Measuring the capabilities of quantum computers

Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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Quantum Computing in the NISQ era and beyond

Preskill, J. (2018) · Quantum · doi:10.22331/q-2018-08-06-79 · Cited by 8,830

Quantum supremacy using a programmable superconducting processor

Arute, F., Arya, K., Babbush, R., Bacon, D., Bardin, J. C., Barends, R., Biswas, R., Boixo, S., Brandao, F. G. S. L., Buell, D. A., et al. (2019) · Nature · doi:10.1038/s41586-019-1666-5 · Cited by 7,720

Improved simulation of stabilizer circuits

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Quantum computing with realistically noisy devices

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Characterizing quantum supremacy in near-term devices

Boixo, S., Isakov, S. V., Smelyanskiy, V. N., Babbush, R., Ding, N., Jiang, Z., Bremner, M. J., Martinis, J. M., & Neven, H. (2018) · Nature Physics · doi:10.1038/s41567-018-0124-x · Cited by 1,175

Randomized benchmarking of quantum gates

Knill, E., Leibfried, D., Reichle, R., Britton, J., Blakestad, R. B., Jost, J. D., Langer, C., Ozeri, R., Seidelin, S., & Wineland, D. J. (2008) · Physical Review A · doi:10.1103/physreva.77.012307 · Cited by 1,019

Scalable and Robust Randomized Benchmarking of Quantum Processes

Magesan, E., Gambetta, J., & Emerson, J. (2011) · Physical Review Letters · doi:10.1103/physrevlett.106.180504 · Cited by 797

Quantum Theory of Electrical Transport Phenomena

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A simple formula for the average gate fidelity of a quantum dynamical operation

Nielsen, M. A. (2002) · Physics Letters A · doi:10.1016/s0375-9601(02)01272-0 · Cited by 725

A Race-Track Trapped-Ion Quantum Processor

Moses, S. A., Baldwin, C. H., Allman, M. S., Ancona, R., Ascarrunz, L., Barnes, C., Bartolotta, J., Bjork, B., Blanchard, P., Bohn, M., et al. (2023) · Physical Review X · doi:10.1103/physrevx.13.041052 · Cited by 391

Application-Oriented Performance Benchmarks for Quantum Computing

Lubinski, T., Johri, S., Varosy, P., Coleman, J., Zhao, L., Necaise, J., Baldwin, C. H., Mayer, K., & Proctor, T. J. (2023) · IEEE Transactions on Quantum Engineering · doi:10.1109/tqe.2023.3253761 · Cited by 242

Benchmarking a trapped-ion quantum computer with 30 qubits

Chen, J.-S., Nielsen, E., Ebert, M., Inlek, V., Wright, K., Chaplin, V., Maksymov, A., Páez, E., Poudel, A., Maunz, P., et al. (2024) · Quantum · doi:10.22331/q-2024-11-07-1516 · Cited by 103

Near-term quantum computing techniques: Variational quantum algorithms, error mitigation, circuit compilation, benchmarking and classical simulation

Huang, H.-L., Xu, X., Guo, C., Tian, G., Wei, S.-J., Sun, X., Bao, W., & Long, G. (2023) · Science China Physics Mechanics and Astronomy · doi:10.1007/s11433-022-2057-y · Cited by 111

Quantum Volume in Practice: What Users Can Expect From NISQ Devices

Pelofske, E., Bärtschi, A., & Eidenbenz, S. (2022) · IEEE Transactions on Quantum Engineering · doi:10.1109/tqe.2022.3184764 · Cited by 90

Benchmarking quantum computers

Proctor, T. J., Young, K. C., Baczewski, A., & Blume-Kohout, R. (2025) · Nature Reviews Physics · doi:10.1038/s42254-024-00796-z · Cited by 116

Scalable Randomized Benchmarking of Quantum Computers Using Mirror Circuits

Proctor, T., Seritan, S., Rudinger, K., Nielsen, E., Blume-Kohout, R., & Young, K. (2022) · Physical Review Letters · doi:10.1103/physrevlett.129.150502 · Cited by 68

