Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

NANOCOM ...

Quantum-computing study of the electronic structure of crystals: the case study of Si

Michal Ďuriška, Ivana Miháliková, Martin Friák

Published Jan 1, 2023• 1 citation
Publisher

Abstract

Quantum computing is newly emerging information-processing technology which is foreseen to be exponentially faster than classical supercomputers.Current quantum processors are nevertheless very limited in their availability and performance and many important software tools for them do not exist yet.Therefore, various systems are studied by simulating the run of quantum computers.Building upon our previous experience with quantum computing of small molecular systems (see I. Miháliková et al., Molecules 27 (2022) 597, and I. Miháliková et al., Nanomaterials 2022, 12, 243), we have recently focused on computing electronic structure of periodic crystalline materials.Being inspired by the work of Cerasoli et al. (Phys.Chem.Chem.Phys., 2020, 22, 21816), we have used hybrid variational quantum eigensolver (VQE) algorithm, which combined classical and quantum information processing.Employing tight-binding type of crystal description, we present our results for crystalline diamond-structure silicon.In particular, we focus on the states along the lowest occupied band within the electronic structure of Si and compare the results with values obtained by classical means.While we demonstrate an excellence agreement between classical and quantum-computed results in most of our calculations, we further critically check the sensitivity of our results with respect to computational set-up in our quantum-computing study.A few results were obtained also using quantum processors provided by the IBM.

DOI
10.37904/nanocon.2023.4774

Related papers

Quantum computing of the electronic structure of crystals by the Variational Quantum Deflation algorithm

Michal Ďuriška, Ivana Miháliková, Martin Friák

Abstract Variational Quantum Eigensolver (VQE) and its extension, Variational Quantum Deflation (VQD), have emerged as promising algorithms for computing ground and excited state energy eigenvalues of Hamiltonians, particularly in quantum physics, chemistry, and materials science. Recently, the VQE and VQD algorithms were implemented to calculate the electronic band structure described by the tight-binding Hamiltonian. Despite their success for simple models, scalability remains a challenge due to the need for a large number of circuit executions, limited qubit coherence times, and low gate fidelities. In this work, we implemented the VQE and VQD algorithms for tight-binding models of diamond and zincblende crystal structures (Sn, C, Si, Ge, AlP, AlAs, AlSb, and GaP) using a quantum computer simulator. We investigate the scalability challenges, focusing on the significant overhead caused by a large number of quantum circuit executions required to find the energy eigenvalues. Our analysis highlights the substantial amount of both measurement and optimization overheads in VQD, particularly as the number of excited states increases. Based on our findings, we critically discuss the implications of implementing VQD on current noisy intermediate-scale quantum (NISQ) devices.

Physica Scripta•Feb 27, 2025•6 citations

Quantum computation of silicon electronic band structure

Frank T. Cerasoli, Kyle Sherbert, Jagoda Sławińska +1 more

Development of quantum architectures during the last decade has inspired hybrid classical-quantum algorithms in physics and quantum chemistry that promise simulations of fermionic systems beyond the capability of modern classical computers, even before the era of quantum computing fully arrives. Strong research efforts have been recently made to obtain minimal depth quantum circuits which could accurately represent chemical systems. Here, we show that unprecedented methods used in quantum chemistry, designed to simulate molecules on quantum processors, can be extended to calculate properties of periodic solids. In particular, we present minimal depth circuits implementing the variational quantum eigensolver algorithm and successfully use it to compute the band structure of silicon on a quantum machine for the first time. We are convinced that the presented quantum experiments performed on cloud-based platforms will stimulate more intense studies towards scalable electronic structure computation of advanced quantum materials.

arXiv:2006.03807•Physical Chemistry Chemical Physics•Jan 1, 2020•33 citations

Quantum computation of the electronic structure of some prototype solids

Naman Khandelwal, Nidhi Verma, Pooja Jamdagni +1 more

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.

