The DOI matches its Crossref record; 42 of the 43 references it cites were found in a registry or an index.
Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets
Open accessAbstract
Quantum computers can be used to address electronic-structure problems and problems in materials science and condensed matter physics that can be formulated as interacting fermionic problems, problems which stretch the limits of existing high-performance computers. Finding exact solutions to such problems numerically has a computational cost that scales exponentially with the size of the system, and Monte Carlo methods are unsuitable owing to the fermionic sign problem. These limitations of classical computational methods have made solving even few-atom electronic-structure problems interesting for implementation using medium-sized quantum computers. Yet experimental implementations have so far been restricted to molecules involving only hydrogen and helium. Here we demonstrate the experimental optimization of Hamiltonian problems with up to six qubits and more than one hundred Pauli terms, determining the ground-state energy for molecules of increasing size, up to BeH 2. We achieve this result by using a variational quantum eigenvalue solver (eigensolver) with efficiently prepared trial states that are tailored specifically to the interactions that are available in our quantum processor, combined with a compact encoding of fermionic Hamiltonians and a robust stochastic optimization routine. We demonstrate the flexibility of our approach by applying it to a problem of quantum magnetism, an antiferromagnetic Heisenberg model in an external magnetic field. In all cases, we find agreement between our experiments and numerical simulations using a model of the device with noise. Our results help to elucidate the requirements for scaling the method to larger systems and for bridging the gap between key problems in high-performance computing and their implementation on quantum hardware.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Pauli strings that commute qubit-wise, i.e. the Pauli operators on each qubit commute, can be measured at the same time (Kandala et al. , 2017; McClean et al. , 2016).”
From Noisy intermediate-scale quantum algorithms (2022) · cites this paper as Kandala et al., 2017
In the citing paper
“Aclass of hardware-efficient ansätze (see Fig. 3(b)) has been proposed to accommodate device constraints (Kandala et al. , 2017).”
From Noisy intermediate-scale quantum algorithms (2022) · cites this paper as Kandala et al., 2017
In the citing paper
“While the quantum analog of #P , #BQP , has been defined and has been shown to contain important physical problems, it turns out to be of equal complexity to #P 67 .”
From Quantum Chemistry in the Age of Quantum Computing (2019) · cites this paper as [67]
In the citing paper
“The most prominent example of this class of algorithms relevant for electronic structure calculations is the variational quantum eigensolver (VQE), which presents a variational (and thus heuristic) algorithm to upper bounding the ground state energy problem.[126][127][128]”
From Quantum Computing for Molecular Biology** (2023) · cites this paper as [127]
Citations
Cites
Szabó, A., & Ostlund, N. S. (1982) · Cited by 6,239
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
