Computational advantage of quantum random sampling
Open accessAbstract
Quantum computers have improved and recent experiments have claimed quantum advantage -- completion of a computational task that is evidently hard for any conventional computer. The problem solved is that of obtaining samples, by quantum measurement, from a certain probability distribution. This review shows in what precise way quantum random sampling can be seen as a computation. It explains what that computation solves, in what way it outperforms classical computations, and what methods of verification are available. Quantum random sampling becomes a first test of the presumed exponential computational advantage of quantum computers over classical ones.
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Because the classical difficulty of sampling from the outputs of random quantum circuits has been extremely well studied over the past decade, RCS provides a well-vetted benchmark for the computational power of a quantum computer.
“At the same time, because the classical difficulty of sampling from the outputs of random quantum circuits has been extremely well studied over the past decade100, RCS provides a well-vetted benchmark for the computational power of a quantum computer.”
From A 98-qubit trapped-ion quantum computer with all-to-all connectivity (2026) · cites this paper as [100]
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Benchmarking quantum computer simulation software packages: State vector simulators
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Pirnay, N., Ulitzsch, V. Q., Wilde, F., Eisert, J., & Seifert, J. (2024) · Science Advances · doi:10.1126/sciadv.adj5170 · Cited by 30 · see citing works
Self-citationAnticoncentration and Nonstabilizerness Spreading under Ergodic Quantum Dynamics
Tirrito, E., Turkeshi, X., & Sierant, P. (2025) · Physical Review Letters · doi:10.1103/1jzy-sk9r · Cited by 29 · see citing works
Bell Sampling from Quantum Circuits
Hangleiter, D., & Gullans, M. J. (2024) · Physical Review Letters · doi:10.1103/physrevlett.133.020601 · Cited by 28 · see citing works
Self-citationFaster classical boson sampling
Clifford, P., & Raphaël Clifford, A. (2024) · Physica Scripta · doi:10.1088/1402-4896/ad4688 · Cited by 28 · see citing works
Learning Shallow Quantum Circuits
Huang, H.-Y., Liu, Y., Broughton, M., Kim, I. H., Anshu, A., Landau, Z. A., & McClean, J. R. (2024) · ACM Symposium on Theory of Computing (STOC) · doi:10.1145/3618260.3649722 · Cited by 26 · see citing works
Shadow estimation of gate-set properties from random sequences
Helsen, J., Ioannou, M., Kitzinger, J., Onorati, E., Werner, A. H., Eisert, J., & Roth, I. (2023) · Nature Communications · doi:10.1038/s41467-023-39382-9 · Cited by 26 · see citing works
Self-citationNoise-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 24 · see citing works
Self-citationAnticoncentration in Clifford Circuits and Beyond: From Random Tensor Networks to Pseudomagic States
Magni, B., Christopoulos, A., Luca, A. D., & Turkeshi, X. (2025) · Physical Review X · doi:10.1103/p8dn-glcw · Cited by 24 · see citing works
Verifying Quantum Advantage Experiments with Multiple Amplitude Tensor Network Contraction
Liu, Y., Chen, Y., Guo, C., Song, J., Shi, X., Gan, L., Wu, W., Wu, W., Fu, H., Liu, X., et al. (2024) · Physical Review Letters · doi:10.1103/physrevlett.132.030601 · Cited by 22 · see citing works
Dynamical simulations of many-body quantum chaos on a quantum computer
Fischer, L. E., Leahy, M., Eddins, A., Keenan, N., Ferracin, D., Rossi, M. A. C., Kim, Y., He, A., Pietracaprina, F., Sokolov, B., et al. (2026) · Nature Physics · doi:10.1038/s41567-025-03144-9 · Cited by 21 · see citing works
Hardware-tailored diagonalization circuits
Miller, D., Fischer, L. E., Levi, K., Kuehnke, E. J., Sokolov, I. O., Barkoutsos, P. K., Eisert, J., & Tavernelli, I. (2024) · npj Quantum Information · doi:10.1038/s41534-024-00901-1 · Cited by 21 · see citing works
