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Phase-Programmable Gaussian Boson Sampling Using Stimulated Squeezed Light
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We report phase-programmable Gaussian boson sampling (GBS) which produces up to 113 photon detection events out of a 144-mode photonic circuit. A new high-brightness and scalable quantum light source is developed, exploring the idea of stimulated emission of squeezed photons, which has simultaneously near-unity purity and efficiency. This GBS is programmable by tuning the phase of the input squeezed states. The obtained samples are efficiently validated by inferring from computationally friendly subsystems, which rules out hypotheses including distinguishable photons and thermal states. We show that our GBS experiment passes a nonclassicality test based on inequality constraints, and we reveal nontrivial genuine high-order correlations in the GBS samples, which are evidence of robustness against possible classical simulation schemes. This photonic quantum computer, Jiuzhang 2.0, yields a Hilbert space dimension up to ∼10^{43}, and a sampling rate ∼10^{24} faster than using brute-force simulation on classical supercomputers.
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“There are then two ways around this no-go result: one can either give up programmability and build an ultralow loss fixed static interferometer, as implemented in refs. 1,2, or give up universality while maintaining a high degree of multimode entanglement using long-ranged gates.”
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“Thus, the fact that significant third- and fourth-order cumulants are observed in refs. 1,2 is simply a reflection of the fact that most of their inputs are vacuum and that their experiment lacks photon-number resolution.”
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“This is not the case for other architectures in which one either has to give up programmability1,2 or suffer steep losses that, in the asymptotic limit of many modes, render the sampling task roughly simulable as the loss scales exponentially with the system size31.”
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“we estimate that Fugaku would require 1.5 h, on average, to generate one sample from the experiment in ref. 2”
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“or 8,500 years for the 50 million generated in their experiment.”
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Citations
Cites
Can Quantum-Mechanical Description of Physical Reality Be Considered Complete?
Einstein, A., Podolsky, B., & Rosen, N. (1935) · Physical Review · doi:10.1103/physrev.47.777 · Cited by 16,654
Ror2 signaling regulates Golgi structure and transport through IFT20 for tumor invasiveness
Nishita, M., Park, S.-Y., Nishio, T., Kamizaki, K., Wang, Z., Tamada, K., Takumi, T., Hashimoto, R., Otani, H., Pazour, G. J., et al. (2017) · Scientific Reports · doi:10.1038/s41598-016-0028-x · Cited by 15,947
Self-citationOn the Einstein Podolsky Rosen paradox
Bell, J. S. (1964) · Physics Physique Fizika · doi:10.1103/physicsphysiquefizika.1.195 · Cited by 12,252
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 · cited 2×
“32(1), which is significantly larger than the statistical fluctuation, thus successfully distinguishing the data of the 30 different groups.”
BackgroundA scheme for efficient quantum computation with linear optics
Knill, E., Laflamme, R., & Milburn, G. J. (2001) · Nature · doi:10.1038/35051009 · Cited by 5,880
Linear optical quantum computing with photonic qubits
Kok, P., Munro, W. J., Nemoto, K., Ralph, T. C., Dowling, J. P., & Milburn, G. J. (2007) · Reviews of Modern Physics · doi:10.1103/revmodphys.79.135 · Cited by 2,904
Quantum computational advantage using photons
Zhong, H.-S., Wang, H., Deng, Y.-H., Chen, M.-C., Peng, L.-C., Luo, Y.-H., Qin, J., Wu, D., Ding, X., Hu, Y., et al. (2020) · Science · doi:10.1126/science.abe8770 · Cited by 2,405
Self-citationQuantum Machine Learning in Feature Hilbert Spaces
Schuld, M., & Killoran, N. (2019) · Physical Review Letters · doi:10.1103/physrevlett.122.040504 · Cited by 1,840
Encoding a qubit in an oscillator
Gottesman, D., Kitaev, A., & Preskill, J. (2001) · Physical Review A · doi:10.1103/physreva.64.012310 · Cited by 1,532
Cited by
Sorted by citations
Strong Quantum Computational Advantage Using a Superconducting Quantum Processor
Wu, Y., Bao, W.-S., Cao, S., Chen, F., Chen, M.-C., Chen, X., Chung, T.-H., Deng, H., Du, Y., Fan, D., et al. (2021) · Physical Review Letters · doi:10.1103/physrevlett.127.180501 · Cited by 1,059
Self-citationQuantum computational advantage with a programmable photonic processor
Madsen, L. S., Laudenbach, F., Askarani, M. F., Rortais, F., Vincent, T., Bulmer, J. F. F., Miatto, F. M., Neuhaus, L., Helt, L. G., Collins, M. J., et al. (2022) · Nature · doi:10.1038/s41586-022-04725-x · Cited by 1,101
“Only a handful of experiments have used quantum devices to carry out computational tasks that are outside the reach of present-day classical computers 1,2,6,7 .”
