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Quantum simulations of materials on near-term quantum computers
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Quantum computers hold promise to enable efficient simulations of the properties of molecules and materials; however, at present they only permit ab initio calculations of a few atoms, due to a limited number of qubits. In order to harness the power of near-term quantum computers for simulations of larger systems, it is desirable to develop hybrid quantum-classical methods where the quantum computation is restricted to a small portion of the system. This is of particular relevance for molecules and solids where an active region requires a higher level of theoretical accuracy than its environment. Here, we present a quantum embedding theory for the calculation of strongly-correlated electronic states of active regions, with the rest of the system described within density functional theory. We demonstrate the accuracy and effectiveness of the approach by investigating several defect quantum bits in semiconductors that are of great interest for quantum information technologies. We perform calculations on quantum computers and show that they yield results in agreement with those obtained with exact diagonalization on classical architectures, paving the way to simulations of realistic materials on near-term quantum computers.
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“Over the past several years, there has been vast progress made in both theory, with the introduction of novel code constructions, improvements in code parameters, and decoding algorithms, and experiment, with advancements in the quality of qubits, coherence times, and gate fidelities, and even some early demonstrations of logical error suppression below threshold and small fault-tolerant primitiv”
From Quantum error correction and fault tolerance: A comprehensive tutorial (2026) · cites this paper as [37]
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“We note that a variant of the unrotated surface code that has been rotated 45◦ is a popular choice in experimental demonstrations of QEC [26, 31, 37, 105].”
From Quantum error correction and fault tolerance: A comprehensive tutorial (2026) · cites this paper as [37]
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Cites
Generalized Gradient Approximation Made Simple
Perdew, J. P., Burke, K., & Ernzerhof, M. (1996) · Physical Review Letters · doi:10.1103/physrevlett.77.3865 · Cited by 215,824
QUANTUM ESPRESSO: a modular and open-source software project for quantum simulations of materials
Giannozzi, P., Baroni, S., Bonini, N., Calandra, M., Car, R., Cavazzoni, C., Ceresoli, D., Chiarotti, G., Cococcioni, M., Dabo, I., et al. (2009) · Journal of Physics Condensed Matter · doi:10.1088/0953-8984/21/39/395502 · Cited by 29,793
Quantum Computing in the NISQ era and beyond
Preskill, J. (2018) · Quantum · doi:10.22331/q-2018-08-06-79 · Cited by 8,830
Georges, A., Kotliar, G., Krauth, W., & Rozenberg, M. J. (1996) · Reviews of Modern Physics · doi:10.1103/revmodphys.68.13 · Cited by 6,901
A variational eigenvalue solver on a photonic quantum processor
Peruzzo, A., McClean, J., Shadbolt, P., Yung, M.-H., Zhou, X.-Q., Love, P. J., Aspuru-Guzik, A., & O’Brien, J. L. (2014) · Nature Communications · doi:10.1038/ncomms5213 · Cited by 4,552 · cited 2×
“mbedding theory, we investigated the stronglycorrelated electronic states of the NV center in diamond using quantum phase estimation algorithm (PEA) [46, 8] and variational quantum eigensolvers (VQE) [47, 48, 49], and we show that quantum simulations yield results in agreement with those obtained with classical FCI ca…”
MethodResultAnisimov, V. I., Aryasetiawan, F., & Lichtenstein, A. I. (1997) · Journal of Physics Condensed Matter · doi:10.1088/0953-8984/9/4/002 · Cited by 4,180
Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets
Kandala, A., Mezzacapo, A., Temme, K., Takita, M., Brink, M., Chow, J. M., & Gambetta, J. M. (2017) · Nature · doi:10.1038/nature23879 · Cited by 2,982
The nitrogen-vacancy colour centre in diamond
Doherty, M. W., Manson, N. B., Delaney, P., Jelezko, F., Wrachtrup, J., & Hollenberg, L. C. L. (2013) · Physics Reports · doi:10.1016/j.physrep.2013.02.001 · Cited by 2,866
Electronic structure calculations with dynamical mean-field theory
Kotliar, G., Savrasov, S. Y., Haule, K., Oudovenko, V. S., Parcollet, O., & Marianetti, C. A. (2006) · Reviews of Modern Physics · doi:10.1103/revmodphys.78.865 · Cited by 2,611
�ber das Paulische �quivalenzverbot
Jordan, P., & Wigner, E. (1928) · Zeitschrift f�r Physik · doi:10.1007/bf01331938 · Cited by 2,597
Research works printed but not linked: 7
- Sun, Q. et al. Py SCF: the python-based simulations of chemistry framework. Wiley Interdiscip. Rev.: Comput. Mol. Sci. 8, e1340 (2017).