Density-Matrix Renormalization Group Algorithm for Simulating Quantum Circuits with a Finite Fidelity

Ayral, T., Louvet, T., Zhou, Y., Lambert, C., Stoudenmire, E. M., & Waintal, X. (2023) · PRX Quantum · doi:10.1103/prxquantum.4.020304 · Cited by 66

Benchmarking Quantum Coprocessors in an Application-Centric, Hardware-Agnostic, and Scalable Way

Martiel, S., Ayral, T., & Allouche, C. (2021) · IEEE Transactions on Quantum Engineering · doi:10.1109/tqe.2021.3090207 · Cited by 60

Benchmarking highly entangled states on a 60-atom analogue quantum simulator

Shaw, A. L., Chen, Z., Choi, J., Mark, D. K., Scholl, P., Finkelstein, R., Elben, A., Choi, S., & Endres, M. (2024) · Nature · doi:10.1038/s41586-024-07173-x · Cited by 64

Suppressing Quantum Circuit Errors Due to System Variability

Nation, P. D., & Treinish, M. (2023) · PRX Quantum · doi:10.1103/prxquantum.4.010327 · Cited by 56

Quantum‐Classical Hybrid Genetic Evolutionary Algorithm for Topology Optimization of Continuum Structures

Wang, Z., & Wang, X. (2025) · International Journal for Numerical Methods in Engineering · doi:10.1002/nme.70073 · Cited by 56

Re-examining the quantum volume test: Ideal distributions, compiler optimizations, confidence intervals, and scalable resource estimations

Baldwin, C. H., Mayer, K., Brown, N. C., Ryan-Anderson, C., & Hayes, D. (2022) · Quantum · doi:10.22331/q-2022-05-09-707 · Cited by 54

Verifiable Blind Quantum Computing with Trapped Ions and Single Photons

Drmota, P., Nadlinger, D. P., Main, D., Nichol, B. C., Ainley, E. M., Leichtle, D., Mantri, A., Kashefi, E., Srinivas, R., Araneda, G., et al. (2024) · Physical Review Letters · doi:10.1103/physrevlett.132.150604 · Cited by 51

Error-Robust Quantum Logic Optimization Using a Cloud Quantum Computer Interface

Carvalho, A. R. R., Ball, H., Biercuk, M. J., Hush, M., & Thomsen, F. (2021) · Physical Review Applied · doi:10.1103/physrevapplied.15.064054 · Cited by 59

Quantum Fourier Transform Using Dynamic Circuits

Bäumer, E., Tripathi, V., Seif, A., Lidar, D. A., & Wang, D. S. (2024) · Physical Review Letters · doi:10.1103/physrevlett.133.150602 · Cited by 49

A Taxonomy of Small Markovian Errors

Blume-Kohout, R., Silva, M. P. D., Nielsen, E., Proctor, T. J., Rudinger, K., Sarovar, M., & Young, K. (2022) · PRX Quantum · doi:10.1103/prxquantum.3.020335 · Cited by 53

Computational Power of Random Quantum Circuits in Arbitrary Geometries

(2025) · doi:10.1103/physrevx.15.021052 · Cited by 45

Experimental Benchmarking of an Automated Deterministic Error-Suppression Workflow for Quantum Algorithms

Mundada, P., Barbosa, A., Maity, S., Wang, Y., Merkh, T., Stace, T. M., Nielson, F. F., Carvalho, A. R. R., Hush, M., Biercuk, M. J., et al. (2023) · Physical Review Applied · doi:10.1103/physrevapplied.20.024034 · Cited by 51

Urban quantum leap: A comprehensive review and analysis of quantum technologies for smart cities

Bonab, A. B., Fedele, M., Formisano, V., & Rudko, I. (2023) · Cities · doi:10.1016/j.cities.2023.104459 · Cited by 47

Quantum utility – definition and assessment of a practical quantum advantage

Herrmann, N., Arya, D., Doherty, M. W., Mingare, A., Pillay, J. C., Preis, F., & Prestel, S. (2023) · IEEE International Conference on Quantum Software (QSW) · doi:10.1109/qsw59989.2023.00028 · Cited by 53