Self-citationOn the expressivity of embedding quantum kernels
Gil-Fuster, E., Eisert, J., & Dunjko, V. (2024) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ad2f51 · Cited by 19 · see citing works
Self-citationProbing coherent quantum thermodynamics using a trapped ion
Onishchenko, O., Guarnieri, G., Rosillo-Rodes, P., Pijn, D. R. M., Hilder, J., Poschinger, U., Perarnau-Llobet, M., Eisert, J., & Schmidt‐Kaler, F. (2024) · Nature Communications · doi:10.1038/s41467-024-51263-3 · Cited by 19 · see citing works
Self-citationJiang, T., O’Gorman, B., Mahajan, A., & Lee, J. (2025) · Physical Review Research · doi:10.1103/physrevresearch.7.013038 · Cited by 19 · see citing works
Claeys, P. W., & Tomasi, G. D. (2025) · Physical Review Letters · doi:10.1103/physrevlett.134.050405 · Cited by 19 · see citing works
Learning quantum states of continuous-variable systems
Mele, F. A., Mele, A. A., Bittel, L., Eisert, J., Giovannetti, V., Lami, L., Leone, L., & Oliviero, S. F. E. (2025) · Nature Physics · doi:10.1038/s41567-025-03086-2 · Cited by 18 · see citing works
Self-citationOpportunities and challenges of quantum computing for climate modeling
Schwabe, M., Pastori, L., de Vega, I., Gentine, P., Iapichino, L., Lahtinen, V., Leib, M., Lorenz, J. M., & Eyring, V. (2025) · Environmental Data Science · doi:10.1017/eds.2025.10010 · Cited by 18 · see citing works
Martínez-Cifuentes, J., Fonseca-Romero, K. M., & Quesada, N. (2023) · Quantum · doi:10.22331/q-2023-08-08-1076 · Cited by 18 · see citing works
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 · see citing works
“At the same time, because the classical difficulty of sampling from the outputs of random quantum circuits has been extremely well studied over the past decade 100 , RCS provides a well-vetted benchmark for the computational power of a quantum computer.”
Exactly solvable quantum many-body dynamics from space-time duality
Bertini, B., Claeys, P. W., & Prosen, T. (2025) · Reviews of Modern Physics · doi:10.1103/yx73-dk86 · Cited by 16 · see citing works
Anticoncentration and State Design of Random Tensor Networks
Lami, G., Nardis, J. D., & Turkeshi, X. (2025) · Physical Review Letters · doi:10.1103/physrevlett.134.010401 · Cited by 16 · see citing works
Unveiling quantum phase transitions from traps in variational quantum algorithms
Cao, C., Gambetta, F. M., Montanaro, A., & Santos, R. A. (2025) · npj Quantum Information · doi:10.1038/s41534-025-01038-5 · Cited by 15 · see citing works
Complexity-Constrained Quantum Thermodynamics
Munson, A., Kothakonda, N. B. T., Haferkamp, J., Halpern, N. Y., Eisert, J., & Faist, P. (2025) · PRX Quantum · doi:10.1103/prxquantum.6.010346 · Cited by 15 · see citing works
Self-citationEfficient Verification of Ground States of Frustration-Free Hamiltonians
Zhu, H., Li, Y., & Chen, T. (2024) · Quantum · doi:10.22331/q-2024-01-10-1221 · Cited by 13 · see citing works
Exploring Unsupervised Anomaly Detection with Quantum Boltzmann Machines in Fraud Detection
Stein, J., Schuman, D., Benkard, M., Holger, T., Sajko, W., Kölle, M., Nüßlein, J., Sünkel, L., Salomon, O., & Linnhoff–Popien, C. (2024) · International Conference on Agents and Artificial Intelligence (ICAART) · doi:10.5220/0012326100003636 · Cited by 13 · see citing works
Markov-chain Monte Carlo method enhanced by a quantum alternating operator ansatz
Nakano, Y., Hakoshima, H., Mitarai, K., & Fujii, K. (2024) · Physical Review Research · doi:10.1103/physrevresearch.6.033105 · Cited by 13 · see citing works
Simulating Gaussian boson sampling quantum computers
Dellios, A. S., Reid, M. D., & Drummond, P. D. (2023) · AAPPS bulletin · doi:10.1007/s43673-023-00099-y · Cited by 13 · see citing works
Fermionic Magic Resources of Quantum Many-Body Systems
Sierant, P., Stornati, P., & Turkeshi, X. (2025) · PRX Quantum · doi:10.1103/3yx4-1j27 · Cited by 12 · see citing works
Quantum computing for discrete optimization: A highlight of three technologies