arXiv:2509.01648•Scientific Reports•Dec 18, 2025•2 citations

SUPPRESING NOISE IN QUANTUM COMPUTING OF ELECTRONIC STRUCTURE OF CRYSTALS

Jan HOJAČ, Vojtěch VAŠINA, Martin Friák

Quantum computing is currently emerging as a new computational technology for solving complex computational problems.Current quantum computers are unfortunately too noisy to provide sufficient accuracy, and quantum-classical hybrid algorithms emerged as a solution.Variational Quantum Algorithms are currently showing promising results at overcoming noise introduced by the current level of quantum hardware.Variational Quantum Deflation (VQD) has gained significant attention when computing the full spectrum of eigenvalues in quantum chemistry and solid-state physics.The noise in the VQD calculations performed on the current noisy quantum computers, or their simulators, can be partly suppressed by methods available in the Qiskit Python package provided by IBM.In our study, several error suppression and mitigation methods have been tested, and our results offer a unique comparison in the case of calculations on the electronic structure of crystals, where performance is sensitive to the actual point in the reciprocal space.

NANOCOM ...•Jan 1, 2025•0 citations

Quantum Computation of a Quasiparticle Band Structure with the Quantum-Selected Configuration Interaction

Takahiro Ohgoe, Hokuto Iwakiri, Kazuhide Ichikawa +2 more

Quasiparticle band structures are fundamental for understanding strongly correlated electron systems. While solving these structures accurately on classical computers is challenging, quantum computing offers a promising alternative. Specifically, the quantum subspace expansion (QSE) method, combined with the variational quantum eigensolver (VQE), provides a quantum algorithm for calculating quasiparticle band structures. However, optimizing the variational parameters in VQE becomes increasingly difficult as the system size grows, due to device noise, statistical noise, and the barren plateau problem. To address these challenges, we propose a hybrid approach that combines QSE with the quantum-selected configuration interaction (QSCI) method for calculating quasiparticle band structures. QSCI may leverage the VQE ansatz as an input state but, unlike the standard VQE, it does not require full optimization of the variational parameters, making it more scalable for larger quantum systems. Based on this approach, we demonstrate the quantum computation of the quasiparticle band structure of a silicon using 16 qubits on an IBM quantum processor.

arXiv:2504.00309•Journal of the Physical Society of Japan•Oct 21, 2025•3 citations

Implementing the Variational Quantum Eigensolver for Materials

Clayton Smith

'In the past few years quantum computers have seen considerable interest from fields which have problems which are computationally expensive to solve with classical computers. One area that has been a major of source of this interest is the simulation of chemical systems. Materials are one type of system that is important to a variety of industries such as renewable energy and medicine. Among the obstacles faced in simulating materials, materials can have properties which are challenging to calculate on classical computers. The promise of quantum computing for improving the exactness of chemical calculations has been shown with a number of methods for a variety of systems. One such method which has previously been used for materials is the variational quantum eigensolver (VQE). However work doing this is still fairly scarce and also typically uses closed source/paid software or does not publish detailed code. The goal of this research is to implement VQE for materials using the python package Tangelo. This implementation will then be used to perform calculations on simple systems to compare this implementation with existing results.