Optimized SWAP networks with equivalent circuit averaging for QAOA

Hashim, A., Rines, R., Omole, V., Naik, R., Kreikebaum, J. M., Santiago, D., Chong, F. T., Siddiqi, I. A., & Gokhale, P. (2022) · Physical Review Research · doi:10.1103/physrevresearch.4.033028 · Cited by 46

Variational Quantum Pulse Learning

Liang, Z., Wang, H., Cheng, J., Ding, Y., Ren, H., Gao, Z., Hu, Z., Boning, D. S., Qian, X., Han, S., et al. (2022) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce53715.2022.00078 · Cited by 44

Protecting expressive circuits with a quantum error detection code

Self, C. N., Benedetti, M., & Amaro, D. (2024) · Nature Physics · doi:10.1038/s41567-023-02282-2 · Cited by 31

Learning Correlated Noise in a 39-Qubit Quantum Processor

Harper, R., & Flammia, S. T. (2023) · PRX Quantum · doi:10.1103/prxquantum.4.040311 · Cited by 33

Practical Introduction to Benchmarking and Characterization of Quantum Computers

Hashim, A., Nguyen, L. B., Goss, N., Marinelli, B., Naik, R., Chistolini, T., Hines, J., Marceaux, J. P., Kim, Y., Gokhale, P., et al. (2025) · PRX Quantum · doi:10.1103/prxquantum.6.030202 · Cited by 85

Accelerating quantum computer developments

Alberts, G., Rol, M. A., Last, T., Broer, B. W., Bultink, C. C., Rijlaarsdam, M. S. C., & Hauwermeiren, A. E. V. (2021) · EPJ Quantum Technology · doi:10.1140/epjqt/s40507-021-00107-w · Cited by 30

HamLib: A library of Hamiltonians for benchmarking quantum algorithms and hardware

Sawaya, N. P. D., Marti-Dafcik, D., Ho, Y., Tabor, D. P., Bernal, D. E., Magann, A. B., Premaratne, S., Dubey, P. K., Matsuura, A. Y., Bishop, N. C., et al. (2024) · Quantum · doi:10.22331/q-2024-12-11-1559 · Cited by 39

SoK: Benchmarking the Performance of a Quantum Computer

Wang, J., Guo, G., & Shan, Z. (2022) · Entropy · doi:10.3390/e24101467 · Cited by 27

Impact of time-correlated noise on zero-noise extrapolation

Schultz, K., LaRose, R., Mari, A., Quiroz, G., Shammah, N., Clader, B. D., & Zeng, W. J. (2022) · Physical Review A · doi:10.1103/physreva.106.052406 · Cited by 28

Rapid solution of logical equivalence problems by quantum computation algorithm

Zidan, M., Hegazy, S. F., Abdel‐Aty, M., & Obayya, S. S. A. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2022.109844 · Cited by 25

A quantum hamiltonian simulation benchmark

Dong, Y., Whaley, K. B., & Lin, L. (2022) · npj Quantum Information · doi:10.1038/s41534-022-00636-x · Cited by 31

Quantum circuit fidelity estimation using machine learning

Vadali, A., Kshirsagar, R., Shyamsundar, P., & Perdue, G. (2023) · Quantum Machine Intelligence · doi:10.1007/s42484-023-00121-4 · Cited by 24

Demonstrating Scalable Randomized Benchmarking of Universal Gate Sets

Hines, J., Lu, M., Naik, R., Hashim, A., Ville, J.-L., Mitchell, B., Kreikebaum, J. M., Santiago, D., Seritan, S., Nielsen, E., et al. (2023) · Physical Review X · doi:10.1103/physrevx.13.041030 · Cited by 32

Scaling of energy and power in a large quantum battery-charger model

Gao, L., Cheng, C., He, W., Mondaini, R., Guan, X.-W., & Lin, H. (2022) · Physical Review Research · doi:10.1103/physrevresearch.4.043150 · Cited by 22

