Ground-State Preparation of the Fermi-Hubbard Model on a Quantum Computer with 2D Topology via Quantum Eigenvalue Transformation of Unitary Matrices
- URL: http://arxiv.org/abs/2411.18535v1
- Date: Wed, 27 Nov 2024 17:32:17 GMT
- Title: Ground-State Preparation of the Fermi-Hubbard Model on a Quantum Computer with 2D Topology via Quantum Eigenvalue Transformation of Unitary Matrices
- Authors: Thilo R. Müller, Manuel Geiger, Christian B. Mendl,
- Abstract summary: We apply the QETU algorithm to the $2 times 2$ Fermi-Hubbard model.
We present circuit simplifications tailored to the model and introduce a mapping to a 9-qubit grid-like hardware architecture inspired by fermionic swap networks.
- Score: 0.0
- License:
- Abstract: Quantum computing holds immense promise for simulating quantum systems, a critical task for advancing our understanding of complex quantum phenomena. One of the primary goals in this domain is to accurately approximate the ground state of quantum systems. The Fermi-Hubbard model, particularly, is of profound interest due to its implications for high-temperature superconductivity and strongly correlated electron systems. The quantum eigenvalue transformation of unitary matrices (QETU) algorithm offers a novel approach for ground state estimation by utilizing a controlled Hamiltonian time evolution operator, circumventing the resource-intensive block-encoding required by previous methods. In this work, we apply the QETU algorithm to the $2 \times 2$ Fermi-Hubbard model, presenting circuit simplifications tailored to the model and introducing a mapping to a 9-qubit grid-like hardware architecture inspired by fermionic swap networks. We investigate how the selection of a favorable hardware architecture can benefit the circuit construction. Additionally, we explore the feasibility of this method under the influence of noise, focusing on its robustness and practical applicability.
Related papers
- Improving thermal state preparation of Sachdev-Ye-Kitaev model with reinforcement learning on quantum hardware [0.0]
This paper integrates reinforcement learning with convolutional neural networks to prepare thermal states on near-term quantum processors.
We demonstrate the effectiveness of the framework in both noiseless and noisy quantum hardware environments.
arXiv Detail & Related papers (2025-01-20T12:41:17Z) - A recipe for local simulation of strongly-correlated fermionic matter on quantum computers: the 2D Fermi-Hubbard model [0.0]
We provide a step-by-step recipe for simulating the paradigmatic two-dimensional Fermi-Hubbard model on a quantum computer using only local operations.
We provide a detailed recipe for an end-to-end simulation including embedding on a physical device.
arXiv Detail & Related papers (2024-08-26T18:00:07Z) - Enhancing Scalability of Quantum Eigenvalue Transformation of Unitary Matrices for Ground State Preparation through Adaptive Finer Filtering [0.13108652488669736]
Hamiltonian simulation is a domain where quantum computers have the potential to outperform classical counterparts.
One of the main challenges of such quantum algorithms is increasing the system size.
We present an approach to improve the scalability of eigenspace filtering for the ground state preparation of a given Hamiltonian.
arXiv Detail & Related papers (2024-01-17T09:52:24Z) - 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) - 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) - A quantum processor based on coherent transport of entangled atom arrays [44.62475518267084]
We show a quantum processor with dynamic, nonlocal connectivity, in which entangled qubits are coherently transported in a highly parallel manner.
We use this architecture to realize programmable generation of entangled graph states such as cluster states and a 7-qubit Steane code state.
arXiv Detail & Related papers (2021-12-07T19:00:00Z) - Observing ground-state properties of the Fermi-Hubbard model using a
scalable algorithm on a quantum computer [0.029316801942271296]
We show an efficient, low-depth variational quantum algorithm with few parameters can reproduce important qualitative features of medium-size instances of the Fermi-Hubbard model.
We address 1x8 and 2x4 instances on 16 qubits on a superconducting quantum processor.
We observe the onset of the metal-insulator transition and Friedel oscillations in 1D, and antiferromagnetic order in both 1D and 2D.
arXiv Detail & Related papers (2021-12-03T17:14:20Z) - Realization of arbitrary doubly-controlled quantum phase gates [62.997667081978825]
We introduce a high-fidelity gate set inspired by a proposal for near-term quantum advantage in optimization problems.
By orchestrating coherent, multi-level control over three transmon qutrits, we synthesize a family of deterministic, continuous-angle quantum phase gates acting in the natural three-qubit computational basis.
arXiv Detail & Related papers (2021-08-03T17:49:09Z) - Fixed Depth Hamiltonian Simulation via Cartan Decomposition [59.20417091220753]
We present a constructive algorithm for generating quantum circuits with time-independent depth.
We highlight our algorithm for special classes of models, including Anderson localization in one dimensional transverse field XY model.
In addition to providing exact circuits for a broad set of spin and fermionic models, our algorithm provides broad analytic and numerical insight into optimal Hamiltonian simulations.
arXiv Detail & Related papers (2021-04-01T19:06:00Z) - Electronic structure with direct diagonalization on a D-Wave quantum
annealer [62.997667081978825]
This work implements the general Quantum Annealer Eigensolver (QAE) algorithm to solve the molecular electronic Hamiltonian eigenvalue-eigenvector problem on a D-Wave 2000Q quantum annealer.
We demonstrate the use of D-Wave hardware for obtaining ground and electronically excited states across a variety of small molecular systems.
arXiv Detail & Related papers (2020-09-02T22:46:47Z) - Resource-Optimized Fermionic Local-Hamiltonian Simulation on Quantum
Computer for Quantum Chemistry [6.361119478712919]
We present a framework that enables bootstrapping the VQE progression towards the convergence of the ground-state energy of the fermionic system.
We show that resource-requirement savings of up to more than $20%$, in small instances, is possible.
arXiv Detail & Related papers (2020-04-08T17:59:13Z)
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.