BackgroundMethodInfluentialThe potential and global outlook of integrated photonics for quantum technologies
Pelucchi, E., Fagas, G., Aharonovich, I., Englund, D. R., Figueroa, E. V., Gong, Q., Hübel, H., Liu, J., Lu, C., Matsuda, N., et al. (2021) · Nature Reviews Physics · doi:10.1038/s42254-021-00398-z · Cited by 578
Self-citationA programmable qudit-based quantum processor
Chi, Y., Huang, J., Zhang, Z., Mao, J., Zhou, Z., Chen, X., Zhai, C., Bao, J., Dai, T., Yuan, H., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-28767-x · Cited by 273
Self-citationQuantum computational advantage via 60-qubit 24-cycle random circuit sampling
Zhu, Q., Cao, S., Chen, F., Chen, M.-C., Chen, X., Chung, T.-H., Deng, H., Du, Y., Fan, D., Gong, M., et al. (2022) · Science Bulletin · doi:10.1016/j.scib.2021.10.017 · Cited by 263
Self-citationA versatile single-photon-based quantum computing platform
Maring, N., Fyrillas, A., Pont, M., Ivanov, E., Stepanov, P., Margaria, N., Hease, W., Pishchagin, A., Lemaître, A., Sagnes, I., et al. (2024) · Nature Photonics · doi:10.1038/s41566-024-01403-4 · Cited by 314
“We record the threshold statistics of all N -photon coincidences ( N ∈ [[1; 6]]) and acquire in total 340.10 9 samples, with a six-photon coincidence rate reduced by the strong bunching of photons in this sampling task down to 0.16 Hz.”
InfluentialVery-large-scale integrated quantum graph photonics
Bao, J., Fu, Z., Pramanik, T., Mao, J., Chi, Y., Cao, Y., Zhai, C., Mao, Y., Dai, T., Chen, X., et al. (2023) · Nature Photonics · doi:10.1038/s41566-023-01187-z · Cited by 215
Deng, Y., Gu, Y.-C., Liu, H.-L., Gong, S.-Q., Su, H., Zhang, Z.-J., Tang, H., Jia, M.-H., Xu, J., Chen, M.-C., et al. (2023) · Physical Review Letters · doi:10.1103/physrevlett.131.150601 · Cited by 176
Self-citationComputational advantage of quantum random sampling
Hangleiter, D., & Eisert, J. (2023) · Reviews of Modern Physics · doi:10.1103/revmodphys.95.035001 · Cited by 145
Phase transitions in random circuit sampling
Morvan, A., Villalonga, B., Mi, X., Mandrà, S., Bengtsson, A., Klimov, P. V., Chen, Z., Hong, S., Erickson, C., Drozdov, I., et al. (2024) · Nature · doi:10.1038/s41586-024-07998-6 · Cited by 233
Beyond-classical computation in quantum simulation
King, A. D., Nocera, A., Rams, M. M., Dziarmaga, J., Wiersema, R., Bernoudy, W., Raymond, J., Kaushal, N., Heinsdorf, N., Harris, R., et al. (2025) · Science · doi:10.1126/science.ado6285 · Cited by 138
Ding, X., Guo, Y.-P., Xu, M.-C., Liu, R., Zou, G.-Y., Zhao, J.-Y., Ge, Z.-X., Zhang, Q.-H., Liu, H.-L., Wang, L.-J., et al. (2025) · Nature Photonics · doi:10.1038/s41566-025-01639-8 · Cited by 170
“Then, the top mirror returns, forming a fully tunable open microcavity that allows in-situ coupling of the cavity spatially and spectrally.”
Self-citationLi, Z., Xue, K., Li, J., Chen, L., Li, R., Wang, Z., Yu, N., Wei, D. S. L., Sun, Q., & Lu, J. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3294240 · Cited by 149
Self-citationNoisy intermediate-scale quantum computers
Cheng, B., Deng, X., Gu, X. Q., He, Y., Hu, G., Huang, P., Li, J., Lin, B., Lu, D., Lu, Y., et al. (2023) · Frontiers of Physics · doi:10.1007/s11467-022-1249-z · Cited by 147
Self-citationGao, D., Fan, D., Zha, C., Bei, J., Cai, G., Cai, J., Cao, S., Chen, F., Jiang, C., Chen, K. Y., et al. (2025) · Physical Review Letters · doi:10.1103/physrevlett.134.090601 · Cited by 129
Self-citationQuantum dynamics in Krylov space: Methods and applications
Nandy, P., Matsoukas-Roubeas, A. S., Martínez-Azcona, P., Dymarsky, A. Y., & del Campo, A. (2025) · Physics Reports · doi:10.1016/j.physrep.2025.05.001 · Cited by 220
Quantum amplification and simulation of strong and ultrastrong coupling of light and matter
Qin, W., Kockum, A. F., Muñoz, C. S., Miranowicz, A., & Nori, F. (2024) · Physics Reports · doi:10.1016/j.physrep.2024.05.003 · Cited by 123
Quantum computational advantage via high-dimensional Gaussian boson sampling
Deshpande, A., Mehta, A., Vincent, T., Quesada, N., Hinsche, M., Ioannou, M., Madsen, L., Lavoie, J., Qi, H., Eisert, J., et al. (2022) · Science Advances · doi:10.1126/sciadv.abi7894 · Cited by 106
Quantum machine learning: from physics to software engineering
Melnikov, A. A., Kordzanganeh, M., Alodjants, A. P., & Lee, R. (2023) · Advances in Physics X · doi:10.1080/23746149.2023.2165452 · Cited by 125
“In addition to classical information processing, photonic quantum computing is also one of the possible technologies to demonstrate quantum advantage [14,367,368], i.”