Testing Platform-Independent Quantum Error Mitigation on Noisy Quantum Computers

Russo, V., Mari, A., Shammah, N., LaRose, R., & Zeng, W. J. (2023) · IEEE Transactions on Quantum Engineering · doi:10.1109/tqe.2023.3305232 · Cited by 30

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

Algorithmic QUBO formulations for k -SAT and hamiltonian cycles

Nüßlein, J., Gabor, T., Linnhoff–Popien, C., & Feld, S. (2022) · Genetic and Evolutionary Computation Conference Companion (GECCO Companion) · doi:10.1145/3520304.3533952 · Cited by 18

Realizing symmetry-protected topological phases in a spin-1/2 chain with next-nearest-neighbor hopping on superconducting qubits

Tan, A. T. K., Sun, S.-N., Tazhigulov, R. N., Chan, G. K., & Minnich, A. J. (2023) · Physical Review A · doi:10.1103/physreva.107.032614 · Cited by 22

Learning how to dynamically decouple by optimizing rotational gates

Rahman, A., Egger, D. J., & Arenz, C. (2024) · Physical Review Applied · doi:10.1103/physrevapplied.22.054074 · Cited by 26

In complexity we trust: A systematic literature review of urban quantum technologies

Bonab, A. B., Fedele, M., Formisano, V., & Rudko, I. (2023) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2023.122642 · Cited by 17

Fair Sampling Error Analysis on NISQ Devices

Golden, J. K., Bärtschi, A., O’Malley, D., & Eidenbenz, S. (2022) · ACM Transactions on Quantum Computing · doi:10.1145/3510857 · Cited by 17

Volumetric Benchmarking of Error Mitigation with Qermit

Cîrstoiu, C., Dilkes, S., Mills, D., Sivarajah, S., & Duncan, R. (2023) · Quantum · doi:10.22331/q-2023-07-13-1059 · Cited by 16

Benchmarking Quantum Gates and Circuits

Tripathi, V., Kowsari, D., Saurav, K., Zhang, H., Levenson-Falk, E. M., & Lidar, D. A. (2025) · Chemical Reviews · doi:10.1021/acs.chemrev.4c00870 · Cited by 22

Benchmarking quantum logic operations relative to thresholds for fault tolerance

Hashim, A., Seritan, S., Proctor, T. J., Rudinger, K., Goss, N., Naik, R., Kreikebaum, J. M., Santiago, D., & Siddiqi, I. A. (2023) · npj Quantum Information · doi:10.1038/s41534-023-00764-y · Cited by 23

Using Shor’s algorithm on near term Quantum computers: a reduced version

Rossi, M. S., Asproni, L., Caputo, D., Rossi, S., Cusinato, A., Marini, R., Agosti, A., & Magagnini, M. (2022) · Quantum Machine Intelligence · doi:10.1007/s42484-022-00072-2 · Cited by 19

Highly optimized quantum circuits synthesized via data-flow engines

Rakyta, P., Morse, G., Nádori, J., Majnay-Takács, Z., Mencer, O., & Zimborás, Z. (2024) · Journal of Computational Physics · doi:10.1016/j.jcp.2024.112756 · Cited by 13

Error estimation in current noisy quantum computers

Aseginolaza, U., Sobrino, N., Sobrino, G., Jornet-Somoza, J., & Borge, J. (2024) · Quantum Information Processing · doi:10.1007/s11128-024-04384-z · Cited by 12

Defining Best Practices for Quantum Benchmarks

Amico, M., Zhang, H., Jurcevic, P., Bishop, L. S., Nation, P., Wack, A., & McKay, D. C. (2023) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce57702.2023.00084 · Cited by 16

Fully Scalable Randomized Benchmarking Without Motion Reversal

Hines, J., Hothem, D., Blume-Kohout, R., Whaley, B., & Proctor, T. (2024) · PRX Quantum · doi:10.1103/prxquantum.5.030334 · Cited by 12

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

Efficient Characterization of Qudit Logical Gates with Gate Set Tomography Using an Error-Free Virtual Z Gate Model

