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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.”
From Quantum computational advantage with a programmable photonic processor (2022) · cites this paper as [2]
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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.”
From Quantum computational advantage with a programmable photonic processor (2022) · cites this paper as [2]
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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
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Cited by
Sorted by citations
Items 101–200 of 434
OneQ: A Compilation Framework for Photonic One-Way Quantum Computation
Zhang, H., Wu, A., Wang, Y., Li, G., Shapourian, H., Shabani, A., & Ding, Y. (2023) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1145/3579371.3589047 · Cited by 24
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Doggen, E. V. H., Gefen, Y., Gornyi, I. V., Mirlin, A. D., & Polyakov, D. G. (2023) · Physical review. B./Physical review. B · doi:10.1103/physrevb.107.214203 · Cited by 20
Exponentially Improved Dispersive Qubit Readout with Squeezed Light
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Experimental Demonstration of Gaussian Boson Sampling with Displacement
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BackgroundReal‐Time Superresolution Interferometric Measurement Enabled by Structured Nonlinear Optics
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A Dynamically Programmable Quantum Photonic Microprocessor for Graph Computation
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Self-citationEntanglement-Enabled Advantage for Learning a Bosonic Random Displacement Channel
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BackgroundShedding Light on the Future: Exploring Quantum Neural Networks through Optics
Yu, S., Jia, Z., Zhang, A., Mer, E., Li, Z., Crescimanna, V., Chen, K., Patel, R. B., Walmsley, I. A., & Kaszlikowski, D. (2024) · Advanced Quantum Technologies · doi:10.1002/qute.202400074 · Cited by 18
“For instance, it allows for the preparation of large-scale cluster states with distinct advantages [73, 74], and achieving programmable Gaussian Boson sampling circuit [8, 9, 75].”
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Self-citationVariational Quantum Algorithm Applied to Collision Avoidance of Unmanned Aerial Vehicles
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Characterization of multimode linear optical networks
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“Note that the same type of photon-number correlations have been used to benchmark computationally intractable Gaussian boson sampling experiments [28, 29].”
When Quantum Computation Meets Data Science: Making Data Science Quantum
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“It was reported in [78] that the second generation of Jiuzhang was built with up to 113 qubits and enhanced performance.”
BackgroundSharing quantum nonlocality in star network scenarios
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“Quantum advantages have been experimentally demonstrated using linear optical system [5, 6], but the setup is usually very bulky.”
Threshold detection statistics of bosonic states
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Emulation of Quantum Algorithms Using CMOS Analog Circuits
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“Google has already claimed to have shown quantum supremacy in its seminal paper, as have others [4], [5], [6].”
BackgroundOn-Chip Quantum Information Processing with Distinguishable Photons
Yard, P., Jones, A. E., Paesani, S., Maïnos, A., Bulmer, J. F. F., & Laing, A. (2024) · Physical Review Letters · doi:10.1103/physrevlett.132.150602 · Cited by 19
Efficient qudit based scheme for photonic quantum computing
Karácsony, M., Oroszlány, L., & Zimborás, Z. (2024) · SciPost Physics Core · doi:10.21468/scipostphyscore.7.2.032 · Cited by 21
“[ 23–25 ] ), however, the realization of universal photonic quantum computation still seems far from reality.”
