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Physica Scripta

Quantum computing of phonon spectra and thermal properties of crystalline solids

Naman Khandelwal, Bikash K. Behera, Ashok Kumar, Prasanta Kumar Panigrahi

Published Jun 16, 2026• 0 citations
Publisher

Abstract

Abstract Variational quantum algorithms offer a promising framework for solving eigenvalue problems on near-term quantum hardware, yet their applicability beyond electronic structure calculations remains relatively unexplored. In this work, we investigate the quantum computing of lattice vibrational and thermodynamical properties by applying the variational quantum eigensolver and variational quantum deflation to phonon Hamiltonians derived from first-principles force constants obtained using density functional theory. The mass-weighted dynamical matrix is mapped onto a qubit-encoded Hermitian operator, enabling computation of the full set of acoustic and optical phonon branches of crystalline silicon and graphene using a reduced qubit register and direct benchmarking against classical diagonalization. The quantum-computed phonon spectrum is further used to evaluate vibrational entropy, constant-volume specific heat, and thermal expansion coefficient, reproducing expected low-temperature quantum behavior and the high-temperature Dulong–Petit limit. We further demonstrate that combined error mitigation strategies help recover phonon dispersions and thermodynamic behavior consistent with expected trends on near-term quantum hardware. Although classical phonon methods remain computationally superior, our results establish phonon-based thermodynamics as a stringent and physically transparent benchmark for assessing variational quantum algorithms on near-term quantum devices.

