dorsal/arxiv
View SchemaQuantum solver for single-impurity Anderson models with particle-hole symmetry
| Authors | Mariia Karabin, Tanvir Sohail, Dmytro Bykov, Eduardo Antonio Coello Pérez, Swarnava Ghosh, Murali Gopalakrishnan Meena, Seongmin Kim, Amir Shehata, In-Saeng Suh, Hanna Terletska, Markus Eisenbach |
|---|---|
| Categories | |
| ArXiv ID | 2601.10594vv1 |
| URL | https://arxiv.org/abs/2601.10594 |
| License | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
Abstract
Quantum embedding methods, such as dynamical mean-field theory (DMFT), provide a powerful framework for investigating strongly correlated materials. A central computational bottleneck in DMFT is in solving the Anderson impurity model (AIM), whose exact solution is classically intractable for large bath sizes. In this work, we develop and benchmark a quantum-classical hybrid solver tailored for DMFT applications, using the variational quantum eigensolver (VQE) to prepare the ground state of the AIM with shallow quantum circuits. The solver uses a unified ansatz framework to prepare the particle and hole excitations of the ground-state from parameter-shifted circuits, enabling the reconstruction of the impurity Green's function through a continued-fraction expansion. We evaluate the performance of this approach across a few bath sizes and interaction strengths under noisy, shot-limited conditions. We compare three optimization routines (COBYLA, Adam, and L-BFGS-B) in terms of convergence and fidelity, assess the benefits of estimating a quantum-computed moment (QCM) correction to the variational energies, and benchmark the approach by comparing the reconstructed density of states (DOS) against that obtained using a classical pipeline. Our results demonstrate the feasibility of Green's function reconstruction on near-term devices and establish practical benchmarks for quantum impurity solvers embedded within self-consistent DMFT loops.
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"date_created": "2026-02-17T05:53:23.747000Z",
"date_modified": "2026-02-17T05:53:23.747000Z",
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"abstract": "Quantum embedding methods, such as dynamical mean-field theory (DMFT), provide a powerful framework for investigating strongly correlated materials. A central computational bottleneck in DMFT is in solving the Anderson impurity model (AIM), whose exact solution is classically intractable for large bath sizes. In this work, we develop and benchmark a quantum-classical hybrid solver tailored for DMFT applications, using the variational quantum eigensolver (VQE) to prepare the ground state of the AIM with shallow quantum circuits. The solver uses a unified ansatz framework to prepare the particle and hole excitations of the ground-state from parameter-shifted circuits, enabling the reconstruction of the impurity Green\u0027s function through a continued-fraction expansion. We evaluate the performance of this approach across a few bath sizes and interaction strengths under noisy, shot-limited conditions. We compare three optimization routines (COBYLA, Adam, and L-BFGS-B) in terms of convergence and fidelity, assess the benefits of estimating a quantum-computed moment (QCM) correction to the variational energies, and benchmark the approach by comparing the reconstructed density of states (DOS) against that obtained using a classical pipeline. Our results demonstrate the feasibility of Green\u0027s function reconstruction on near-term devices and establish practical benchmarks for quantum impurity solvers embedded within self-consistent DMFT loops.",
"arxiv_id": "2601.10594",
"authors": [
"Mariia Karabin",
"Tanvir Sohail",
"Dmytro Bykov",
"Eduardo Antonio Coello P\u00e9rez",
"Swarnava Ghosh",
"Murali Gopalakrishnan Meena",
"Seongmin Kim",
"Amir Shehata",
"In-Saeng Suh",
"Hanna Terletska",
"Markus Eisenbach"
],
"categories": [
"quant-ph",
"cond-mat.mtrl-sci"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"title": "Quantum solver for single-impurity Anderson models with particle-hole symmetry",
"url": "https://arxiv.org/abs/2601.10594",
"version": "v1"
},
"schema_id": "dorsal/arxiv",
"source": {
"execution_id": "2c647c57-9c5b-40e8-81f9-ce0638e19f46",
"id": "arXiv Dataset",
"type": "Model",
"variant": "snapshot-2026-01-17",
"version": "0.1.0"
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"user_id": 1000002
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