- Bravyi, S., Gambetta, J. M., Mezzacapo, A. & Temme, K. Tapering off qubits to simulate fermionic hamiltonians. Preprint at https://arxiv.org/abs/1701.08213 (2017). · arXiv
- Rubin, N. C. A hybrid classical/quantum approach for large-scale studies of quantum systems with density matrix embedding theory. Preprint at https://arxiv.org/abs/1610.06910 (2016). · arXiv
- Yamazaki, T., Matsuura, S., Narimani, A., Saidmuradov, A. & Zaribafiyan, A. Towards the practical application of near-term quantum computers in quantum chemistry simulations: a problem decomposition approach. Preprint at https://arxiv.org/abs/1806.01305 (2018). · arXiv
- Bauer, B., Wecker, D., Millis, A. J., Hastings, M. B. & Troyer, M. Hybrid quantum-classical approach to correlated materials. Phys. Rev. X 6, 031045 (2016).
- Seo, H., Govoni, M. & Galli, G. Design of defect spins in piezoelectric aluminum nitride for solid-state hybrid quantum technologies. Sci. Rep. 6, 20803 (2016).
- Jacob, C. R. & Neugebauer, J. Subsystem density-functional theory. Wiley Interdiscip. Rev.: Comput. Mol. Sci. 4, 325–362 (2014).
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Jin, Y., Park, J., McMillan, M. M., Ohm, D. D., Barnes, C., Pingault, B., Egerstrom, C., Huang, B., Govoni, M., Heremans, F. J., et al. (2025) · Physical Review Letters · doi:10.1103/nw3r-zy8q · Cited by 17
Self-citationToward density functional theory on quantum computers?
Senjean, B., Yalouz, S., & Saubanère, M. (2023) · SciPost Physics · doi:10.21468/scipostphys.14.3.055 · Cited by 18
Entanglement-variational hardware-efficient ansatz for eigensolvers
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Learning bounds for quantum circuits in the agnostic setting
Popescu, C. M. (2021) · Quantum Information Processing · doi:10.1007/s11128-021-03225-7 · Cited by 13
Design Rules to Engineer the Spin Structure of Cr 4+ Molecular Qubits via Matrix Modularity
Baldinelli, L., Sorbelli, D., Toriyama, M. Y., Bistoni, G., Angelis, F. D., & Galli, G. (2025) · Journal of the American Chemical Society · doi:10.1021/jacs.5c04004 · Cited by 13
Self-citationBenchmarking the Variational Quantum Eigensolver using different quantum hardware
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Scalable approach to quantum simulation via projection-based embedding
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Sequential optimal selections of single-qubit gates in parameterized quantum circuits
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Code interoperability extends the scope of quantum simulations
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Self-citationGraph optimization perspective for low-depth Trotter-Suzuki decomposition
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Molecular dynamics on quantum annealers
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Screened configuration interaction method for open-shell excited states applied to NV centers
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Identifying native point defect configurations in α-alumina
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Kovyrshin, A., Skogh, M., Broo, A., Mensa, S., Sahin, E., Crain, J. N., & Tavernelli, I. (2023) · The Journal of Chemical Physics · doi:10.1063/5.0150291 · Cited by 13
Algorithm-oriented qubit mapping for variational quantum algorithms
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Sparse modeling of large-scale quantum impurity models with low symmetries
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Fermionic simulators for enhanced scalability of variational quantum simulation
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Chiarotti, T., Marzari, N., & Ferretti, A. (2022) · Physical Review Research · doi:10.1103/physrevresearch.4.013242 · Cited by 11
Metadata
- Authors
- Ma, H., Govoni, M., & Galli, G.
- Affiliations
- Argonne National Laboratory (US)
- Corresponding author
- Ma, He
- Govoni, Marco
- Galli, Giulia
- Venue
- npj Computational Materials
- Published
- 2020-07-02
- Volume
- 6
- Issue
- 1
- ISSN
- 2057-3960 (ISSN-L)
- OpenAlex
- W3007817217
- Cited by
- 197
- References
- 78
- Citations by year
- 2026: 16 · 2025: 34 · 2024: 39 · 2023: 40 · 2022: 32 · 2021: 27 · 2020: 9
- FWCI
- 10.60
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- npj Computational Materials (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- IRIS UNIMORE (University of Modena and Reggio Emilia) (submittedVersion, cc-by)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Funders
- U.S. Department of Energy (DE-AC02-06CH11357)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum many-body systems
- Keywords
- hybrid quantum-classical methods, near-term quantum devices, strongly correlated electrons, density functional theory, semiconductor defects, quantum information technology
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