BackgroundHuang, 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
Integrated quantum optical phase sensor in thin film lithium niobate
Stokowski, H. S., McKenna, T. P., Park, T., Hwang, A. Y., Dean, D. J., Celik, O. T., Ansari, V., Fejer, M. M., & Safavi‐Naeini, A. H. (2023) · Nature Communications · doi:10.1038/s41467-023-38246-6 · Cited by 103
20-Mode Universal Quantum Photonic Processor
Taballione, C., Anguita, M. C., de Goede, M., Venderbosch, P., Kassenberg, B., Snijders, H., Kannan, N., Vleeshouwers, W., Smith, D. H., Epping, J. P., et al. (2023) · Quantum · doi:10.22331/q-2023-08-01-1071 · Cited by 100
“Photonics is one of the most attractive approaches to quantum computing, having gained momentum thanks to recent experimental results demonstrating a quantum advantage in photonics [1, 2].”
BackgroundSelf-citationRoadmap on electromagnetic metamaterials and metasurfaces
Cui, T. J., Zhang, S., Alù, A., Wegener, M., Pendry, S. J., Luo, J., Lai, Y., Wang, Z., Lin, X., Chen, H., et al. (2024) · Journal of Physics Photonics · doi:10.1088/2515-7647/ad1a3b · Cited by 96
Self-citationThe boundary for quantum advantage in Gaussian boson sampling
Bulmer, J. F. F., Bell, B. A., Chadwick, R. S., Jones, A. E., Moise, D., Rigazzi, A., Thorbecke, J., Haus, U.-U., Van Vaerenbergh, T., Patel, R. B., et al. (2022) · Science Advances · doi:10.1126/sciadv.abl9236 · Cited by 88
Quantum-enhanced Markov chain Monte Carlo
Layden, D., Mazzola, G., Mishmash, R. V., Motta, M., Wocjan, P., Kim, J.-S., & Sheldon, S. (2023) · Nature · doi:10.1038/s41586-023-06095-4 · Cited by 89
“…j from independent standard normal distributions gives an ensemble of model instances similar to the well-known Sherrington-Kirkpatrick (S-K) model [3], but with two main differences: (i) the original S-K model does not include the random local fields discussed above, and (ii) it draws { J jk } from…”
BackgroundMethodNISQ Computers: A Path to Quantum Supremacy
AbuGhanem, M., & Eleuch, H. (2024) · IEEE Access · doi:10.1109/access.2024.3432330 · Cited by 94
“Reproduced under Creative Commons license CC BY from [36].”
BackgroundMethodInfluentialNanowire‐based integrated photonics for quantum information and quantum sensing
Chang, J., Gao, J., Zadeh, I. E., Elshaari, A. W., & Zwiller, V. (2023) · Nanophotonics · doi:10.1515/nanoph-2022-0652 · Cited by 74
“Gaussian boson sampling has been experimentally demonstrated using both ultra-low loss bulk optics [4, 54] and silicon photonics platform [96].”
BackgroundMethodPerceval: A Software Platform for Discrete Variable Photonic Quantum Computing
Heurtel, N., Fyrillas, A., de Gliniasty, G., Bihan, R. L., Malherbe, S., Pailhas, M., Bertasi, E., Bourdoncle, B., Emeriau, P.-E., Mezher, R., et al. (2023) · Quantum · doi:10.22331/q-2023-02-21-931 · Cited by 94
Resources for Bosonic Quantum Computational Advantage
Chabaud, U., & Walschaers, M. (2023) · Physical Review Letters · doi:10.1103/physrevlett.130.090602 · Cited by 80
Chip-scale simulations in a quantum-correlated synthetic space
Javid, U. A., Lopez‐Rios, R., Ling, J., Graf, A., Staffa, J., & Lin, Q. (2023) · Nature Photonics · doi:10.1038/s41566-023-01236-7 · Cited by 66
“This is done by incoherently adding the random walk of each term of the superposition ∑ n â † nâ † −n|0〉 to simulate a statistical mixture of frequency correlated modes [6].”
