Ground state energy and magnetization curve of a frustrated magnetic
system from real-time evolution on a digital quantum processor
- URL: http://arxiv.org/abs/2401.03015v1
- Date: Fri, 5 Jan 2024 18:57:34 GMT
- Title: Ground state energy and magnetization curve of a frustrated magnetic
system from real-time evolution on a digital quantum processor
- Authors: Aaron Szasz, Ed Younis, Wibe Albert de Jong
- Abstract summary: We show how to construct efficient quantum circuits to implement time evolution for the Heisenberg model.
We also give an empirical demonstration on small systems that the hybrid algorithms can efficiently find the ground state energy and the magnetization curve.
- Score: 0.47191037525744733
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Models of interacting many-body quantum systems that may realize new exotic
phases of matter, notably quantum spin liquids, are challenging to study using
even state-of-the-art classical methods such as tensor network simulations.
Quantum computing provides a promising route for overcoming these difficulties
to find ground states, dynamics, and more. In this paper, we argue that
recently developed hybrid quantum-classical algorithms based on real-time
evolution are promising methods for solving a particularly important model in
the search for spin liquids, the antiferromagnetic Heisenberg model on the
two-dimensional kagome lattice. We show how to construct efficient quantum
circuits to implement time evolution for the model and to evaluate key
observables on the quantum computer, and we argue that the method has favorable
scaling with increasing system size. We then restrict to a 12-spin star
plaquette from the kagome lattice and a related 8-spin system, and we give an
empirical demonstration on these small systems that the hybrid algorithms can
efficiently find the ground state energy and the magnetization curve. For these
demonstrations, we use four levels of approximation: exact state vectors, exact
state vectors with statistical noise from sampling, noisy classical emulators,
and (for the 8-spin system only) real quantum hardware, specifically the
Quantinuum H1-1 processor; for the noisy simulations, we also employ error
mitigation strategies based on the symmetries of the Hamiltonian. Our results
strongly suggest that these hybrid algorithms present a promising direction for
resolving important unsolved problems in condensed matter theory and beyond.
Related papers
- Fourier Neural Operators for Learning Dynamics in Quantum Spin Systems [77.88054335119074]
We use FNOs to model the evolution of random quantum spin systems.
We apply FNOs to a compact set of Hamiltonian observables instead of the entire $2n$ quantum wavefunction.
arXiv Detail & Related papers (2024-09-05T07:18:09Z) - Demonstration of a variational quantum eigensolver with a solid-state spin system under ambient conditions [15.044543674753308]
Quantum simulators offer the potential to utilize the quantum nature of a physical system to study another physical system.
The variational-quantum-eigensolver algorithm is a particularly promising application for investigating molecular electronic structures.
Spin-based solid-state qubits have the advantage of long decoherence time and high-fidelity quantum gates.
arXiv Detail & Related papers (2024-07-23T09:17:06Z) - Entanglement with neutral atoms in the simulation of nonequilibrium dynamics of one-dimensional spin models [0.0]
We study the generation and role of entanglement in the dynamics of spin-1/2 models.
We introduce the neutral atom Molmer-Sorensen gate, involving rapid adiabatic Rydberg dressing interleaved in a spin-echo sequence.
In quantum simulation, we consider critical behavior in quench dynamics of transverse field Ising models.
arXiv Detail & Related papers (2024-06-07T23:29:16Z) - Computational supremacy in quantum simulation [22.596358764113624]
We show that superconducting quantum annealing processors can generate samples in close agreement with solutions of the Schr"odinger equation.
We conclude that no known approach can achieve the same accuracy as the quantum annealer within a reasonable timeframe.
arXiv Detail & Related papers (2024-03-01T19:00:04Z) - 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) - Towards Neural Variational Monte Carlo That Scales Linearly with System
Size [67.09349921751341]
Quantum many-body problems are central to demystifying some exotic quantum phenomena, e.g., high-temperature superconductors.
The combination of neural networks (NN) for representing quantum states, and the Variational Monte Carlo (VMC) algorithm, has been shown to be a promising method for solving such problems.
We propose a NN architecture called Vector-Quantized Neural Quantum States (VQ-NQS) that utilizes vector-quantization techniques to leverage redundancies in the local-energy calculations of the VMC algorithm.
arXiv Detail & Related papers (2022-12-21T19:00:04Z) - Finite-size criticality in fully connected spin models on
superconducting quantum hardware [0.0]
We exploit the new resources offered by quantum algorithms to detect the quantum critical behaviour of fully connected spin$-1/2$ models.
We propose a method based on variational algorithms run on superconducting transmon qubits.
arXiv Detail & Related papers (2022-08-04T16:00:34Z) - 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) - Simulating the Mott transition on a noisy digital quantum computer via
Cartan-based fast-forwarding circuits [62.73367618671969]
Dynamical mean-field theory (DMFT) maps the local Green's function of the Hubbard model to that of the Anderson impurity model.
Quantum and hybrid quantum-classical algorithms have been proposed to efficiently solve impurity models.
This work presents the first computation of the Mott phase transition using noisy digital quantum hardware.
arXiv Detail & Related papers (2021-12-10T17:32:15Z) - 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)
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.