STI Publications - View Publication Form #20100
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Publication Information
| Title | Exploring thermal equilibria of the Fermi–Hubbard model with variational quantum algorithms | ||||
| Abstract | This study investigates the thermal properties of the repulsive Fermi-Hubbard model with chemical potential using variational quantum algorithms, crucial in comprehending particle behaviour within lattices at heightened temperatures in condensed matter systems. Conventional computational methods encounter challenges, especially in managing chemical potential, prompting exploration into Hamiltonian approaches. Despite the promise of quantum algorithms, their efficacy is hampered by coherence limitations when simulating extended imaginary time evolution sequences. To overcome these constraints, this research focuses on optimizing variational quantum algorithms to probe the thermal properties of the Fermi-Hubbard model. Physics-inspired circuit designs are tailored to alleviate coherence constraints, facilitating a more comprehensive exploration of materials at elevated temperatures. Our study demonstrates the potential of variational algorithms in simulating the thermal properties of the | ||||
| Author(s) | Jack Araz, Michael Spannowsky, Matt Wingate | ||||
| Publication Date | June 2024 | ||||
| Document Type | Journal Article | ||||
| Primary Institution | Thomas Jefferson National Accelerator Facility, Newport News | ||||
| Affiliation | Theory & Comp Physics / THEORY CENTER / THEORY CENTER | ||||
| Funding Source | Nuclear Physics (NP) | ||||
| Proprietary? | No | ||||
| This publication conveys | Technical Science Results | ||||
| Document Numbers |
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| Associated with an experiment | No | ||||
| Associated with EIC | No | ||||
| Supported by Jefferson Lab LDRD Funding | No |
Journal Article
| Journal Name | Physical Review A |
| Refereed | No |
| Volume | 109 |
| Issue | |
| Page(s) | 062422 |
Attachments/Datasets/DOI Link
| Document(s) |
Fermi_Hubbard_Model-2.pdf
(STI Document)
2312.09292v2.pdf
(Accepted Manuscript)
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| DOI Link | |
| Dataset(s) | (none) |
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