BackgroundMethodAdvances in Solid-State Nonlinear Optical Materials: From Fundamentals to Applications
Aslam, H. Z., Doane, J. T., Yeung, M. T., & Akopov, G. (2023) · ACS Applied Optical Materials · doi:10.1021/acsaom.3c00352 · Cited by 63
Jnane, H., Undseth, B., Cai, Z., Benjamin, S. C., & Koczor, B. (2022) · Physical Review Applied · doi:10.1103/physrevapplied.18.044064 · Cited by 71
Quantum machine learning algorithms for anomaly detection: A review
Corli, S., Moro, L., Dragoni, D., Dispenza, M., & Prati, E. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.107632 · Cited by 81
Solving Graph Problems Using Gaussian Boson Sampling
Deng, Y., Gong, S.-Q., Gu, Y.-C., Zhang, Z.-J., Liu, H.-L., Su, H., Tang, H., Xu, J.-M., Jia, M.-H., Chen, M.-C., et al. (2023) · Physical Review Letters · doi:10.1103/physrevlett.130.190601 · Cited by 66
Self-citationExperimental Realization of Two Qutrits Gate with Tunable Coupling in Superconducting Circuits
Luo, K., Huang, W., Tao, Z., Zhang, L., Zhou, Y., Chu, J., Liu, W., Wang, B.-Y., Cui, J., Liu, S., et al. (2023) · Physical Review Letters · doi:10.1103/physrevlett.130.030603 · Cited by 61
Classical algorithm for simulating experimental Gaussian boson sampling
Oh, C., Liu, M., Alexeev, Y., Fefferman, B., & Jiang, L. (2024) · Nature Physics · doi:10.1038/s41567-024-02535-8 · Cited by 87
“39,40.”
InfluentialDemonstration of Algorithmic Quantum Speedup
Pokharel, B., & Lidar, D. A. (2023) · Physical Review Letters · doi:10.1103/physrevlett.130.210602 · Cited by 66
“In contrast to recent quantum supremacy demonstrations [11–13], the quantum speedup reported here does not rely on any additional assumptions or complexity-theoretic conjectures [14–16] and solves a bona fide computational problem, in the setting of a game with an oracle and a verifier.”
BackgroundLimitations of Linear Cross-Entropy as a Measure for Quantum Advantage
Gao, X., Kalinowski, M., Chou, C.-N., Lukin, M. D., Barak, B., & Choi, S. (2024) · PRX Quantum · doi:10.1103/prxquantum.5.010334 · Cited by 89
Limitations of Linear Cross-Entropy as a Measure for Quantum Advantage
Gao, X., Kalinowski, M., Chou, C.-N., Lukin, M. D., Barak, B., & Choi, S. (2021) · arXiv · doi:10.48550/arxiv.2112.01657 · Cited by 54
Unconditional and Robust Quantum Metrological Advantage beyond N00N States
Qin, J., Deng, Y.-H., Zhong, H.-S., Peng, L.-C., Su, H., Luo, Y.-H., Xu, J.-M., Wu, D., Gong, S.-Q., Liu, H.-L., et al. (2023) · Physical Review Letters · doi:10.1103/physrevlett.130.070801 · Cited by 58
Self-citationIntegrated photonics in quantum technologies
Giordani, T., Hoch, F., Carvacho, G., Spagnolo, N., & Sciarrino, F. (2023) · Rivista Del Nuovo Cimento · doi:10.1007/s40766-023-00040-x · Cited by 62
Reconfigurable continuously-coupled 3D photonic circuit for Boson Sampling experiments
Hoch, F., Piacentini, S., Giordani, T., Tian, Z.-N., Iuliano, M., Esposito, C., Camillini, A., Carvacho, G., Ceccarelli, F., Spagnolo, N., et al. (2022) · npj Quantum Information · doi:10.1038/s41534-022-00568-6 · Cited by 51
Advances in Chip-Based Quantum Key Distribution
Liu, Q., Huang, Y., Du, Y., Zhao, Z., Geng, M., Zhang, Z., & Wei, K. (2022) · Entropy · doi:10.3390/e24101334 · Cited by 51
Superconducting photon detectors
Morozov, D., Casaburi, A., & Hadfield, R. H. (2021) · Contemporary Physics · doi:10.1080/00107514.2022.2043596 · Cited by 54
Epitaxial quantum dots: a semiconductor launchpad for photonic quantum technologies
Zhou, X., Zhai, L., & Liu, J. (2022) · Photonics Insights · doi:10.3788/pi.2022.r07 · Cited by 56
“The demonstration was shortly followed by larger versions involving more photons [328,329] .”