Cao, S., Lall, D., Bakr, M. S., Campanaro, G., Fasciati, S. D., Wills, J., Chidambaram, V., Shteynas, B., Rungger, I., & Leek, P. (2024) · Physical Review Letters · doi:10.1103/physrevlett.133.120802 · Cited by 14

Quantum error reduction with deep neural network applied at the post-processing stage

Zhukov, A., & Pogosov, W. V. (2022) · Quantum Information Processing · doi:10.1007/s11128-022-03433-9 · Cited by 13

Empirical Learning of Dynamical Decoupling on Quantum Processors

Tong, C., Zhang, H., & Pokharel, B. (2025) · PRX Quantum · doi:10.1103/h7pq-s159 · Cited by 10

Nanoionics enabled atomic point contact construction and quantum conductance effects

Gao, R., Ye, X., Hu, C., Zhang, Z., Ji, X., Zhang, Y., Meng, X., Yang, H., Zhu, X., & Li, R. (2024) · Materials Horizons · doi:10.1039/d4mh00916a · Cited by 9

An accurate and efficient analytic model of fidelity under depolarizing noise oriented to large scale quantum system design

Escofet, P., Rodrigo, S., García-Sáez, A., Alarcón, E., Abadal, S., & Almudéver, C. G. (2025) · Quantum Science and Technology · doi:10.1088/2058-9565/aded2f · Cited by 12

A 98-qubit trapped-ion quantum computer with all-to-all connectivity

Ransford, A., Allman, M. S., Arkinstall, J., Campora, J. P., Cooper, S. F., Delaney, R. D., Dreiling, J. M., Estey, B., Figgatt, C., Hall, A., et al. (2026) · Nature · doi:10.1038/s41586-026-10676-4 · Cited by 16

Benchmarking Quantum Generative Learning: A Study on Scalability and Noise Resilience using QUARK

Kiwit, F. J., Wolf, M. A., Marso, M., Ross, P., Lorenz, J. M., Riofrío, C. A., & Luckow, A. (2024) · Künstliche Intell. · doi:10.1007/s13218-024-00864-7 · Cited by 9

Benchmarking quantum computers: towards a standard performance evaluation approach

Acuaviva, A., Aguirre, D., Peña, R., & Sanz, M. (2026) · Quantum Science and Technology · doi:10.1088/2058-9565/ae4269 · Cited by 24

Current Trends and Advances in Quantum Navigation for Maritime Applications: A Comprehensive Review

Sambataro, O., Costanzi, R., Alves, J., Caiti, A., Paglierani, P., Petroccia, R., & Munafò, A. (2025) · IEEE Journal of Oceanic Engineering · doi:10.1109/joe.2025.3538941 · Cited by 17

A Comprehensive Cross-Model Framework for Benchmarking the Performance of Quantum Hamiltonian Simulations

Chatterjee, A., Rappaport, S., Giri, A., Johri, S., Proctor, T. J., Neira, D. E. B., Sathe, P., & Lubinski, T. (2025) · IEEE Transactions on Quantum Engineering · doi:10.1109/tqe.2025.3558090 · Cited by 12

Dynamic Tariff Adjustment for Electric Vehicle Charging in Renewable-Rich Smart Grids: A Multi-Factor Optimization Approach to Load Balancing and Cost Efficiency

Wang, D., Chen, X., Liu, X., Li, Y., Piao, Z., & Li, H. (2025) · Energies · doi:10.3390/en18164283 · Cited by 7

Predictive Models from Quantum Computer Benchmarks

Hothem, D., Hines, J., Nataraj, K., Blume-Kohout, R., & Proctor, T. J. (2023) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce57702.2023.00086 · Cited by 7

Learning to rank quantum circuits for hardware-optimized performance enhancement

Hartnett, G. S., Barbosa, A., Mundada, P., Hush, M., Biercuk, M. J., & Baum, Y. (2024) · Quantum · doi:10.22331/q-2024-11-27-1542 · Cited by 12

Efficient flexible characterization of quantum processors with nested error models