Multivariate stochastic approximation using a simultaneous perturbation gradient approximation
Spall, J. C. (1992) · IEEE Transactions on Automatic Control · doi:10.1109/9.119632 · Cited by 2,283
The theory of variational hybrid quantum-classical algorithms
McClean, J. R., Romero, J., Babbush, R., & Aspuru-Guzik, A. (2016) · New Journal of Physics · doi:10.1088/1367-2630/18/2/023023 · Cited by 2,227
A Quantum Approximate Optimization Algorithm
Farhi, E., Goldstone, J., & Gutmann, S. (2014) · arXiv · doi:10.48550/arxiv.1411.4028 · Cited by 1,789
Simulated Quantum Computation of Molecular Energies
Aspuru-Guzik, A., Dutoi, A. D., Love, P. J., & Head-Gordon, M. (2005) · Science · doi:10.1126/science.1113479 · Cited by 1,418
Error Mitigation for Short-Depth Quantum Circuits
Temme, K., Bravyi, S., & Gambetta, J. M. (2017) · Physical Review Letters · doi:10.1103/physrevlett.119.180509 · Cited by 1,379
Self-citationScalable Quantum Simulation of Molecular Energies
O’Malley, P. J. J., Babbush, R., Kivlichan, I. D., Romero, J., McClean, J. R., Barends, R., Kelly, J., Roushan, P., Tranter, A., Ding, N., et al. (2016) · Physical Review X · doi:10.1103/physrevx.6.031007 · Cited by 1,059
Bravyi, S. B., & Kitaev, A. Y. (2002) · Annals of Physics · doi:10.1006/aphy.2002.6254 · Cited by 1,033
Towards quantum chemistry on a quantum computer
Lanyon, B. P., Whitfield, J. D., Gillett, G. G., Goggin, M. E., Almeida, M. P., Kassal, I., Biamonte, J. D., Mohseni, M., Powell, B. J., Barbieri, M., et al. (2010) · Nature Chemistry · doi:10.1038/nchem.483 · Cited by 796
Cited by
Sorted by citations
Items 901–1,000 of 1,010
Quantum algorithms for electronic structures: basis sets and boundary conditions
Liu, J., Fan, Y., Li, Z., & Yang, J. (2022) · Chemical Society Reviews · doi:10.1039/d1cs01184g · Cited by 27
Hyperparameter importance and optimization of quantum neural networks across small datasets
Moussa, C., Patel, Y. J., Dunjko, V., Bäck, T., & van Rijn, J. N. (2023) · Machine Learning · doi:10.1007/s10994-023-06389-8 · Cited by 31
Impact of Unreliable Devices on Stability of Quantum Computations
Dasgupta, S., & Humble, T. S. (2024) · ACM Transactions on Quantum Computing · doi:10.1145/3682071 · Cited by 46
Automatic quantum circuit encoding of a given arbitrary quantum state
Shirakawa, T., Ueda, H., & Yunoki, S. (2024) · Physical Review Research · doi:10.1103/physrevresearch.6.043008 · Cited by 35
Predicting excited states from ground state wavefunction by supervised quantum machine learning
Kawai, H., & Nakagawa, Y. O. (2020) · Machine Learning Science and Technology · doi:10.1088/2632-2153/aba183 · Cited by 27
Efficient Estimation of Trainability for Variational Quantum Circuits
Heyraud, V., Li, Z., Donatella, K., Boité, A. L., & Ciuti, C. (2023) · PRX Quantum · doi:10.1103/prxquantum.4.040335 · Cited by 29
Neural-network-encoded variational quantum algorithms
Miao, J., Hsieh, C., & Zhang, S. (2024) · Physical Review Applied · doi:10.1103/physrevapplied.21.014053 · Cited by 28
A Leap From Theory to Reality: Knowledge Visualization of Quantum Computing
Rawat, K. S., & Sharma, T. K. (2024) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2024.3466949 · Cited by 26
Quantum algorithms for scientific computing
Au-Yeung, R., Camino, B., Rathore, O., & Kendon, V. M. (2024) · Reports on Progress in Physics · doi:10.1088/1361-6633/ad85f0 · Cited by 37