BackgroundKoczor, B., & Benjamin, S. C. (2022) · Physical Review Research · doi:10.1103/physrevresearch.4.023017 · Cited by 55
Tight Bounds on the Convergence of Noisy Random Circuits to the Uniform Distribution
Deshpande, A., Niroula, P., Shtanko, O., Gorshkov, A. V., Fefferman, B., & Gullans, M. J. (2022) · PRX Quantum · doi:10.1103/prxquantum.3.040329 · Cited by 48
Hunting for quantum-classical crossover in condensed matter problems
Yoshioka, N., Okubo, T., Suzuki, Y., Koizumi, Y., & Mizukami, W. (2024) · npj Quantum Information · doi:10.1038/s41534-024-00839-4 · Cited by 75
Beating the 3 dB Limit for Intracavity Squeezing and Its Application to Nondemolition Qubit Readout
Qin, W., Miranowicz, A., & Nori, F. (2022) · Physical Review Letters · doi:10.1103/physrevlett.129.123602 · Cited by 48
“Such a propagating-field squeezing has been widely used for, e.g., gravitational-wave detection [7 – 9], mechanical cooling [10,11], nondemolition qubit readout [12 – 15], and even demonstrating quantum supremacy [16,17].”
MethodBreaking the rate-distance limitation of measurement-device-independent quantum secret sharing
Li, C.-L., Fu, Y., Liu, W., Xie, Y.-M., Li, B.-H., Zhou, M.-G., Yin, H., & Chen, Z. (2023) · Physical Review Research · doi:10.1103/physrevresearch.5.033077 · Cited by 43
Quantum simulation of thermodynamics in an integrated quantum photonic processor
Somhorst, F. H. B., van der Meer, R., Anguita, M. C., Schadow, R., Snijders, H., de Goede, M., Kassenberg, B., Venderbosch, P., Taballione, C., Epping, J. P., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-38413-9 · Cited by 46
Self-citationProgrammable Quantum Simulations of Bosonic Systems with Trapped Ions
Katz, O., & Monroe, C. R. (2023) · Physical Review Letters · doi:10.1103/physrevlett.131.033604 · Cited by 47
Intelligent Photonics: A Disruptive Technology to Shape the Present and Redefine the Future
Xu, D., Ma, Y., Jin, G., & Cao, L. (2024) · Engineering · doi:10.1016/j.eng.2024.08.016 · Cited by 47
Quantifying n -Photon Indistinguishability with a Cyclic Integrated Interferometer
Pont, M., Albiero, R., Thomas, S. E., Spagnolo, N., Ceccarelli, F., Corrielli, G., Brieussel, A., Somaschi, N., Huet, H., Harouri, A., et al. (2022) · Physical Review X · doi:10.1103/physrevx.12.031033 · Cited by 50
The Complexity of Bipartite Gaussian Boson Sampling
Grier, D., Brod, D. J., Arrazola, J. M., Alonso, M. B. D. A., & Quesada, N. (2022) · Quantum · doi:10.22331/q-2022-11-28-863 · Cited by 46
“GBS has already been used to claim quantum computational advantage [23, 24, 25], and there are several more proposals for hard-to-simulate GBS experiments [6, 7, 26].”
BackgroundMethodClassical Simulation of Boson Sampling Based on Graph Structure
Oh, C., Lim, Y., Fefferman, B., & Jiang, L. (2022) · Physical Review Letters · doi:10.1103/physrevlett.128.190501 · Cited by 44
“[10], we define where E l ⊂ E consists of the loops, and E 0 ≡ E \ E l , and the sequence { T k ( a ) k N satisfies the following recursion: In particular, T k ( a ) represents the loop hafnian of a constant matrix with an element a .”
BackgroundMethodInfluentialQuantum 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
“[3] Han-Sen Zhong, Yu-Hao Deng, Jian Qin, Hui Wang, Ming-Cheng Chen, Li-Chao Peng, Yi-Han Luo, Dian Wu, Si-Qiu Gong, Hao Su, Yi Hu, Peng Hu, Xiao-Yan Yang, Wei-Jun Zhang, Hao Li, Yuxuan Li, Xiao Jiang, Lin Gan, Guangwen Yang, Lixing You, Zhen Wang, Li Li, Nai-Le Liu, Jelmer J.”
Quantum entropy and central limit theorem
Bu, K., Gu, W., & Jaffe, A. M. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2304589120 · Cited by 51
“[44] Han-Sen Zhong, Yu-Hao Deng, Jian Qin, Hui Wang, MingCheng Chen, Li-Chao Peng, Yi-Han Luo, Dian Wu, SiQiu Gong, Hao Su, Yi Hu, Peng Hu, Xiao-Yan Yang, Wei-Jun Zhang, Hao Li, Yuxuan Li, Xiao Jiang, Lin Gan, Guangwen Yang, Lixing You, Zhen Wang, Li Li, Nai-Le Liu, Jelmer J.”