Quantum separability criteria based on realignment moments
Zhang, T., Jing, N., & Fei, S.-M. (2022) · Quantum Information Processing · doi:10.1007/s11128-022-03630-6 · Cited by 15
Page curves and typical entanglement in linear optics
Iosue, J. T., Ehrenberg, A., Hangleiter, D., Deshpande, A., & Gorshkov, A. V. (2023) · Quantum · doi:10.22331/q-2023-05-23-1017 · Cited by 17
Piquasso: A Photonic Quantum Computer Simulation Software Platform
Kolarovszki, Z., Rybotycki, T., Rakyta, P., Kaposi, Á., Poór, B., Jóczik, S., Nagy, D., Varga, H., El-Safty, K. H., Morse, G., et al. (2025) · Quantum · doi:10.22331/q-2025-04-15-1708 · Cited by 22
“…proposals, the relevance of photonic quantum computers increased due to recent demonstrations of possible photonic quantum advantage schemes [1–3] and the develop-Zoltán Kolarovszki: kolarovszki.zoltan@wigner.hun-ren.hu Zoltán Zimborás: zimboras.zoltan@wigner.hun-ren.hu ment of feasible…”
Quantum prospects for hybrid thin-film lithium niobate on silicon photonics
Adcock, J. C., & Ding, Y. (2022) · Frontiers of Optoelectronics · doi:10.1007/s12200-022-00006-7 · Cited by 14
Quantum orbital angular momentum in fibers: A review
Wang, J., Wang, Q., Liu, J., & Lyu, D. (2022) · AVS Quantum Science · doi:10.1116/5.0101179 · Cited by 16
All-photonic quantum repeater for multipartite entanglement generation
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Single photon detection performance of highly disordered NbTiN thin films
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“Introduction Superconducting nanowire single photon detectors (SNSPDs) with high system detection efficiency, low timing jitter, low dark counts, and high counting rate have widely been applied in broad fields for both quantum and classical realms, such as quantum optics [1-3], quantum information processing [4-7].”
BackgroundSelf-citationSimulating 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
Intelligent certification for quantum simulators via machine learning
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“In the NISQ era, the random circuit sampling and Boson sampling problems are realized to achieve quantum supremacy (also called quantum advantage) based on super-conducting quantum circuit and photonics platform, respectively 9 – 11 .”
BackgroundSelf-citationChaotic Image Encryption Based on Boson Sampling
Shi, J., Zhao, T., Wang, Y., Feng, Y., & Wu, J. (2022) · Advanced Quantum Technologies · doi:10.1002/qute.202200104 · Cited by 13
Continuous-variable quantum approximate optimization on a programmable photonic quantum processor
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“Physical Review Letters, 119(17) 170501, (2017).”
Henry, A., Raghunathan, R., Ricard, G., Lefaucher, B., Miatto, F. M., Belabas, N., Zaquine, I., & Alléaume, R. (2023) · Physical Review A · doi:10.1103/physreva.107.062610 · Cited by 14
Deterministic optimal quantum cloning via a quantum-optical neural network
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Optimizing Gate Decomposition for High-Level Quantum Programming
Rosa, E. C. R., Duzzioni, E. I., & de Santiago, R. (2025) · Quantum · doi:10.22331/q-2025-03-12-1659 · Cited by 18
“Despite achieving the milestone of quantum advantage [37], where problems that would take days to solve on classical supercomputers can be solved in seconds [2, 30, 47, 50, 51], quantum computing technology is not yet ready to address industrial-scale problems in production environments.”
Walking through Hilbert Space with Quantum Computers
Jiang, T., Zhang, J., Baumgarten, M. K. A., Chen, M., Dinh, H. Q., Ganeshram, A., Maskara, N., Ni, A. Z., & Lee, J. (2025) · Chemical Reviews · doi:10.1021/acs.chemrev.4c00508 · Cited by 20
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Self-citationExperimental simulation of loop quantum gravity on a photonic chip
van der Meer, R., Huang, Z., Anguita, M. C., Qu, D., Hooijschuur, P., Liu, H., Han, M., Renema, J. J., & Cohen, L. (2023) · npj Quantum Information · doi:10.1038/s41534-023-00702-y · Cited by 13
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Resource estimations for the Hamiltonian simulation in correlated electron materials
Kanno, S., Endo, S., Utsumi, T., & Tada, T. (2022) · Physical Review A · doi:10.1103/physreva.106.012612 · Cited by 11
Continuous-variable nonclassicality certification under coarse-grained measurement
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Deterministic generation of arbitrary n -photon states in a waveguide-QED system
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Quantum-Inspired Classical Algorithm for Graph Problems by Gaussian Boson Sampling
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Nonadiabatic geometric quantum gates with on-demand trajectories
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“A recent milestone in this field is that the quantum computational advantage of quantum computers has been successfully demonstrated [7–10]; however, it is still difficult to build a practical quantum computer.”
