Hybrid Quantum-Classical Computing for Quasiparticle Band Structures
© The Physical Society of Japan
This article is on
J. Phys. Soc. Jpn.
94,
114002
(2025)
.
The proposed hybrid quantum-classical computing method enables practical calculations of quasiparticle band structures to expand the possibilities for quantum materials research.
Understanding the electronic properties of materials, particularly those with strong electron correlations, is a central challenge in condensed matter physics and materials science. One key concept is the “quasiparticle band structure,” which describes how electrons behave in a solid and determines its physical properties. However, accurate calculation of these band structures is difficult for classical computers, particularly for materials with strong electron interactions.
Quantum computing has emerged as a promising approach to overcome these challenges. Among the various quantum algorithms, the variational quantum eigensolver (VQE) combined with quantum subspace expansion (QSE) has shown potential for simulating electronic structures. However, as the size of the system increases, optimizing the many parameters in VQE becomes increasingly challenging due to device noise and the so-called “barren plateau” problem, which makes finding the optimal solution very difficult.
In this study, we introduce a hybrid quantum-classical computing method that combines QSE with the quantum-selected configuration interaction (QSCI) technique. Unlike standard VQE, QSCI does not require the full optimization of all parameters. Instead, it uses a quantum computer to sample dominant electron configurations, and then employs a classical computer to build the subspace Hamiltonian and perform the diagonalization required to obtain the energy levels and band structure. This approach is more robust against noise and statistical errors, making it suitable for larger quantum systems.
To demonstrate the effectiveness of this method, we calculated the quasiparticle band structure of a silicon crystal using 16 qubits on an IBM quantum processor. Our results agreed well with those obtained using accurate classical numerical methods, validating the effectiveness of our approach. The QSCI-QSE method not only improves scalability but also paves the way to study more complex and strongly correlated materials that were previously out of reach.
(Written by T. Ohgoe on behalf of all authors.)
J. Phys. Soc. Jpn.
94,
114002
(2025)
.
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