Analog Photonics Computing for Information Processing, Inference, and Optimization
Stroev, N., & Berloff, N. G. (2023) · Advanced Quantum Technologies · doi:10.1002/qute.202300055 · Cited by 42
Li, J., Xie, Z., Li, Y., Liang, Y., Li, Z., & Li, T. (2024) · Science China Physics Mechanics and Astronomy · doi:10.1007/s11433-023-2245-9 · Cited by 37
Self-citationQuantum Error Mitigated Classical Shadows
Jnane, H., Steinberg, J., Cai, Z., Nguyen, H. C., & Koczor, B. (2024) · PRX Quantum · doi:10.1103/prxquantum.5.010324 · Cited by 49
Quantum computing for fusion energy science applications
Joseph, I., Shi, Y. C., Porter, M. D., Castelli, A. R., Geyko, V., Graziani, F. R., Libby, S. B., & DuBois, J. L. (2023) · Physics of Plasmas · doi:10.1063/5.0123765 · Cited by 42
“In fact, in recent years, there have been claims that some demonstrations have already passed the point of quantum supremacy, 10–12 the point at which quantum computers can surpass even the world’s best supercomputers at performing certain computational tasks.”
BackgroundQuantum machine learning with Adaptive Boson Sampling via post-selection
Hoch, F., Caruccio, E., Rodari, G., Francalanci, T., Suprano, A., Giordani, T., Carvacho, G., Spagnolo, N., Koudia, S., Proietti, M., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-55877-z · Cited by 43
Perturbative Quantum Simulation
Sun, J., Endo, S., Lin, H., Hayden, P. M., Vedral, V., & Yuan, X. (2022) · Physical Review Letters · doi:10.1103/physrevlett.129.120505 · Cited by 36
Hilaire, P., Vidro, L., Eisenberg, H. S., & Economou, S. E. (2023) · Quantum · doi:10.22331/q-2023-04-27-992 · Cited by 42
“Photonic quantum processors are one of the leading contenders for the implementation of such a quantum computer as they are one of the only two platforms that have already reached the milestone of demonstrating quantum advantage [1, 2], together with superconducting circuits [3].”
BackgroundMitigating Barren Plateaus of Variational Quantum Eigensolvers
Liu, X., Liu, G., Zhang, H.-K., Huang, J., & Wang, X. (2024) · IEEE Transactions on Quantum Engineering · doi:10.1109/tqe.2024.3383050 · Cited by 55
“Recent results [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24] have shown that it is promising to realize quantum advantages with such devices in specific tasks.”
Superconducting single-photon detector with a speed of 5 GHz and a photon number resolution of 61
Zhang, T., Huang, J., Zhang, X., Ding, C., Yu, H., Xiao, Y., Lv, C., Liu, X., Wang, Z., You, L., et al. (2024) · Photonics Research · doi:10.1364/prj.522714 · Cited by 33
“…owing to their near-unit efficiency [10 – 12], ultralow dark count rate (CR) [13,14], and excellent timing resolution [15]; more-over, they enable numerous novel applications, such as quantum communication [16 – 18], optical quantum computation [6,19], and quantum principle verification [20,21].”
Self-citationScalable photonic quantum technologies
Wang, H., Ralph, T. C., Renema, J. J., Lu, C., & Pan, J.-W. (2025) · Nature Materials · doi:10.1038/s41563-025-02306-7 · Cited by 54
“…quantum computer Jiuzhang 1.0, which sampled up to 76 photons from 100 modes 142 ; then Jiuzhang 2.0, which sampled up to 113 photons from 144 modes 143 ; Jiuzhang 3.0, which scaled up to 255 photons sampled from 1,152 modes 144 ; and Borealis, which sampled up to 219 photons in 216 modes 145 ,…”
InfluentialSelf-citationQuantum pattern recognition on real quantum processing units
Das, S., Zhang, J., Martina, S., Suter, D., & Caruso, F. (2023) · Quantum Machine Intelligence · doi:10.1007/s42484-022-00093-x · Cited by 34
Time and frequency as quantum continuous variables
Fabre, N., Keller, A., & Milman, P. (2022) · Physical Review A · doi:10.1103/physreva.105.052429 · Cited by 39
40 km fiber transmission of squeezed light measured with a real local oscillator
Suleiman, I., Nielsen, J. A. H., Guo, X., Jain, N., Neergaard-Nielsen, J. S., Gehring, T., & Andersen, U. L. (2022) · Quantum Science and Technology · doi:10.1088/2058-9565/ac7ba1 · Cited by 32
“…has been the engine of numerous quantum sensing experiments, such as the quantum-enhanced measurements of gravitational waves [4] and vibrational modes of molecules [5, 6], and recent quantum computing models including Gaussian boson sampling [7, 8] and measurement-based quantum computing [9–11].”
Adiabatic Quantum Computing for Multi Object Tracking
Zaech, J.-N., Liniger, A., Danelljan, M., Dai, D., & Gool, L. V. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00861 · Cited by 34
“Even though implementations of quantum computers are still heavily experimental, some problems have already been shown to profit from them, including the sampling of pseudo-random quantum circuits [2,53] and Gaussian boson sampling [58].”