BackgroundLeapfrogging Sycamore: harnessing 1432 GPUs for 7× faster quantum random circuit sampling
Zhao, X.-H., Zhong, H.-S., Pan, F., Chen, Z., Fu, R., Su, Z. B., Xie, X., Zhao, C., Zhang, P., Ouyang, W., et al. (2024) · National Science Review · doi:10.1093/nsr/nwae317 · Cited by 23
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Bounding the speedup of the quantum-enhanced Markov-chain Monte Carlo algorithm
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Toward scalable fault-tolerant photonic quantum computers
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Role of Collective Information in Networks of Quantum Operating Agents
Yukalov, V. I., Yukalova, E. P., & Sornette, D. (2022) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2022.127365 · Cited by 11
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“Additionally, to reduce the side effects of residual ZZ coupling between qubits during idling or single-qubit gate operations, we bias the tunable couplers near their turn-off points [4], where the ZZ coupling is negligible.”
BackgroundMethodImproving the efficiency of learning-based error mitigation
Czarnik, P., McKerns, M. M., Sornborger, A. T., & Cincio, Ł. (2025) · Quantum · doi:10.22331/q-2025-05-05-1727 · Cited by 28
“[2] Han-Sen Zhong, Yu-Hao Deng, Jian Qin, Hui Wang, et al.”
Semi-Device-Independent Characterization of Multiphoton Indistinguishability
Rodari, G., Novo, L., Albiero, R., Suprano, A., Tavares, C., Caruccio, E., Hoch, F., Giordani, T., Carvacho, G., Gardina, M., et al. (2025) · PRX Quantum · doi:10.1103/prxquantum.6.020340 · Cited by 20
Demonstration of Algorithmic Quantum Speedup for an Abelian Hidden Subgroup Problem
Singkanipa, P., Kasatkin, V., Zhou, Z., Quiroz, G., & Lidar, D. A. (2025) · Physical Review X · doi:10.1103/physrevx.15.021082 · Cited by 10
Cracking the Quantum Advantage Threshold for Gaussian Boson Sampling
Popova, A. S., & Rubtsov, A. N. (2022) · Quantum 2.0 Conference and Exhibition · doi:10.1364/quantum.2022.qw2a.15 · Cited by 10
“5 21 , in other words, half of the injected photons are lost.”
Atomic boson sampling in a Bose-Einstein-condensed gas
Kocharovsky, V. V., Kocharovsky, V. V., & Tarasov, S. V. (2022) · Physical Review A · doi:10.1103/physreva.106.063312 · Cited by 13
Exploring shallow-depth boson sampling: Toward a scalable quantum advantage
Go, B., Oh, C., Jiang, L., & Jeong, H. (2024) · Physical Review A · doi:10.1103/physreva.109.052613 · Cited by 10
“[8] Han-Sen Zhong, Yu-Hao Deng, Jian Qin, Hui Wang, Ming-Cheng Chen, Li-Chao Peng, Yi-Han Luo, Dian”
BackgroundBenchmarking 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
“Several physical implementations for quantum computers have been explored, each employing different technologies as qubits, such as trapped ion [58, 59], superconducting circuits [60–62], photonic [63, 64], and cold atoms [65, 66].”