Hybrid digital-analog simulation of many-body dynamics with superconducting qubits
Валерьевич, Б. Д., Zhukov, A. A., & Pogosov, W. V. (2020) · Physical Review A · doi:10.1103/physreva.101.052337 · Cited by 28
Verma, P., Huntington, L. M. J., Coons, M. P., Kawashima, Y., Yamazaki, T., & Zaribafiyan, A. (2021) · The Journal of Chemical Physics · doi:10.1063/5.0054647 · Cited by 29
Quantum Computer-Aided Design: Digital Quantum Simulation of Quantum Processors
Kyaw, T. H., Menke, T., Sim, S., Anand, A., Sawaya, N. P. D., Oliver, W. D., Guerreschi, G. G., & Aspuru‐Guzik, A. (2021) · Physical Review Applied · doi:10.1103/physrevapplied.16.044042 · Cited by 27
Unitary block optimization for variational quantum algorithms
Slattery, L., Villalonga, B., & Clark, B. K. (2022) · Physical Review Research · doi:10.1103/physrevresearch.4.023072 · Cited by 28
Quantum kernels to learn the phases of quantum matter
Sancho-Lorente, T., Román-Roche, J., & Zueco, D. (2022) · Physical Review A · doi:10.1103/physreva.105.042432 · Cited by 33
Bauman, N. P., & Kowalski, K. (2022) · The Journal of Chemical Physics · doi:10.1063/5.0076260 · Cited by 28
Limiting the Search Space in Optimal Quantum Circuit Mapping
Burgholzer, L., Schneider, S., & Wille, R. (2022) · Springer eBooks · doi:10.1007/978-3-032-06770-8_11 · Cited by 28
Xie, Q., Liu, S., & Zhao, Y. (2022) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c00159 · Cited by 27
Bierman, J., Li, Y., & Lu, J. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c00895 · Cited by 28
Gradient-Descent Quantum Process Tomography by Learning Kraus Operators
Ahmed, S., Quijandría, F., & Kockum, A. F. (2023) · Physical Review Letters · doi:10.1103/physrevlett.130.150402 · Cited by 35
Advancements in Quantum Computing—Viewpoint: Building Adoption and Competency in Industry
Pfaendler, S. M.-L., Konson, K., & Greinert, F. (2024) · Datenbank-Spektrum · doi:10.1007/s13222-024-00467-4 · Cited by 32
Quantum Davidson algorithm for excited states
Tkachenko, N. V., Cincio, Ł., Boldyrev, A. I., Tretiak, S., Dub, P. A., & Zhang, Y. (2024) · Quantum Science and Technology · doi:10.1088/2058-9565/ad3a97 · Cited by 40
Doubly Optimal Parallel Wire Cutting without Ancilla Qubits
Harada, H., Wada, K., & Yamamoto, N. (2024) · PRX Quantum · doi:10.1103/prxquantum.5.040308 · Cited by 46
Experimental data from a quantum computer verifies the generalized Pauli exclusion principle
Smart, S. E., Schuster, D., & Mazziotti, D. A. (2019) · Communications Physics · doi:10.1038/s42005-019-0110-3 · Cited by 25
Canonical circuit quantization with linear nonreciprocal devices
Parra-Rodriguez, A., Egusquiza, I. L., Divincenzo, D. P., & Solano, E. (2019) · Physical review. B./Physical review. B · doi:10.1103/physrevb.99.014514 · Cited by 25
Reinforcement learning for semi-autonomous approximate quantum eigensolver
Albarrán-Arriagada, F., Retamal, J. C., Solano, E., & Lamata, L. (2020) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ab43b4 · Cited by 26
Improving the accuracy and efficiency of quantum connected moments expansions *
Claudino, D., Peng, B., Bauman, N. P., Kowalski, K., & Humble, T. S. (2021) · Quantum Science and Technology · doi:10.1088/2058-9565/ac0292 · Cited by 25
Mizuta, K., Fujii, M., Fujii, S., Ichikawa, K., Imamura, Y., Okuno, Y., & Nakagawa, Y. O. (2021) · Physical Review Research · doi:10.1103/physrevresearch.3.043121 · Cited by 27