BackgroundInterpretable Quantum Advantage in Neural Sequence Learning
Anschuetz, E. R., Hu, H.-Y., Huang, J.-L., & Gao, X. (2023) · PRX Quantum · doi:10.1103/prxquantum.4.020338 · Cited by 43
The potential of multidimensional photonic computing
Bente, I., Taheriniya, S., Lenzini, F., Brückerhoff‐Plückelmann, F., Kues, M., Bhaskaran, H., Wright, C. D., & Pernice, W. H. P. (2025) · Nature Reviews Physics · doi:10.1038/s42254-025-00843-3 · Cited by 36
Liu, Z., Shen, P.-X., Li, W., Duan, L.-M., & Deng, D.-L. (2022) · Quantum Science and Technology · doi:10.1088/2058-9565/aca55d · Cited by 29
Superconducting quantum computers: who is leading the future?
AbuGhanem, M. (2025) · EPJ Quantum Technology · doi:10.1140/epjqt/s40507-025-00405-7 · Cited by 38
High-dimensional quantum information processing on programmable integrated photonic chips
Chi, Y., Yu, Y., Gong, Q., & Wang, J. (2023) · Science China Information Sciences · doi:10.1007/s11432-022-3602-0 · Cited by 31
“In the past decades, it has witnessed the closure of loopholes in Bell tests [1,2], the establishment of long-distance secured quantum communications [3,4], and the realization of quantum computational advantages [5,6].”
BackgroundHeralded nonlocal quantum gates for distributed quantum computation in a decoherence-free subspace
Su, W., Qin, W., Miranowicz, A., Li, T., & Nori, F. (2024) · Physical Review A · doi:10.1103/physreva.110.052612 · Cited by 27
Spin–Lattice Relaxation Decoherence Suppression in Vanishing Orbital Angular Momentum Qubits
Buch, C. D., Kundu, K., Marbey, J., van Tol, J., Weihe, H., Hill, S., & Piligkos, S. (2022) · Journal of the American Chemical Society · doi:10.1021/jacs.2c07057 · Cited by 27
Distinguishability and mixedness in quantum interference
Jones, A. E., Kumar, S., D’Aurelio, S. E., Bayerbach, M. J., Menssen, A. J., & Barz, S. (2023) · Physical Review A · doi:10.1103/physreva.108.053701 · Cited by 30
Proposal of Low-Loss Non-Volatile Mid-Infrared Optical Phase Shifter Based on Ge2Sb2Te3S2
Miyatake, Y., Makino, K., Tominaga, J., Miyata, N., Nakano, T., Okano, M., Toprasertpong, K., Takagi, S., & Takenaka, M. (2023) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2023.3235865 · Cited by 26
Information processing at the speed of light
AbuGhanem, M. (2024) · Frontiers of Optoelectronics · doi:10.1007/s12200-024-00133-3 · Cited by 31
Training Variational Quantum Circuits with CoVaR: Covariance Root Finding with Classical Shadows
Boyd, G., & Koczor, B. (2022) · Physical Review X · doi:10.1103/physrevx.12.041022 · Cited by 27
Phase-Programmable Gaussian Boson Sampling Using Stimulated Squeezed Light
Zhong, H.-S., Deng, Y.-H., Qin, J., Wang, H., Chen, M.-C., Peng, L.-C., Luo, Y.-H., Wu, D., Gong, S.-Q., Su, H., et al. (2022) · Optica Advanced Photonics Congress · doi:10.1364/iprsn.2022.itu3b.1 · Cited by 25
Self-citationSingle-Electron Spin Qubits in Silicon for Quantum Computing
Hu, G., Huang, W., Cai, R., Wang, L., Yang, C. H., Cao, G., Xue, X., Huang, P., & He, Y. (2025) · Intelligent Computing · doi:10.34133/icomputing.0115 · Cited by 24
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
Dipolar-Coupled Entangled Molecular 4f Qubits
Bode, B. E., Fusco, E., Nixon, R., Buch, C. D., Weihe, H., & Piligkos, S. (2023) · Journal of the American Chemical Society · doi:10.1021/jacs.2c10902 · Cited by 26
Breaking universal limitations on quantum conference key agreement without quantum memory
Li, C., Fu, Y., Liu, W., Xie, Y.-M., Li, B.-H., Zhou, M.-G., Yin, H., & Chen, Z. (2023) · Communications Physics · doi:10.1038/s42005-023-01238-5 · Cited by 24
Effect of partial distinguishability on quantum supremacy in Gaussian Boson sampling
Shi, J., & Byrnes, T. (2022) · npj Quantum Information · doi:10.1038/s41534-022-00557-9 · Cited by 37
“(23) closely, we find that by multiplying an additional factorial, the product on the right side forms a multinomial distribution:”
BackgroundInfluentialFundamental Limitation on the Detectability of Entanglement
Liu, P., Liu, Z., Chen, S., & Ma, X. (2022) · Physical Review Letters · doi:10.1103/physrevlett.129.230503 · Cited by 23
Houde, M., & Quesada, N. (2023) · AVS Quantum Science · doi:10.1116/5.0133009 · Cited by 24
Spoofing Cross-Entropy Measure in Boson Sampling
Oh, C., Jiang, L., & Fefferman, B. (2023) · Physical Review Letters · doi:10.1103/physrevlett.131.010401 · Cited by 25
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
“[6] Han-Sen Zhong, Yu-Hao Deng, Jian Qin, Hui Wang, Ming-Cheng Chen, Li-Chao Peng, Yi-Han Luo, Dian Wu, Si-Qiu Gong, Hao Su, et al.”