Fast, 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
Self-citationJiawei, 王. W., Pei, 张. Z., & Cai, Y. (2024) · Chinese Journal of Lasers · doi:10.3788/cjl240894 · Cited by 9
Verifiable 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 20
Full Quantum Process Tomography of a Universal Entangling Gate on an IBM’s Quantum Computer
AbuGhanem, M. (2025) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-025-10391-8 · Cited by 14
Enhancement of a silicon waveguide single photon source by temporal multiplexing
Adcock, J. C., Bacco, D., & Ding, Y. (2022) · Quantum Science and Technology · doi:10.1088/2058-9565/ac57f2 · Cited by 9
“[3, 4], as well as the generation of large entangled states, with 12 photon Greenberger–Horne–Zeilinger”
BackgroundQuantum variational learning for entanglement witnessing
Scala, F., Mangini, S., Macchiavello, C., Bajoni, D., & Gerace, D. (2022) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9892080 · Cited by 11
Certification of Gaussian Boson Sampling via graphs feature vectors and kernels
Giordani, T., Mannucci, V., Spagnolo, N., Fumero, M., Rampini, A., Rodolà, E., & Sciarrino, F. (2022) · Quantum Science and Technology · doi:10.1088/2058-9565/ac969b · Cited by 10
Lyons, A., Choi, S., & Altman, E. (2023) · Physical review. B./Physical review. B · doi:10.1103/physrevb.107.174304 · Cited by 9
“[4] 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.”
Ryu, C. J., Na, D.-Y., & Chew, W. C. (2023) · Physical Review A · doi:10.1103/physreva.107.063707 · Cited by 9
Gain-Induced Group Delay in Spontaneous Parametric Down-Conversion
Thekkadath, G., Houde, M., England, D. G., Bustard, P. J., Bouchard, F., Quesada, N., & Sussman, B. (2024) · Physical Review Letters · doi:10.1103/physrevlett.133.203601 · Cited by 10
Simulating macroscopic quantum correlations in linear networks
Dellios, A. S., Drummond, P. D., Opanchuk, B., Teh, R. Y., & Reid, M. D. (2021) · Physics Letters A · doi:10.1016/j.physleta.2021.127911 · Cited by 9
Multi-Qubit Bose–Einstein Condensate Trap for Atomic Boson Sampling
Tarasov, S. V., Shannon, W., Kocharovsky, V. V., & Kocharovsky, V. V. (2022) · Entropy · doi:10.3390/e24121771 · Cited by 10
Quantum Volume for Photonic Quantum Processors
Zhang, Y., Niu, D., Shabani, A., & Shapourian, H. (2023) · Physical Review Letters · doi:10.1103/physrevlett.130.110602 · Cited by 11
Active learning on a programmable photonic quantum processor
Ding, C., Xu, X.-Y., Niu, Y.-F., Zhang, S., Huang, H.-L., & Bao, W. (2023) · Quantum Science and Technology · doi:10.1088/2058-9565/acdd92 · Cited by 10
Experimental Quantum‐Enhanced Machine Learning in Spin‐Based Systems
Wang, X., Lin, Z., Che, L., Chen, H.-Y., & Lu, D. (2022) · Advanced Quantum Technologies · doi:10.1002/qute.202200005 · Cited by 9
Schwaller, N., Park, G., Okamoto, R., & Takeuchi, S. (2022) · Physical Review A · doi:10.1103/physreva.106.043719 · Cited by 9
Bosehedral: Compiler Optimization for Bosonic Quantum Computing
Zhou, J., Liu, Y., Shi, Y., Javadi-Abhari, A., & Li, G. (2024) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1109/isca59077.2024.00028 · Cited by 12
“Recently, the quantum advantage [34, 57, 58] has been experimentally demonstrated on several Bosonic QC platforms.”
BackgroundGeneralized interference of fermions and bosons
Spivak, D., Niu, M. Y., Sanders, B. C., & de Guise, H. (2022) · Physical Review Research · doi:10.1103/physrevresearch.4.023013 · Cited by 9
“[16] 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.”
