Strategies for solving the Fermi-Hubbard model on near-term quantum computers

Item: Strategies for solving the Fermi-Hubbard model on near-term quantum computers
Rating: 4
Author: PostSingularity Institute

by Lana Mineh

Formal & Physical Sciences Computing & Cybersecurity UYComputer science UYXNatural computing

🎙 Lana Mineh 👥 1K 📅 June 24, 2020 ⏱ 42 min 👁 592 📄 original study 🧭 2026-08-18

Available in: English (current) Français

Keywords

Fermi-Hubbard modelvariational quantum eigensolverJordan-Wigner transformationHamiltonian variational ansatznoise resilience

Summary

Lana Mineh presents her research on solving the Fermi-Hubbard model using variational quantum eigensolver (VQE) on near-term quantum computers. She introduces the challenges of NISQ devices, including limited qubits, gate depth, connectivity, and noise. The VQE algorithm is explained as a hybrid quantum-classical approach to find ground states. The Fermi-Hubbard model is described as a fundamental model in condensed matter physics, with classical simulations limited to 22 sites. The talk details the Jordan-Wigner transformation for mapping fermions to qubits, and the use of a snake ordering to minimize circuit depth. Two ansatz circuits are considered: the Hamiltonian Variational ansatz and a novel number-preserving ansatz. Efficient implementations using swap networks reduce circuit depth. The measurement strategy exploits commuting sets of terms to reduce circuit evaluations. The classical optimizers SPSA and coordinate descent are compared, with SPSA showing robustness to noise. Numerical simulations for systems up to 12 sites demonstrate that the ansätze can achieve high fidelity with relatively low circuit depth, even under depolarizing noise. An error detection procedure based on number conservation is also tested. The results suggest that near-term quantum computers could potentially solve instances of the Hubbard model beyond classical exact diagonalization.

194 words

Critical Evaluation

Value of the Information & Strength of the Argument

The talk provides valuable insights into the practical implementation of VQE for the Fermi-Hubbard model, emphasizing circuit depth optimization and noise resilience. The argumentation is solid, supported by numerical experiments and comparisons of different ansätze and optimizers. The speaker clearly explains the trade-offs between ansatz expressibility and optimization complexity. The presentation is well-structured, building from basic concepts to detailed results.

Scientific Rigor, Source Quality, Title Accuracy

The talk is based on original research, with a related paper on arXiv (https://arxiv.org/abs/1912.06007) . The speaker cites relevant prior work, such as the Hamiltonian Variational ansatz, and provides context from condensed matter physics. The title accurately reflects the content. The talk is a seminar presentation, so it is not peer-reviewed, but the methodology appears rigorous. The description includes links to the speaker’s lab and the hosting institution, which add credibility.

146 words

Title / Content Match

The title accurately reflects the content: the talk focuses on strategies for solving the Fermi-Hubbard model on near-term quantum computers, including ansatz design, circuit optimization, and numerical results.

Quality & Reliability

8/10

Presentation of original research with detailed methodology, numerical simulations, and references to a preprint on arXiv. The speaker is affiliated with a recognized university lab. The talk is technical and appears rigorous, though it is a seminar and not peer-reviewed at the time.

Key Moments

Markers derived by PSI from the transcript: the creator did not define chapters.

Cited Sources

Strategies for solving the Fermi-Hubbard model on near-term quantum computers — The paper associated with this talk, providing detailed methodology and results.
UTS Centre for Quantum Software and Information — Hosting institution for the seminar.
Michael Bremner's profile — Host of the seminar and professor at UTS.

Concurring Sources

Strategies for solving the Fermi-Hubbard model on near-term quantum computers — The paper itself, which the talk is based on.

Contribution & Novelties

The talk presents original research on optimizing VQE for the Fermi-Hubbard model, introducing an efficient Hamiltonian Variational ansatz and a novel number-preserving ansatz. The use of swap networks significantly reduces circuit depth compared to previous work. The numerical simulations for systems up to 12 sites demonstrate that high fidelity can be achieved with relatively low circuit depth, even under realistic noise. This work contributes to the feasibility of using near-term quantum computers for quantum simulation.

Pour aller plus loin :

Fermi-Hubbard model — Provides background on the model and its importance in condensed matter physics.
Variational quantum eigensolver — Overview of the VQE algorithm and its applications.
Jordan-Wigner transformation — Explanation of the mapping used to encode fermions on qubits.
Quantum error mitigation — Techniques to handle noise in near-term quantum computers, relevant to the error detection discussed.

138 words

Radar Profile

The radar profile shows high scores in technical level and information quality, with slightly lower but still strong scores in quantity and reliability. This indicates a technically dense and reliable presentation, suitable for an expert audience.

Reliability 8/10