BackgroundSelf-citationSimulating lossy Gaussian boson sampling with matrix-product operators
Liu, M., Oh, C., Liu, J., Jiang, L., & Alexeev, Y. (2023) · Physical Review A · doi:10.1103/physreva.108.052604 · Cited by 28
“This allowed recent experimental demonstrations of GBS to claim quantum supremacy [27, 28, 50].”
BackgroundProgrammable Photonic Quantum Circuits with Ultrafast Time-Bin Encoding
Bouchard, F., Fenwick, K. L., Bonsma-Fisher, K., England, D. G., Bustard, P. J., Heshami, K., & Sussman, B. (2024) · Physical Review Letters · doi:10.1103/physrevlett.133.090601 · Cited by 26
Tensor networks for quantum computing
Berezutskii, A. V., Liu, M., Acharya, A., Ellerbrock, R., Gray, J., Haghshenas, R., He, Z., Khan, A., Kuzmin, V., Lyakh, D. I., et al. (2025) · Nature Reviews Physics · doi:10.1038/s42254-025-00853-1 · Cited by 62
Faster classical boson sampling
Clifford, P., & Clifford, R. (2024) · Physica Scripta · doi:10.1088/1402-4896/ad4688 · Cited by 36
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
“Recent progress in noisy, intermediate-scale quantum ( NISQ ) computers [1–3] lead to a fast development of algorithms suited for use on these machines [4] with the purpose of achieving a quantum advantage in various areas: physics, machine learning, quantum chemistry, and combinatorial…”
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
Experimentally Finding Dense Subgraphs Using a Time-Bin Encoded Gaussian Boson Sampling Device
Sempere-Llagostera, S., Patel, R. B., Walmsley, I. A., & Kolthammer, W. S. (2022) · Physical Review X · doi:10.1103/physrevx.12.031045 · Cited by 22
Metadata
- Authors
- Zhong, H.-S., Deng, Y.-H., Qin, J., Wang, H., Chen, M.-C., Peng, L.-C., Luo, Y.-H., Wu, D., Gong, S.-Q., Su, H., Hu, Y., Hu, P., Yang, X.-Y., Zhang, W.-J., Li, H., Li, Y., Jiang, X., Gan, L., Yang, G., You, L., Wang, Z., Li, L., Liu, N.-L., Renema, J. J., Lu, C.-Y., & Pan, J.-W.
- Affiliations
- University of Science and Technology of China (CN)
- Chinese Academy of Sciences (CN)
- Beijing National Research Center for Information Science and Technology (CN)
- University of Twente (NL)
- ORCID
- Venue
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- Published
- 2021-10-25
- Volume
- 127
- Issue
- 18
- Pages
- 180502-180502
- ISSN
- 0031-9007 (ISSN-L)
- 1079-7114
- OpenAlex
- W3176603110
- Cited by
- 434
- References
- 79
- Citations by year
- 2026: 45 · 2025: 87 · 2024: 91 · 2023: 113 · 2022: 85 · 2021: 13
- FWCI
- 26.69
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Physical Review Letters (publishedVersion)
- PubMed (publishedVersion)
- University of Twente Research Information (publishedVersion, other-oa)
- University of Twente Research Information (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- CERN Document Server (European Organization for Nuclear Research) (submittedVersion, cc-by)
- Figshare (submittedVersion, cc-by)
- University of Twente Research Information (submittedVersion)
- University of Twente Research Information (submittedVersion, other-oa)
- arXiv (Cornell University) (cc-by)
- Funders
- National Natural Science Foundation of China
- National Key Research and Development Program of China (2019YFA0308700)
- Chinese Academy of Sciences
- Science and Technology Commission of Shanghai Municipality (2019SHZDZX01)
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek
- Anhui Initiative in Quantum Information Technologies
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum Information and Cryptography
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum Mechanics and Applications
- Keywords
- Gaussian boson sampling, photonic quantum computing, quantum advantage, Hilbert space dimension
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