Bochkarev, A. A., Heese, R., Jäger, S., Schiewe, P., & Schöbel, A. (2025) · European Journal of Operational Research · doi:10.1016/j.ejor.2025.07.063 · Cited by 12 · see citing works
Bounding the speedup of the quantum-enhanced Markov-chain Monte Carlo algorithm
Orfi, A., & Sels, D. (2024) · Physical Review A · doi:10.1103/physreva.110.052414 · Cited by 12 · see citing works
Quantum Computational Advantage with Constant-Temperature Gibbs Sampling
Bergamaschi, T., Chen, C.-F., & Liu, Y. (2024) · Annual Symposium on Foundations of Computer Science · doi:10.1109/focs61266.2024.00071 · Cited by 11 · see citing works
Fault-Tolerant Compiling of Classically Hard Instantaneous Quantum Polynomial Circuits on Hypercubes
Hangleiter, D., Kalinowski, M., Bluvstein, D., Cain, M., Maskara, N., Gao, X., Kubica, A., Lukin, M. D., & Gullans, M. J. (2025) · PRX Quantum · doi:10.1103/prxquantum.6.020338 · Cited by 11 · see citing works
Self-citationVerifiable measurement-based quantum random sampling with trapped ions
Ringbauer, M., Hinsche, M., Feldker, T., Faehrmann, P. K., Bermejo-Vega, J., Edmunds, C., Postler, L., Stricker, R., Marciniak, C. D., Meth, M., et al. (2025) · Nature Communications · doi:10.1038/s41467-024-55342-3 · Cited by 11 · see citing works
Self-citationQuantum information processing with superconducting circuits: A perspective
Wendin, G. P. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-90800-9.00226-2 · Cited by 11 · see citing works
Signatures of a sampling quantum advantage in driven quantum many-body systems
Tangpanitanon, J., Thanasilp, S., Lemonde, M.-A., Dangniam, N., & Angelakis, D. G. (2023) · Quantum Science and Technology · doi:10.1088/2058-9565/acbd69 · Cited by 10 · see citing works
Semi-device-dependent blind quantum tomography
Roth, I., Wilkens, J., Hangleiter, D., & Eisert, J. (2023) · Quantum · doi:10.22331/q-2023-07-11-1053 · Cited by 10 · see citing works
Self-citationFast, universal scheme for calibrating microwave crosstalk in superconducting circuits
Yang, X., Zhang, H.-F., Du, L., Tao, H.-R., Guo, L.-L., Wang, T.-L., Jia, Z.-L., Kong, W., Chen, Z., Duan, P., et al. (2024) · Applied Physics Letters · doi:10.1063/5.0211159 · Cited by 9 · see citing works
Exploring quantum neural networks for binary classification on MNIST dataset: A swap test approach
Chen, K., Liu, J., & Yan, F. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107442 · Cited by 9 · see citing works
On the average-case complexity of learning output distributions of quantum circuits
Nietner, A., Ioannou, M., Sweke, R., Kueng, R., Eisert, J., Hinsche, M., & Haferkamp, J. (2025) · Quantum · doi:10.22331/q-2025-10-13-1883 · Cited by 9 · see citing works
Self-citationUniversality in the anticoncentration of chaotic quantum circuits
Anonymous, Magni, B., Lami, G., Turkeshi, X., & Nardis, J. D. (2025) · Physical review. B./Physical review. B · doi:10.1103/lkwg-4dbt · Cited by 9 · see citing works
Gibbs Sampling gives Quantum Advantage at Constant Temperatures with O(1)-Local Hamiltonians
Rajakumar, J., & Watson, J. D. (2026) · Quantum · doi:10.22331/q-2026-01-22-1981 · Cited by 9 · see citing works
Founding Quantum Cryptography on Quantum Advantage, or, Towards Cryptography from #P Hardness
Khurana, D., & Tomer, K. (2025) · ACM Symposium on Theory of Computing (STOC) · doi:10.1145/3717823.3718145 · Cited by 9 · see citing works
Error mitigation thresholds in noisy random quantum circuits
Niroula, P., Gopalakrishnan, S., & Gullans, M. J. (2025) · Physical review. B./Physical review. B · doi:10.1103/qsmz-9kkh · Cited by 9 · see citing works
Hamiltonian learning for 300 trapped ion qubits with long-range couplings
Guo, S., Wu, Y., Ye, J., Zhang, L., Wang, Y., Lian, W., Yao, R., Xu, Y., Zhang, C., Xu, Y., et al. (2025) · Science Advances · doi:10.1126/sciadv.adt4713 · Cited by 8 · see citing works
Linear Cross-Entropy Certification of Quantum Computational Advantage in Gaussian Boson Sampling