•Jan 1, 2024•0 citations
Quantum computing is newly emerging information-processing technology which is foreseen to be exponentially faster than classical supercomputers.Current quantum processors are nevertheless very limited in their availability and performance and many important software tools for them do not exist yet.Therefore, various systems are studied by simulating the run of quantum computers.Building upon our previous experience with quantum computing of small molecular systems (see I. Miháliková et al., Molecules 27 (2022) 597, and I. Miháliková et al., Nanomaterials 2022, 12, 243), we have recently focused on computing electronic structure of periodic crystalline materials.Being inspired by the work of Cerasoli et al. (Phys.Chem.Chem.Phys., 2020, 22, 21816), we have used hybrid variational quantum eigensolver (VQE) algorithm, which combined classical and quantum information processing.Employing tight-binding type of crystal description, we present our results for crystalline diamond-structure silicon.In particular, we focus on the states along the lowest occupied band within the electronic structure of Si and compare the results with values obtained by classical means.While we demonstrate an excellence agreement between classical and quantum-computed results in most of our calculations, we further critically check the sensitivity of our results with respect to computational set-up in our quantum-computing study.A few results were obtained also using quantum processors provided by the IBM.
Abstract Variational Quantum Eigensolver (VQE) and its extension, Variational Quantum Deflation (VQD), have emerged as promising algorithms for computing ground and excited state energy eigenvalues of Hamiltonians, particularly in quantum physics, chemistry, and materials science. Recently, the VQE and VQD algorithms were implemented to calculate the electronic band structure described by the tight-binding Hamiltonian. Despite their success for simple models, scalability remains a challenge due to the need for a large number of circuit executions, limited qubit coherence times, and low gate fidelities. In this work, we implemented the VQE and VQD algorithms for tight-binding models of diamond and zincblende crystal structures (Sn, C, Si, Ge, AlP, AlAs, AlSb, and GaP) using a quantum computer simulator. We investigate the scalability challenges, focusing on the significant overhead caused by a large number of quantum circuit executions required to find the energy eigenvalues. Our analysis highlights the substantial amount of both measurement and optimization overheads in VQD, particularly as the number of excited states increases. Based on our findings, we critically discuss the implications of implementing VQD on current noisy intermediate-scale quantum (NISQ) devices.
Development of quantum architectures during the last decade has inspired hybrid classical-quantum algorithms in physics and quantum chemistry that promise simulations of fermionic systems beyond the capability of modern classical computers, even before the era of quantum computing fully arrives. Strong research efforts have been recently made to obtain minimal depth quantum circuits which could accurately represent chemical systems. Here, we show that unprecedented methods used in quantum chemistry, designed to simulate molecules on quantum processors, can be extended to calculate properties of periodic solids. In particular, we present minimal depth circuits implementing the variational quantum eigensolver algorithm and successfully use it to compute the band structure of silicon on a quantum machine for the first time. We are convinced that the presented quantum experiments performed on cloud-based platforms will stimulate more intense studies towards scalable electronic structure computation of advanced quantum materials.
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.
Quantum computing is currently emerging as a new computational technology for solving complex computational problems.Current quantum computers are unfortunately too noisy to provide sufficient accuracy, and quantum-classical hybrid algorithms emerged as a solution.Variational Quantum Algorithms are currently showing promising results at overcoming noise introduced by the current level of quantum hardware.Variational Quantum Deflation (VQD) has gained significant attention when computing the full spectrum of eigenvalues in quantum chemistry and solid-state physics.The noise in the VQD calculations performed on the current noisy quantum computers, or their simulators, can be partly suppressed by methods available in the Qiskit Python package provided by IBM.In our study, several error suppression and mitigation methods have been tested, and our results offer a unique comparison in the case of calculations on the electronic structure of crystals, where performance is sensitive to the actual point in the reciprocal space.
Quasiparticle band structures are fundamental for understanding strongly correlated electron systems. While solving these structures accurately on classical computers is challenging, quantum computing offers a promising alternative. Specifically, the quantum subspace expansion (QSE) method, combined with the variational quantum eigensolver (VQE), provides a quantum algorithm for calculating quasiparticle band structures. However, optimizing the variational parameters in VQE becomes increasingly difficult as the system size grows, due to device noise, statistical noise, and the barren plateau problem. To address these challenges, we propose a hybrid approach that combines QSE with the quantum-selected configuration interaction (QSCI) method for calculating quasiparticle band structures. QSCI may leverage the VQE ansatz as an input state but, unlike the standard VQE, it does not require full optimization of the variational parameters, making it more scalable for larger quantum systems. Based on this approach, we demonstrate the quantum computation of the quasiparticle band structure of a silicon using 16 qubits on an IBM quantum processor.
'In the past few years quantum computers have seen considerable interest from fields which have problems which are computationally expensive to solve with classical computers. One area that has been a major of source of this interest is the simulation of chemical systems. Materials are one type of system that is important to a variety of industries such as renewable energy and medicine. Among the obstacles faced in simulating materials, materials can have properties which are challenging to calculate on classical computers. The promise of quantum computing for improving the exactness of chemical calculations has been shown with a number of methods for a variety of systems. One such method which has previously been used for materials is the variational quantum eigensolver (VQE). However work doing this is still fairly scarce and also typically uses closed source/paid software or does not publish detailed code. The goal of this research is to implement VQE for materials using the python package Tangelo. This implementation will then be used to perform calculations on simple systems to compare this implementation with existing results.