Noise-induced shallow circuits and the absence of barren plateaus
Mele, A. A., Angrisani, A., Ghosh, S., Khatri, S., Eisert, J., França, D. S., & Quek, Y. (2026) · Nature Physics · doi:10.1038/s41567-026-03245-z · Cited by 65
Quantum Algorithms for Quantum Molecular Systems: A Survey
Zhang, Y., Zhang, X., Sun, J., Lin, H., Huang, Y., Lv, D., & Yuan, X. (2025) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.70020 · Cited by 30
Topology-driven quantum architecture search framework
Su, J., Fan, J., Wu, S., Li, G., Qin, S., & Gao, F. (2025) · Science China Information Sciences · doi:10.1007/s11432-024-4486-x · Cited by 34
A simulational model for witnessing quantum effects of gravity using IBM quantum computer
Manabputra, Behera, B. K., & Panigrahi, P. K. (2020) · Quantum Information Processing · doi:10.1007/s11128-020-2617-7 · Cited by 28
Yu, X., Jiang, L., Quan, Y., Wu, T., Chen, Y., Zou, L., & Wu, J. (2020) · Physical review. B./Physical review. B · doi:10.1103/physrevb.101.045422 · Cited by 24
Quantum computation and simulation with superconducting qubits*
He, K., Geng, X., Huang, R., Liu, J., & Chen, W. (2021) · Chinese Physics B · doi:10.1088/1674-1056/ac16cf · Cited by 24
García-Molina, P., Rodríguez-Mediavilla, J., & García‐Ripoll, J. J. (2022) · Physical Review A · doi:10.1103/physreva.105.012433 · Cited by 30
Evolving objective function for improved variational quantum optimization
Kolotouros, I., & Wallden, P. (2022) · Physical Review Research · doi:10.1103/physrevresearch.4.023225 · Cited by 24
The basics of quantum computing for chemists
Claudino, D. (2022) · International Journal of Quantum Chemistry · doi:10.1002/qua.26990 · Cited by 27
Quantum variational learning for quantum error-correcting codes
Cao, C., Zhang, C., Wu, Z., Grassl, M., & Zeng, B. (2022) · Quantum · doi:10.22331/q-2022-10-06-828 · Cited by 29
Multi-Objective Evolutionary Architecture Search for Parameterized Quantum Circuits
Ding, L., & Spector, L. C. (2023) · Entropy · doi:10.3390/e25010093 · Cited by 26
Classical splitting of parametrized quantum circuits
Tüysüz, C., Clemente, G., Crippa, A., Hartung, T., Kühn, S., & Jansen, K. (2023) · Quantum Machine Intelligence · doi:10.1007/s42484-023-00118-z · Cited by 24
Schrödinger-Heisenberg Variational Quantum Algorithms
Shang, Z.-X., Chen, M.-C., Yuan, X., Lu, C., & Pan, J.-W. (2023) · Physical Review Letters · doi:10.1103/physrevlett.131.060406 · Cited by 30
Influence of HW-SW-Co-Design on Quantum Computing Scalability
Safi, H., Wintersperger, K., & Mauerer, W. (2023) · IEEE International Conference on Quantum Software (QSW) · doi:10.1109/qsw59989.2023.00022 · Cited by 28
Efficient Quantum Recurrent Reinforcement Learning Via Quantum Reservoir Computing
Chen, S. Y.-C. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10446089 · Cited by 28
Approximate complex amplitude encoding algorithm and its application to data classification problems
Mitsuda, N., Ichimura, T., Nakaji, K., Suzuki, Y., Tanaka, T., Raymond, R., Tezuka, H., Onodera, T., & Yamamoto, N. (2024) · Physical Review A · doi:10.1103/physreva.109.052423 · Cited by 24
First quantum computers need smart software
Zeng, W., Johnson, B., Smith, R., Rubin, N., Reagor, M., Ryan, C. A., & Rigetti, C. T. (2017) · Nature · doi:10.1038/549149a · Cited by 24