DOI
10.1088/1402-4896/ae7659

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Variational quantum algorithms offer a promising framework for solving eigenvalue problems on near-term quantum hardware, yet their applicability beyond electronic structure calculations remains relatively unexplored. In this work, we investigate the quantum computing of lattice vibrational and thermodynamical properties by applying the variational quantum eigensolver and variational quantum deflation to phonon Hamiltonians derived from first-principles force constants obtained using density functional theory. The mass-weighted dynamical matrix is mapped onto a qubit-encoded Hermitian operator, enabling computation of the full set of acoustic and optical phonon branches of crystalline silicon and graphene using a reduced qubit register and direct benchmarking against classical diagonalization. The quantum-computed phonon spectrum is further used to evaluate vibrational entropy, constant-volume specific heat, and thermal expansion coefficient, reproducing expected low-temperature quantum behavior and the high-temperature Dulong-Petit limit. We further demonstrate that combined error mitigation strategies help recover phonon dispersions and thermodynamic behavior consistent with expected trends on near-term quantum hardware. Although classical phonon methods remain computationally superior, our results establish phonon-based thermodynamics as a stringent and physically transparent benchmark for assessing variational quantum algorithms on near-term quantum devices.
Quantum chemistry is one of the most promising near-term applications of quantum computers. Quantum algorithms such as variational quantum eigen solver (VQE) and variational quantum deflation (VQD) algorithms have been mainly applied for molecular systems and there is a need to implement such methods for periodic solids. Using Wannier tight-binding Hamiltonian (WTBH) approaches, we demonstrate the application of VQE and VQD to accurately predict both electronic and phonon bandstructure properties of several elemental as well as multi-component solid-state materials. We apply VQE-VQD calculations for 307 spin-orbit coupling based electronic WTBHs and 933 finite-difference based phonon WTBHs. Also, we discuss a workflow for using VQD with lattice Green's function that can be used for solving dynamical mean-field theory problems. The WTBH model solvers can be used for testing other quantum algorithms and models also.
Quantum chemistry is one of the most promising near-term applications of quantum computers. Quantum algorithms such as variational quantum eigen solver (VQE) and variational quantum deflation (VQD) algorithms have been mainly applied for molecular systems and there is a need to implement such methods for periodic solids. Using Wannier tight-binding Hamiltonian (WTBH) approaches, we demonstrate the application of VQE and VQD to accurately predict both electronic and phonon bandstructure properties of several elemental as well as multi-component solid-state materials. We apply VQE-VQD calculations for 307 spin-orbit coupling based electronic WTBHs and 933 finite-difference based phonon WTBHs. We establish a workflow for using VQD with lattice Greens function that can be used for solving dynamical mean-field theory problems. The WTBH model solvers can be used for testing other quantum algorithms also.
The solution of large-scale eigenvalue problems is crucial in nuclear many-body theory, where Hamiltonian matrices often reach extremely large dimensions. Quantum computing opens new perspectives for addressing such demanding problems. Although the Quantum Phase Estimation algorithm offers, in principle, a systematic route to matrix diagonalization, its practical deployment demands levels of coherence and error correction that current quantum hardware cannot yet support. A viable near-term strategy is instead to exploit quantum annealing, which enables the recasting of eigenvalue problems into quadratic unconstrained binary optimization formulations that can be addressed by existing annealing-based processors. Here, we propose a hybrid quantum-classical algorithm that combines quantum annealing and classical deflation to iteratively extract the full eigenspectrum of both standard and generalized eigenvalue problems. We benchmark this method on eigenvalue problems arising from the Equation of Motion Phonon Method performing calculations on real quantum hardware. Our approach illustrates the capabilities and limitations of near-term quantum devices in addressing nuclear eigenvalue problems.
Over the last decade, researchers have been working to improve a crucial aspect of quantum computing to predict Hamiltonian energy of solids. Quantum algorithms such as Variational Quantum Eigensolver (VQE) and Variational Quantum Deflation (VQD) have been used to study the molecular systems. However, there is growing interest in adapting and applying these methods to periodic solid-state materials. In this work, we have integrated first-principles density functional theory with VQE and VQD algorithms and utilizing the Wannier Tight-Binding Hamiltonian (WTBH) method to predict the electronic characteristics of solids. We demonstrate that VQE and VQD algorithms can be used to accurately predict electronic characteristics in a variety of multi-component prototype solid-state materials such as - Silicon (semiconductor), Gold (metallic), Boron Nitrile (insulator), Graphene (semi-metal). Efficient SU2 performs well among all the predefined ansatz used in the study. COBYLA is the fastest optimizer among the classical optimizers with minimum number of iterations for convergence. Results of noise models help to understand the band structure when calculated on real quantum hardware. As quantum hardware advances, our method might be a starting point for using quantum computers to study materials and could help make material discovery more automatic in the future.
Floquet engineering—the periodic driving of quantum systems—offers a powerful pathway to realize exotic nonequilibrium phases, including topological Floquet insulators and discrete time crystals. However, computing the quasienergy band structure of interacting, many-body Floquet systems remains a formidable challenge for classical computers due to the exponential growth of the Hilbert space and the difficulty of diagonalizing the non-Hermitian effective Floquet Hamiltonian. In this work, we introduce and experimentally demonstrate a variational quantum algorithm (VQA) tailored specifically for the near-term computation of Floquet quasienergy bands. Unlike standard variational eigensolvers that minimize static energy expectations, our approach employs a parameterized quantum circuit to prepare variational trial states and directly extracts the quasienergy spectrum via a Hadamard-test-based cost function that measures the complex overlap with the single-period Floquet unitary propagator. By scanning the conserved crystal momentum, we reconstruct the full quasienergy dispersion for both one- and two-dimensional lattice models. We benchmark the algorithm against exact diagonalization for system sizes up to N=8N=8 qubits and subsequently deploy it on superconducting transmon processors, systematically characterizing the impact of decoherence, gate infidelity, and readout errors on the resolved band structure. To mitigate hardware noise, we integrate zero-noise extrapolation and measurement-error mitigation, recovering quasienergy features with fidelities exceeding 90% for shallow circuit depths. Our results establish that variational phase-estimation protocols offer a scalable, hardware-efficient route to exploring driven quantum matter, providing a practical toolkit for simulating Floquet topological phenomena on current-generation quantum devices well before the advent of fault-tolerant quantum computation.