Quantum-Classical Embedding via Ghost Gutzwiller Approximation for Enhanced Simulations of Correlated Electron Systems

• URL: http://arxiv.org/abs/2506.01204v1
• Date: Sun, 01 Jun 2025 22:47:31 GMT
• Title: Quantum-Classical Embedding via Ghost Gutzwiller Approximation for Enhanced Simulations of Correlated Electron Systems
• Authors: I-Chi Chen, Aleksei Khindanov, Carlos Salazar, Humberto Munoz Barona, Feng Zhang, Cai-Zhuang Wang, Thomas Iadecola, Nicola Lanatà, Yong-Xin Yao,
• Abstract summary: Simulating correlated materials on present-day quantum hardware remains challenging due to limited quantum resources.<n>Quantum embedding methods offer a promising route by reducing computational complexity.<n>This work develops a quantum-classical embedding framework based on the ghost Gutzwiller approximation.
• Score: 3.5408300118027243
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Simulating correlated materials on present-day quantum hardware remains challenging due to limited quantum resources. Quantum embedding methods offer a promising route by reducing computational complexity through the mapping of bulk systems onto effective impurity models, allowing more feasible simulations on pre- and early-fault-tolerant quantum devices. This work develops a quantum-classical embedding framework based on the ghost Gutzwiller approximation to enable quantum-enhanced simulations of ground-state properties and spectral functions of correlated electron systems. Circuit complexity is analyzed using an adaptive variational quantum algorithm on a statevector simulator, applied to the infinite-dimensional Hubbard model with increasing ghost mode numbers from 3 to 5, resulting in circuit depths growing from 16 to 104. Noise effects are examined using a realistic error model, revealing significant impact on the spectral weight of the Hubbard bands. To mitigate these effects, the Iceberg quantum error detection code is employed, achieving up to 40% error reduction in simulations. Finally, the accuracy of the density matrix estimation is benchmarked on IBM and Quantinuum quantum hardware, featuring distinct qubit-connectivity and employing multiple levels of error mitigation techniques.

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