Nielsen, E., Rudinger, K., Proctor, T. J., Young, K. C., & Blume-Kohout, R. (2021) · New Journal of Physics · doi:10.1088/1367-2630/ac20b9 · Cited by 8

Towards a Quantum Benchmark Suite with Standardized KPIs

Becker, C. K., Tcholtchev, N., Gheorghe-Pop, I.-D., Bock, S., Seidel, R., & Hauswirth, M. (2022) · IEEE International Conference on Software Architecture Companion (ICSA-C) · doi:10.1109/icsa-c54293.2022.00038 · Cited by 7

Quantum circuit debugging and sensitivity analysis via local inversions

Calderon-Vargas, F. A., Proctor, T. J., Rudinger, K., & Sarovar, M. (2023) · Quantum · doi:10.22331/q-2023-02-09-921 · Cited by 8

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

Non-Markovian quantum gate set tomography

Li, Z., Zheng, C., Meng, F., Zeng, H., Luan, T., Zhang, Z., & Yu, X. (2024) · Quantum Science and Technology · doi:10.1088/2058-9565/ad3d80 · Cited by 6

Calibration and Performance Evaluation of a Superconducting Quantum Processor in an HPC Center

Deng, X., Pogorzalek, S., Vigneau, F., Yang, P., Schulz, M., & Schulz, L. B. (2024) · ISC High Performance (ISC) · doi:10.23919/isc.2024.10528924 · Cited by 6

Field-based formalism for calculating multiqubit exchange-coupling rates for transmon qubits

Khan, G., & Roth, T. E. (2024) · Physical Review Applied · doi:10.1103/physrevapplied.22.064084 · Cited by 6

Simulating Floquet scrambling circuits on trapped-ion quantum computers

Seki, K., Kikuchi, Y., Hayata, T., & Yunoki, S. (2025) · Physical Review Research · doi:10.1103/physrevresearch.7.023032 · Cited by 13

Unifying Non-Markovian Characterization with an Efficient and Self-Consistent Framework

White, G. A. L., Jurcevic, P., Hill, C. D., & Modi, K. K. (2025) · Physical Review X · doi:10.1103/physrevx.15.021047 · Cited by 6

Qubit Assignment Using Time Reversal

Peters, E., Shyamsundar, P., Li, A. C. Y., & Perdue, G. (2022) · PRX Quantum · doi:10.1103/prxquantum.3.040333 · Cited by 8

Robustness of chaotic behavior in iterated quantum protocols

Portik, A., Kálmán, O., Jex, I., & Kiss, T. (2024) · Physical Review A · doi:10.1103/physreva.109.042410 · Cited by 5

Measuring error rates of mid-circuit measurements

Hothem, D., Hines, J., Baldwin, C., Gresh, D., Blume-Kohout, R., & Proctor, T. (2025) · Nature Communications · doi:10.1038/s41467-025-60923-x · Cited by 5

QuAS: Quantum Application Score for Benchmarking the Utility of Quantum Computers

Mesman, K. J., van der Schoot, W., Möller, M., & Neumann, N. M. P. (2024) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce60285.2024.00111 · Cited by 5

Antenna Array Analysis Through Universal Quantum Computing Processors—A Study on Noise Modeling and Impact

Tosi, L., & Rocca, P. (2023) · IEEE Transactions on Microwave Theory and Techniques · doi:10.1109/tmtt.2023.3344878 · Cited by 5

Construction and volumetric benchmarking of quantum computing noise models

Weber, T., Borras, K., Jansen, K., Krücker, D., & Riebisch, M. (2024) · Physica Scripta · doi:10.1088/1402-4896/ad406c · Cited by 8

A Theory of Direct Randomized Benchmarking

Polloreno, A., Carignan-Dugas, A., Hines, J., Blume-Kohout, R., Young, K. C., & Proctor, T. J. (2025) · Quantum · doi:10.22331/q-2025-09-05-1848 · Cited by 15

AppQSim: Application-oriented benchmarks for Hamiltonian simulation on a quantum computer