Simulation of molecular spectroscopy with circuit quantum electrodynamics
Hu, L., Ma, Y.-C., Xu, Y., Wang, W.-T., Ma, Y.-W., Liu, K., Wang, H.-Y., Song, Y.-P., Yung, M.-H., & Sun, L.-Y. (2018) · 中国科学通报：英文版 · doi:10.1016/j.scib.2018.02.001 · Cited by 24
Accelerated variational algorithms for digital quantum simulation of many-body ground states
Lyu, C., Montenegro, V., & Bayat, A. (2020) · Quantum · doi:10.22331/q-2020-09-16-324 · Cited by 27
TILT: Achieving Higher Fidelity on a Trapped-Ion Linear-Tape Quantum Computing Architecture
Wu, X.-C., Debroy, D. M., Ding, Y., Baker, J. M., Alexeev, Y., Brown, K. R., & Chong, F. T. (2021) · IEEE International Symposium on High-Performance Computer Architecture (HPCA) · doi:10.1109/hpca51647.2021.00023 · Cited by 33
QuEst: Graph Transformer for Quantum Circuit Reliability EstimationPreprint
Wang, H., Liu, P., Cheng, J., Liang, Z., Gu, J., Li, Z., Ding, Y., Jiang, W., Shi, Y., Qian, X., et al. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2210.16724 · Cited by 36
Incompressible Navier–Stokes solve on noisy quantum hardware via a hybrid quantum–classical scheme
Song, Z., Deaton, R., Gard, B., & Bryngelson, S. H. (2024) · Computers & Fluids · doi:10.1016/j.compfluid.2024.106507 · Cited by 31
Error-mitigated data-driven circuit learning on noisy quantum hardware
Hamilton, K. E., & Pooser, R. C. (2020) · Quantum Machine Intelligence · doi:10.1007/s42484-020-00021-x · Cited by 23
Quantum embedding description of the Anderson lattice model with the ghost Gutzwiller approximation
Frank, M. S., Lee, T.-H., Bhattacharyya, G., Tsang, P. K. H., Quito, V. L., Dobrosavljević, V., Christiansen, O., & Lanatà, N. (2021) · Physical review. B./Physical review. B · doi:10.1103/physrevb.104.l081103 · Cited by 23
Experimental Quantum Embedding for Machine Learning
Gianani, I., Mastroserio, I., Buffoni, L., Bruno, N., Donati, L., Cimini, V., Barbieri, M., Cataliotti, F. S., & Caruso, F. (2022) · Advanced Quantum Technologies · doi:10.1002/qute.202100140 · Cited by 23
Simulating Static and Dynamic Properties of Magnetic Molecules with Prototype Quantum Computers
Crippa, L., Tacchino, F., Chizzini, M., Aita, A., Grossi, M., Chiesa, A., Santini, P. M., Tavernelli, I., & Carretta, S. (2021) · Magnetochemistry · doi:10.3390/magnetochemistry7080117 · Cited by 24
Symmetry Breaking Slows Convergence of the ADAPT Variational Quantum Eigensolver
Bertels, L. W., Grimsley, H. R., Economou, S. E., Barnes, E., & Mayhall, N. J. (2022) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c00709 · Cited by 28
Haidar, M., Rančić, M. J., Ayral, T., Maday, Y., & Piquemal, J. (2023) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1664 · Cited by 30
Khan, I. T., Tudorovskaya, M., Kirsopp, J. J. M., Ramo, D. M., Warrier, P. W., Papanastasiou, D. K., & Singh, R. (2023) · The Journal of Chemical Physics · doi:10.1063/5.0144680 · Cited by 25
Two Algorithms for Excited-State Quantum Solvers: Theory and Application to EOM-UCCSD
Kim, Y., & Krylov, A. I. (2023) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.3c02480 · Cited by 25
Quantum simulation of bosons with the contracted quantum eigensolver
Wang, Y., Sager-Smith, L. M., & Mazziotti, D. A. (2023) · New Journal of Physics · doi:10.1088/1367-2630/acf9c3 · Cited by 24
PANSATZ: pulse-based ansatz for variational quantum algorithms