Martínez-Cifuentes, J., de Guise, H., & Quesada, N. (2024) · PRX Quantum · doi:10.1103/prxquantum.5.040312 · Cited by 8 · see citing works
Photon-number moments and cumulants of Gaussian states
Cardin, Y., & Quesada, N. (2024) · Quantum · doi:10.22331/q-2024-11-13-1521 · Cited by 8 · see citing works
Shadow estimation of gate-set properties from random sequences
Helsen, J., Ioannou, M., Kitzinger, J., Onorati, E., Werner, A. H., Eisert, J., & Roth, I. (2023) · Refubium (Universitätsbibliothek der Freien Universität Berlin) · doi:10.17169/refubium-40372 · Cited by 8 · see citing works
Self-citationQuantum estimation bound of the Gaussian matrix permanent
Huh, J. (2025) · Physical Review A · doi:10.1103/physreva.111.012418 · Cited by 8 · see citing works
Polynomial-Time Classical Simulation of Noisy IQP Circuits with Constant Depth
Rajakumar, J., Watson, J. D., & Liu, Y.-K. (2025) · Proceedings of the 2025 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA) · doi:10.1137/1.9781611978322.30 · Cited by 7 · see citing works
“Let 𝐺 be a graph with maximum degree Δ , and let 𝐻 𝑐 be the cost function for MAXCUT on 𝐺 .”
Goh, K. W., Khan, B. U. I., Khan, A. R., Putra, D. S., P., F. R. P., & Bhuyian, M. A. (2025) · Scientific Reports · doi:10.1038/s41598-025-23310-6 · Cited by 7 · see citing works
Challenges for Quantum Software Engineering: An Industrial Application Scenario Perspective
Carbonelli, C., Felderer, M., Jung, M., Lobe, E., Lochau, M., Luber, S. M., Mauerer, W., Ramler, R., Schaefer, I., & Schroth, C. (2024) · Springer eBooks · doi:10.1007/978-3-031-64136-7_12 · Cited by 7 · see citing works
Power of quantum measurement in simulating unphysical operations
Zhao, X., Zhang, L., Zhao, B., & Wang, X. (2025) · Physical Review Research · doi:10.1103/physrevresearch.7.013334 · Cited by 7 · see citing works
Computational Complexity of Isometric Tensor-Network States
Malz, D., & Trivedi, R. (2025) · PRX Quantum · doi:10.1103/prxquantum.6.020310 · Cited by 7 · see citing works
BosonSampling.jl: A Julia package for quantum multi-photon interferometry
Seron, B., & Restivo, A. (2024) · Quantum · doi:10.22331/q-2024-06-18-1378 · Cited by 7 · see citing works
Optimal estimates of trace distance between bosonic Gaussian states and applications to learning
Bittel, L., Mele, F. A., Mele, A. A., Tirone, S., & Lami, L. (2025) · Quantum · doi:10.22331/q-2025-06-12-1769 · Cited by 6 · see citing works
Learning Quantum States Prepared by Shallow Circuits in Polynomial Time
Landau, Z. A., & Liu, Y. (2025) · ACM Symposium on Theory of Computing (STOC) · doi:10.1145/3717823.3718311 · Cited by 6 · see citing works
Behrends, J., & Béri, B. (2025) · PRX Quantum · doi:10.1103/gskb-t5ql · Cited by 6 · see citing works
Quantifying Quantum Computational Advantage on a Processor of Ultracold Atoms
Zheng, Y.-G., Shen, Y.-C., Zhang, W.-Y., Luo, A., Liu, Y., He, M.-G., Zhang, H., Wan, L., Wang, H.-Y., Zhu, Z., et al. (2026) · Physical Review X · doi:10.1103/5mqb-3by1 · Cited by 6 · see citing works
Verification of Bell nonlocality by violating quantum monogamy relations
Yang, Y., Liu, X.-Z., Zheng, X.-Z., Fei, S.-M., & Luo, M. (2024) · Cell Reports Physical Science · doi:10.1016/j.xcrp.2024.101840 · Cited by 6 · see citing works
Phase transitions in sampling and error correction in local Brownian circuits
Sahu, S., & Jian, S.-K. (2024) · Physical Review A · doi:10.1103/physreva.109.042414 · Cited by 6 · see citing works
Quantum-Inspired Statistical Frameworks: Enhancing Traditional Methods with Quantum Principles
Kyriazos, T. A., & Poga, M. (2025) · Encyclopedia · doi:10.3390/encyclopedia5020048 · Cited by 6 · see citing works
Finite temperature tensor network algorithm for frustrated two-dimensional quantum materials
Schmoll, P., Balz, C., Lake, B., Eisert, J., & Kshetrimayum, A. (2024) · Physical review. B./Physical review. B · doi:10.1103/physrevb.109.235119 · Cited by 5 · see citing works
Self-citationCan Quantum Computing Improve Uniform Random Sampling of Large Configuration Spaces?