Granet, E., & Dreyer, H. (2025) · Physical Review Research · doi:10.1103/qt55-7d6r · Cited by 9

Cross-platform comparison of arbitrary quantum processes

Zheng, C., Yu, X., & Wang, K. (2024) · npj Quantum Information · doi:10.1038/s41534-023-00797-3 · Cited by 7

Characterizing non-Markovian and coherent errors in quantum simulation

Prakash, A., & Madhusudhana, B. H. (2024) · Physical Review Research · doi:10.1103/physrevresearch.6.043127 · Cited by 4

Q‐fid: Quantum Circuit Fidelity Improvement with LSTM Networks

Mao, Y., Shresthamali, S., & Kondo, M. (2025) · Advanced Quantum Technologies · doi:10.1002/qute.202500022 · Cited by 7

Testing quantum computers with the protocol of quantum state matching

Ortega, A., Kálmán, O., & Kiss, T. (2023) · Physica Scripta · doi:10.1088/1402-4896/acb2ff · Cited by 5

Perturbative analysis of quasiperiodic patterning of transmon quantum computers: Enhancement of many-body localization

Varvelis, E., & Divincenzo, D. P. (2024) · Physical review. B./Physical review. B · doi:10.1103/physrevb.109.144201 · Cited by 8

Simulating sparse SYK model with a randomized algorithm on a trapped-ion quantum computer

Granet, E., Kikuchi, Y., Dreyer, H., & Rinaldi, E. (2026) · npj Quantum Information · doi:10.1038/s41534-026-01206-1 · Cited by 3

Joint encryption and error correction for secure quantum communication

Jha, N., Parakh, A., & Subramaniam, M. (2024) · Scientific Reports · doi:10.1038/s41598-024-75212-8 · Cited by 3

Scalable Full-Stack Benchmarks for Quantum Computers

Hines, J., & Proctor, T. J. (2024) · IEEE Transactions on Quantum Engineering · doi:10.1109/tqe.2024.3404502 · Cited by 12

Cyber Security in the Post Quantum Computer Era: Threats and Perspectives

Iqbal, M. S., Sajid, A., & Malik, R. (2025) · Information systems engineering and management · doi:10.1007/978-3-031-81481-5_2 · Cited by 3

Analysis on noise impact in algorithm-based quantum computing benchmark

Wang, J., Zhang, M., Lai, J., Zhao, W., & Zhang, H. (2022) · International Conference on Optical Communications and Networks (ICOCN) · doi:10.1109/icocn55511.2022.9901092 · Cited by 3

A Practical Framework for Assessing the Performance of Observable Estimation in Quantum Simulation

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Magic states in the asymmetric quantum Rabi model

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Distributing Quantum Computations, Shot-Wise

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Scalable Programming Workflows for Validation of Quantum Computers

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Qubit Condensation for Assessing Efficacy of Molecular Simulation on Quantum Computers

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A Testing Pipeline for Quantum Computing Applications

Becker, C. K., Gheorghe-Pop, I.-D., & Tcholtchev, N. (2023) · IEEE International Conference on Quantum Software (QSW) · doi:10.1109/qsw59989.2023.00016 · Cited by 2

A user-centric quantum benchmarking test suite and evaluation framework

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Quantum Gates for Electronics Engineers

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Learning a Quantum Computer's Capability

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Benchmarking the computational power of quantum computersPreprint

Proctor, T. J., Hart, O., Maupin, O. G., Girling, M., Hothem, D., Mills, D., Hines, J., Mayer, K., Nelson, J. S., LeBlond, T., et al. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2609.12146

Benchmarking quantum computers

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Scalable Full-Stack Benchmarks for Quantum Computers

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A Review and Collection of Metrics and Benchmarks for Quantum Computers: definitions, methodologies and software

Lall, D., Agarwal, A., Zhang, W., Lindoy, L. P., Lindström, T., Webster, S., Hall, S., Chancellor, N., Wallden, P., García−Patrón, R., et al. (2025) · doi:10.59161/azye8097 · Cited by 3

Measuring progress towards useful quantum computation using precision benchmarks

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