Meirom, D., & Frankel, S. H. (2023) · Frontiers in Quantum Science and Technology · doi:10.3389/frqst.2023.1273581 · Cited by 23
Effect of Nonunital Noise on Random-Circuit Sampling
Fefferman, B., Ghosh, S., Gullans, M. J., Kuroiwa, K., & Sharma, K. (2024) · PRX Quantum · doi:10.1103/prxquantum.5.030317 · Cited by 40
Quantum approximate Bayesian computation for NMR model inference
Sels, D., Dashti, H., Mora, S., Demler, O., & Demler, E. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0198-x · Cited by 23
Variationally scheduled quantum simulation
Matsuura, S., Buck, S., Senicourt, V., & Zaribafiyan, A. (2021) · Physical Review A · doi:10.1103/physreva.103.052435 · Cited by 27
Cryogenic Electronics and Quantum Information Processing
Holmes, D. S. (2021) · doi:10.1109/irds54852.2021.00012 · Cited by 24
Leveraging randomized compiling for the quantum imaginary-time-evolution algorithm
Ville, J.-L., Morvan, A., Hashim, A., Naik, R., Lu, M., Mitchell, B., Kreikebaum, J. M., O’Brien, K. P., Wallman, J. J., Hincks, I., et al. (2022) · Physical Review Research · doi:10.1103/physrevresearch.4.033140 · Cited by 30
Quantum Combinatorial Optimization in the NISQ Era: A Systematic Mapping Study
Gemeinhardt, F., Garmendia, A., Wimmer, M., Weder, B., & Leymann, F. (2024) · ACM Computing Surveys · doi:10.1145/3620668 · Cited by 22
“The variational form of the parametrized quantum circuit, also called ansatz, is important in this regard, where standard approaches (e.g., References [191, 202]) comprise the iterative alternating application of non-commuting single qubit rotations and controlled two-qubit operations.”
MethodReducing the Amount of Single‐Qubit Rotations in VQE and Related Algorithms
Rasmussen, S. E., Loft, N. J. S., Bækkegaard, T., Kues, M., & Zinner, N. T. (2020) · Advanced Quantum Technologies · doi:10.1002/qute.202000063 · Cited by 22
Potential of quantum scientific machine learning applied to weather modeling
Jaderberg, B., Gentile, A. A., Ghosh, A., Elfving, V. E., Jones, C. R., Vodola, D., Manobianco, J., & Weiß, H. (2024) · Physical Review A · doi:10.1103/physreva.110.052423 · Cited by 22
Towards a variational Jordan–Lee–Preskill quantum algorithm
Liu, J., Li, Z., Zheng, H., Yuan, X., & Sun, J. (2022) · Machine Learning Science and Technology · doi:10.1088/2632-2153/aca06b · Cited by 32
Quantum computation of phase transition in the massive Schwinger model
Thompson, S., & Siopsis, G. (2022) · Quantum Science and Technology · doi:10.1088/2058-9565/ac5f5a · Cited by 22
The Cost of Improving the Precision of the Variational Quantum Eigensolver for Quantum Chemistry
Miháliková, I., Pivoluska, M., Plesch, M., Friák, M., Nagaj, D., & Šob, M. (2022) · Nanomaterials · doi:10.3390/nano12020243 · Cited by 25
Anomalous Josephson current through a driven double quantum dot
Ortega-Taberner, C., Jauho, A., & Paaske, J. (2023) · Physical review. B./Physical review. B · doi:10.1103/physrevb.107.115165 · Cited by 22
Making existing software quantum safe: A case study on IBM Db2
Zhang, L., Miranskyy, A. V., Rjaibi, W., Stager, G., Gray, M., & Peck, J. S. (2023) · Information and Software Technology · doi:10.1016/j.infsof.2023.107249 · Cited by 25
Efficient variational synthesis of quantum circuits with coherent multi-start optimization
Nemkov, N. A., Kiktenko, E. O., Luchnikov, I. A., & Fedorov, A. K. (2023) · Quantum · doi:10.22331/q-2023-05-04-993 · Cited by 27
