direct connection between EP and Onsager reciprocity and exploit this to derive a quantum version of EP.
This can be used to optimize loss functions that depend on the expectation values of observables of an arbitrary quantum system.
arXiv Detail & Related papers (2024-06-10T17:22:09Z) - Quantum data learning for quantum simulations in high-energy physics [55.41644538483948]
We explore the applicability of quantum-data learning to practical problems in high-energy physics.
We make use of ansatz based on quantum convolutional neural networks and numerically show that it is capable of recognizing quantum phases of ground states.
The observation of non-trivial learning properties demonstrated in these benchmarks will motivate further exploration of the quantum-data learning architecture in high-energy physics.
arXiv Detail & Related papers (2023-06-29T18:00:01Z) - Quantum Machine Learning: from physics to software engineering [58.720142291102135]
We show how classical machine learning approach can help improve the facilities of quantum computers.
We discuss how quantum algorithms and quantum computers may be useful for solving classical machine learning tasks.
arXiv Detail & Related papers (2023-01-04T23:37:45Z) - Time-Optimal Quantum Driving by Variational Circuit Learning [2.9582851733261286]
Digital quantum simulation and hybrid circuit learning opens up new prospects for quantum optimal control.
We simulate the wave-packet expansion of a trapped quantum particle on a quantum device with a finite number qubits.
We discuss the robustness of our method against errors and demonstrate the absence of barren plateaus in the circuit.
arXiv Detail & Related papers (2022-11-01T11:53:49Z) - Simulating groundstate and dynamical quantum phase transitions on a
superconducting quantum computer [0.11744028458220425]
We simulate the groundstate of the quantum Ising model through its quantum critical point on a superconducting quantum device.
Our approach avoids finite-size scaling effects by using sequential quantum circuits inspired by infinite matrix product states.
arXiv Detail & Related papers (2022-05-25T18:05:53Z) - Recompilation-enhanced simulation of electron-phonon dynamics on IBM
Quantum computers [62.997667081978825]
We consider the absolute resource cost for gate-based quantum simulation of small electron-phonon systems.
We perform experiments on IBM quantum hardware for both weak and strong electron-phonon coupling.
Despite significant device noise, through the use of approximate circuit recompilation we obtain electron-phonon dynamics on current quantum computers comparable to exact diagonalisation.
arXiv Detail & Related papers (2022-02-16T19:00:00Z) - Quantum algorithms for quantum dynamics: A performance study on the
spin-boson model [68.8204255655161]
Quantum algorithms for quantum dynamics simulations are traditionally based on implementing a Trotter-approximation of the time-evolution operator.
variational quantum algorithms have become an indispensable alternative, enabling small-scale simulations on present-day hardware.
We show that, despite providing a clear reduction of quantum gate cost, the variational method in its current implementation is unlikely to lead to a quantum advantage.
arXiv Detail & Related papers (2021-08-09T18:00:05Z) - On exploring practical potentials of quantum auto-encoder with
advantages [92.19792304214303]
Quantum auto-encoder (QAE) is a powerful tool to relieve the curse of dimensionality encountered in quantum physics.
We prove that QAE can be used to efficiently calculate the eigenvalues and prepare the corresponding eigenvectors of a high-dimensional quantum state.
We devise three effective QAE-based learning protocols to solve the low-rank state fidelity estimation, the quantum Gibbs state preparation, and the quantum metrology tasks.
arXiv Detail & Related papers (2021-06-29T14:01:40Z) - Information Scrambling in Computationally Complex Quantum Circuits [56.22772134614514]
We experimentally investigate the dynamics of quantum scrambling on a 53-qubit quantum processor.
We show that while operator spreading is captured by an efficient classical model, operator entanglement requires exponentially scaled computational resources to simulate.
arXiv Detail & Related papers (2021-01-21T22:18:49Z) - Quantum simulation of quantum field theories as quantum chemistry [9.208624182273288]
Conformal truncation is a powerful numerical method for solving generic strongly-coupled quantum field theories.
We show that quantum computation could not only help us understand fundamental physics in the lattice approximation, but also simulate quantum field theory methods directly.
arXiv Detail & Related papers (2020-04-28T01:20:04Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.