Ammermann, J., Bittner, T., Eichhorn, D., Schaefer, I., & Seidl, C. (2023) · IEEE/ACM International Workshop on Quantum Software Engineering (Q-SE) · doi:10.1109/q-se59154.2023.00012 · Cited by 5 · see citing works
Classical simulability of Clifford + T circuits with Clifford-augmented matrix product states
Liu, Z., & Clark, B. K. (2026) · Physical Review Research · doi:10.1103/ybnf-rjw8 · Cited by 4 · see citing works
Experimental validation of boson sampling using detector binning
Anguita, M. C., Camillini, A., Marzban, S., Robbio, M., Seron, B., Novo, L., & Renema, J. J. (2025) · Quantum Science and Technology · doi:10.1088/2058-9565/adecbc · Cited by 4 · see citing works
Quantum beat of two single photons in the transverse momentum space
Guo, B., Chen, Z., Maggio, L., Wu, W., Liu, S., Tamma, V., & Fan, J. (2025) · Physical Review A · doi:10.1103/wv2d-5z4g · Cited by 4 · see citing works
Comparative Analysis of Quantum Encoding Techniques for Biomarker Classification
Eutamene, A., Djemmal, I., Belhadef, H., & Mutawa, A. M. (2025) · IEEE Access · doi:10.1109/access.2025.3628830 · Cited by 4 · see citing works
Quantum-inspired dynamical models on quantum and classical annealers
Hanussek, P., Pawłowski, J., Mzaouali, Z., & Gardas, B. (2026) · Physical Review Applied · doi:10.1103/rrf3-jm5m · Cited by 4 · see citing works
Toward quantum unmanned systems
Cheng, S., Qi, H., Sun, Y., Liu, L., Xie, S., & Ren, X. (2024) · Science China Technological Sciences · doi:10.1007/s11431-023-2642-1 · Cited by 4 · see citing works
Behrends, J., & Béri, B. (2025) · PRX Quantum · doi:10.1103/psf5-b6j2 · Cited by 3 · see citing works
Entanglement dynamics and Page curves in random permutation circuits
Szász-Schagrin, D., Mazzoni, M., Bertini, B., Klobas, K., & Piroli, L. (2025) · Physical Review Research · doi:10.1103/d8tm-1586 · Cited by 3 · see citing works
Liang, W., & Zf, Y. (2025) · Physical Review A · doi:10.1103/9smt-lgth · Cited by 3 · see citing works
Quantum community detection via deterministic elimination
Umeano, C., Scali, S., & Kyriienko, O. (2025) · Physical Review A · doi:10.1103/s7cm-c9qy · Cited by 3 · see citing works
Quantum Bayesian Inference with Renormalization for Gravitational Waves
Escrig, G., Campos, R., Qi, H., & Martin-Delgado, M. A. (2025) · The Astrophysical Journal Letters · doi:10.3847/2041-8213/ada6ae · Cited by 3 · see citing works
Transition of Anticoncentration in Gaussian Boson Sampling
Ehrenberg, A., Iosue, J. T., Deshpande, A., Hangleiter, D., & Gorshkov, A. V. (2025) · Physical Review Letters · doi:10.1103/physrevlett.134.140601 · Cited by 3 · see citing works
Self-citationSecond moment of Hafnians in Gaussian boson sampling
Ehrenberg, A., Iosue, J. T., Deshpande, A., Hangleiter, D., & Gorshkov, A. V. (2025) · Physical Review A · doi:10.1103/physreva.111.042412 · Cited by 3 · see citing works
Self-citationQuantum tug of war between randomness and symmetries on homogeneous spaces
Arvind, R., Bharti, K., Khoo, J. Y., Koh, D. E., & Kong, J. F. (2025) · Physical Review Research · doi:10.1103/physrevresearch.7.013105 · Cited by 3 · see citing works
Binned-detector probability distributions for Gaussian boson sampling validation