Quantum simulation of battery materials using ionic pseudopotentials
Zini, M. S., Delgado, A., dos Reis, R., Casares, P. A. M., Mueller, J. E., Voigt, A.-C., & Arrazola, J. M. (2023) · Quantum · doi:10.22331/q-2023-07-10-1049 · Cited by 28
Using Differential Evolution to avoid local minima in Variational Quantum Algorithms
Faílde, D., Viqueira, J. D., Juane, M. M., & Gómez, A. (2023) · Scientific Reports · doi:10.1038/s41598-023-43404-3 · Cited by 22
Distributed quantum architecture search
Situ, H., He, Z., Zheng, S., & Li, L. (2024) · Physical Review A · doi:10.1103/physreva.110.022403 · Cited by 23
Calibrating the role of entanglement in variational quantum circuits
Nakhl, A. C., Quella, T., & Usman, M. (2024) · Physical Review A · doi:10.1103/physreva.109.032413 · Cited by 22
Group-theoretic error mitigation enabled by classical shadows and symmetries
Zhao, A., & Miyake, A. (2024) · npj Quantum Information · doi:10.1038/s41534-024-00854-5 · Cited by 34
Neural quantum embedding: Pushing the limits of quantum supervised learning
Hur, T., Araujo, I. F., & Park, D. K. (2024) · Physical Review A · doi:10.1103/physreva.110.022411 · Cited by 29
Maciejewski, F. B., Hadfield, S., Hall, B., Hodson, M. J., Dupont, M., Evert, B., Sud, J., Alam, M. S., Wang, Z., Jeffrey, S., et al. (2024) · Physical Review Applied · doi:10.1103/physrevapplied.22.044074 · Cited by 28
Quantum computing quantum Monte Carlo algorithm
Zhang, Y., Huang, Y., Sun, J., Lv, D., & Yuan, X. (2025) · Physical Review A · doi:10.1103/jt8s-hzhd · Cited by 27
Hardware-efficient fermionic simulation with a cavity–QED system
Zhu, G., Subaşı, Y., Whitfield, J. D., & Hafezi, M. (2018) · npj Quantum Information · doi:10.1038/s41534-018-0065-3 · Cited by 22
Addressing Temporal Variations in Qubit Quality Metrics for Parameterized Quantum Circuits
Alam, M., Saki, A. A., & Ghosh, S. (2019) · International Symposium on Low Power Electronics and Design (ISLPED) · doi:10.1109/islped.2019.8824907 · Cited by 23
A diagrammatic approach to variational quantum ansatz construction
Herasymenko, Y., & O’Brien, T. E. (2021) · Quantum · doi:10.22331/q-2021-12-02-596 · Cited by 22
Application of Pontryagin's minimum principle to Grover's quantum search problem
Lin, C., Wang, Y., Kolesov, G., & Kalabic, U. V. (2019) · Physical Review A · doi:10.1103/physreva.100.022327 · Cited by 22
Collective optimization for variational quantum eigensolvers
Zhang, D.-B., & Yin, T. (2020) · Physical Review A · doi:10.1103/physreva.101.032311 · Cited by 24
Quantum Fredkin gate based on synthetic three-body interactions in superconducting circuits
Feng, W., & Wang, D. (2020) · Physical Review A · doi:10.1103/physreva.101.062312 · Cited by 22
Chemistry beyond the Hartree–Fock energy via quantum computed moments
Jones, M. A., Vallury, H. J., Hill, C. D., & Hollenberg, L. C. L. (2022) · Scientific Reports · doi:10.1038/s41598-022-12324-z · Cited by 25
Protocols for trainable and differentiable quantum generative modeling
Kyriienko, O., Paine, A. E., & Elfving, V. E. (2024) · Physical Review Research · doi:10.1103/physrevresearch.6.033291 · Cited by 39
FLIP: A flexible initializer for arbitrarily-sized parametrized quantum circuitsPreprint
Sauvage, F., Sim, S., Kunitsa, A. A., Simon, W., Mauri, M., & Perdomo‐Ortiz, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.08572 · Cited by 32