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
Material-Inherent Noise Sources in Quantum Information Architecture
Yang, H., & Kim, N. Y. (2023) · Materials · doi:10.3390/ma16072561 · Cited by 11
Multi-class classification based on quantum state discrimination
Giuntini, R., Arango, A. C. G., Freytes, H., Holik, F., & Sergioli, G. (2023) · Fuzzy Sets and Systems · doi:10.1016/j.fss.2023.03.012 · Cited by 10
OnePerc: A Randomness-aware Compiler for Photonic Quantum Computing
Zhang, H., Ruan, J., Shapourian, H., Kompella, R. R., & Ding, Y. (2024) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3620666.3651372 · Cited by 12
Efficient Classical Shadow Tomography through Many-body Localization Dynamics
Zhou, T.-G., & Zhang, P. (2024) · Quantum · doi:10.22331/q-2024-09-11-1467 · Cited by 11
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 11
“A further technique is to compare the correlations between modes present in the PNR or threshold detection samples with those predicted by the ground truth model, and by adversarial models or samplers [8, 13, 14].”
MethodPhoton-number moments and cumulants of Gaussian states
Cardin, Y., & Quesada, N. (2024) · Quantum · doi:10.22331/q-2024-11-13-1521 · Cited by 11
“Since in our graph representation vertices i and i + ℓ represent the same mode, we will be interested in the Y -alternating walks over the perfect matching represented graphically in Fig.”
Quantum technology: a financial risk assessment
Nguyen, P.-N. (2025) · Digital Finance · doi:10.1007/s42521-025-00127-6 · Cited by 12
“With 113 detected photons, they estimated Jiuzhang 2.0 could solve the problem approximately 10 24 times faster than classical supercomputers (Zhong et al. 2021).”
InfluentialQuantum Leap for Quantum Primacy
Sanders, B. C. (2021) · Physics · doi:10.1103/physics.14.147 · Cited by 8
The origin of Franson-type nonlocal correlation
Byoung Seung, H. A. M. (2022) · AVS Quantum Science · doi:10.1116/5.0083056 · Cited by 18
“Ever since the well-known thought experiment by Einstein, Podolsky and Rosen (EPR) in 1935 [2], EPR has been a key aspect of quantum information science and technologies in computing [3-5], communications [6-9], and sensing areas [10-12].”
Fitzke, E., Niederschuh, F., & Walther, T. (2023) · APL Photonics · doi:10.1063/5.0129638 · Cited by 7
Surpassing Sycamore: Achieving Energetic Superiority Through System-Level Circuit Simulation
Fu, R., Su, Z. B., Zhong, H.-S., Zhao, X., Zhang, J., Pan, F., Zhang, P., Zhao, X., Chen, M.-C., Lu, C., et al. (2024) · International Conference for High Performance Computing, Networking, Storage and Analysis (SC) · doi:10.1109/sc41406.2024.00085 · Cited by 7
Self-citationKim, S. J., Kim, Y., Yoon, Y.-D., Jeon, S., Kim, W.-J., & Sohn, Y.-I. (2024) · Physical Review Applied · doi:10.1103/physrevapplied.22.064092 · Cited by 9
Buchs, G., Beck, T. L., Bennink, R. S., Claudino, D., Delgado, A., Fadel, N. A., Groszkowski, P., Hamilton, K. E., Humble, T. S., Kumar, N., et al. (2026) · Future Generation Computer Systems · doi:10.1016/j.future.2026.108487 · Cited by 11
“There have been multiple claims of quantum advantage [285, 286, 287, 288, 289] (also known as quantum supremacy/primacy [290]), in which a QC can perform a task at a significantly lower cost than classical computers.”
Recent progress towards large-scale integrated photonic quantum computation
zhu, H., Chen, T., Ma, H., Zhao, Z., Jia, G. U. O., Liang, Q., Tu, Y., He, T., Luo, W., Wang, B., et al. (2026) · npj Nanophotonics · doi:10.1038/s44310-026-00114-8 · Cited by 12
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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