Bressanini, G., Seron, B., Novo, L., Cerf, N. J., & Kim, M. S. (2025) · Physical Review A · doi:10.1103/jqvf-pm1r · Cited by 3 · see citing works
Local coherence by thermalized intra-system coupling
Kolář, M., & Filip, R. (2024) · Quantum Science and Technology · doi:10.1088/2058-9565/ad57e8 · Cited by 3 · see citing works
Tailoring Nuclear Spins Order With Defects: A Quantum Technology CAD Study
Calogero, G., Deretzis, I., Fisicaro, G., Ricciarelli, D., Viglione, R. G., & Magna, A. L. (2025) · Advanced Quantum Technologies · doi:10.1002/qute.202500160 · Cited by 2 · see citing works
Scalable projected entangled-pair state representation of random quantum circuit states
Lee, S., Choi, H. R., Ohm, D. D., & Lee, S. B. (2025) · Physical Review Research · doi:10.1103/rzgm-cywf · Cited by 2 · see citing works
Neural-Network-Assisted Monte Carlo Sampling Trained by Quantum Approximate Optimization Algorithm
Nakano, Y., Okada, K. N., & Fujii, K. (2026) · PRX Quantum · doi:10.1103/9nhx-5pym · Cited by 2 · see citing works
Configurable Photonic Simulator for Quantum Field Dynamics
D'Achille, M., Gärttner, M., & Haas, T. (2026) · PRX Quantum · doi:10.1103/8z9p-tygz · Cited by 2 · see citing works
Random Regular Graph States Are Complex at Almost Any Depth
Ghosh, S., Hangleiter, D., & Helsen, J. (2025) · PRX Quantum · doi:10.1103/52xz-3hpc · Cited by 2 · see citing works
Self-citationPenetration testing of a quantum key distribution system as a black box
Huang, A., Peng, Q., Liu, J., Yuan, X., Peng, C., Yang, Z., Wu, H., Chen, Z., Sun, G., Wang, F., et al. (2026) · National Science Review · doi:10.1093/nsr/nwag174 · Cited by 2 · see citing works
- Authors
- Hangleiter, D., & Eisert, J.
- Affiliations
- National Institute of Standards and Technology (US)
- Corresponding author
- Hangleiter, Dominik
- Venue
- Reviews of Modern Physics
- Publisher
- American Physical Society (APS)
- Published
- 2023-07-20
- Volume
- 95
- Issue
- 3
- ISSN
- 0034-6861 (ISSN-L)
- 1539-0756
- OpenAlex
- W4384935381
- Cited by
- 145
- References
- 377
- Citations by year
- 2026: 29 · 2025: 66 · 2024: 36 · 2023: 12 · 2022: 2
- FWCI
- 12.66
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Reviews of Modern Physics (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Refubium (Universitätsbibliothek der Freien Universität Berlin) (submittedVersion)
- HZB Repository (Helmholtz-Zentrum Berlin für Materialien und Energie GmbH (HZB)) (submittedVersion)
- Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion)
- arXiv (Cornell University)
- Universitätsbibliothek der FU Berlin Hochschulschriftenstelle u. Dokumentenserver
- Funders
- U.S. Department of Defense
- Simons Institute for the Theory of Computing, University of California Berkeley
- Deutsche Forschungsgemeinschaft (CRC 183, EI 519/21-1)
- Bundesministerium für Bildung und Forschung
- QuantERA
- Einstein Stiftung Berlin
- Horizon 2020 Framework Programme (817482)
- German Federal Ministry of Economics and Climate Protection
- Munich Quantum Valley
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Information and Cryptography
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum Mechanics and Applications
- Keywords
- quantum advantage, quantum measurement, probability distributions
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