Enhanced connectivity of quantum hardware with digital-analog control
Galicia, A., Ramon, B., Solano, E., & Sanz, M. (2020) · Physical Review Research · doi:10.1103/physrevresearch.2.033103 · Cited by 22
Wan, D., Couet, S., Piao, X., Souriau, L., Canvel, Y., Tsvetanova, D., Vangoidsenhoven, D., Thiam, A., Pacco, A., Potočnik, A., et al. (2021) · Japanese Journal of Applied Physics · doi:10.35848/1347-4065/abe5bb · Cited by 20
DRLQ: A Deep Reinforcement Learning-based Task Placement for Quantum Cloud Computing
Nguyen, H. T., Usman, M., & Buyya, R. (2024) · IEEE ... International Conference on Cloud Computing · doi:10.1109/cloud62652.2024.00060 · Cited by 22
A hybrid quantum-classical framework for computational fluid dynamics
Ye, C.-C., An, N.-B., Ma, T.-Y., Dou, M., Bai, W., Sun, D.-J., Chen, Z., & Guo, G. (2024) · Physics of Fluids · doi:10.1063/5.0238193 · Cited by 26
Toward Cosmological Simulations of Dark Matter on Quantum Computers
Mocz, P., & Szasz, A. (2021) · The Astrophysical Journal · doi:10.3847/1538-4357/abe6ac · Cited by 21
Generic detection-based error mitigation using quantum autoencoders
Zhang, X., Kong, W., Farooq, M. U., Yung, M., Guo, G., & Wang, X. (2021) · Physical Review A · doi:10.1103/physreva.103.l040403 · Cited by 24
Quantum Simulation of Molecules in Solution
Castaldo, D., Jahangiri, S., Delgado, A., & Corni, S. (2022) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c00974 · Cited by 23
Demonstration of Density Matrix Exponentiation Using a Superconducting Quantum Processor
Kjærgaard, M., Schwartz, M. E., Greene, A., Samach, G., Bengtsson, A., O’Keeffe, M. F., McNally, C. M., Braumüller, J., Kim, D. K., Krantz, P., et al. (2022) · Physical Review X · doi:10.1103/physrevx.12.011005 · Cited by 24
Unsupervised detection of decoupled subspaces: Many-body scars and beyond
Szołdra, T., Sierant, P., Lewenstein, M., & Zakrzewski, J. (2022) · Physical review. B./Physical review. B · doi:10.1103/physrevb.105.224205 · Cited by 25
Simulating time evolution with fully optimized single-qubit gates on parametrized quantum circuits
Wada, K., Raymond, R., Ohnishi, Y., Kaminishi, E., Sugawara, M., Yamamoto, N., & Watanabe, H. C. (2022) · Physical Review A · doi:10.1103/physreva.105.062421 · Cited by 24
Metadata
- Authors
- Kandala, A., Mezzacapo, A., Temme, K., Takita, M., Brink, M., Chow, J. M., & Gambetta, J. M.
- Affiliations
- IBM (United States) (US)
- Corresponding author
- Kandala, Abhinav
- Mezzacapo, Antonio
- ORCID
- Venue
- Nature
- Published
- 2017-09
- Volume
- 549
- Issue
- 7671
- Pages
- 242-246
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W2755255888
- Cited by
- 2,982
- References
- 43
- Citations by year
- 2026: 334 · 2025: 423 · 2024: 439 · 2023: 419 · 2022: 386 · 2021: 405 · 2020: 284 · 2019: 173 · 2018: 101 · 2017: 15
- FWCI
- 93.44
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum many-body systems
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Keywords
- variational quantum eigensolver, quantum simulation, ground state energy, small molecules, lithium hydride, beryllium hydride, Heisenberg model, quantum magnetism, superconducting qubits, stochastic optimization, quantum chemistry, quantum error correction, noise modeling
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
