Photon loss is destructive to the performance of quantum photonic devices and therefore suppressing the effects of photon loss is paramount to photonic quantum technologies. We present two schemes to mitigate the effects of photon loss for a Gaussian Boson Sampling device, in particular, to improve the estimation of the sampling probabilities. Instead of using error correction codes which are expensive in terms of their hardware resource overhead, our schemes require only a small amount of hardware modifications or even no modification. Our loss-suppression techniques rely either on collecting additional measurement data or on classical post-processing once the measurement data is obtained. We show that with a moderate cost of classical post processing, the effects of photon loss can be significantly suppressed for a certain amount of loss. The proposed schemes are thus a key enabler for applications of near-term photonic quantum devices.
 A. G. Fowler, M. Mariantoni, J. M. Martinis, and A. N. Cleland, Surface codes: Towards practical large-scale quantum computation, Phys. Rev. A 86, 032324 (2012).
 S. Boixo, S. V. Isakov, V. N. Smelyanskiy, R. Babbush, N. Ding, Z. Jiang, M. J. Bremner, J. M. Martinis, and H. Neven, Characterizing quantum supremacy in near-term devices, Nature Physics 14, 595 (2018).
 M. J. Bremner, R. Jozsa, and D. J. Shepherd, Classical simulation of commuting quantum computations implies collapse of the polynomial hierarchy, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 467, 459 (2011).
 M. J. Bremner, A. Montanaro, and D. J. Shepherd, Average-case complexity versus approximate simulation of commuting quantum computations, Phys. Rev. Lett. 117, 080501 (2016).
 S. Aaronson, A. Arkhipov, The computational complexity of linear optics, Proceedings of the forty-third annual ACM symposium on Theory of computing, 333-342 (2011).
 C. S. Hamilton, R. Kruse, L. Sansoni, S. Barkhofen, C. Silberhorn, Christine, and I. Jex, Gaussian Boson Sampling, Phys. Rev. Lett. 119, 170501 (2017).
 S. Rahimi-Keshari, A. P. Lund, and T. C. Ralph, What Can Quantum Optics Say about Computational Complexity Theory?, Phys. Rev. Lett. 114, 060501 (2015).
 S. Rahimi-Keshari, T. C. Ralph, and C. M. Caves, Sufficient Conditions for Efficient Classical Simulation of Quantum Optics, Phys. Rev. X 6, 021039 (2016).
 A. Peruzzo, J. McClean, P. Shadbolt, M. Yung, X. Zhou, P. J. Love, A. Aspuru-Guzik, and J. L. O'brien, A variational eigenvalue solver on a photonic quantum processor, Nature Communications 5, 4213 (2014).
 K. Temme, S. Bravyi, and J. M. Gambetta, Error Mitigation for Short-Depth Quantum Circuits, Phys. Rev. Lett. 119, 180509 (2017).
 A. Kandala, K. Temme, A. D. Córcoles, A. Mezzacapo, J. M. Chow, and J. M. Gambetta, Error mitigation extends the computational reach of a noisy quantum processor, Nature 567, 491 (2019).
 C. Song, J. Cui, H. Wang, J. Hao, H. Feng, H. and Li, Ying, Quantum computation with universal error mitigation on a superconducting quantum processor, Science Advances 5, (2019).
 S. Zhang, Y. Lu, K. Zhang, W. Chen, Y. Li, J. Zhang, and K. Kim, Error-mitigated quantum gates exceeding physical fidelities in a trapped-ion system, Nature Communications 11, 1 (2020).
 R. Sagastizabal, X. Bonet-Monroig, M. Singh, M. A. Rol, C. C. Bultink, X. Fu, C. H. Price, V. P. Ostroukh, N. Muthusubramanian, A. Bruno, M. Beekman, N. Haider, T. E. O'Brien, and L. DiCarlo, Experimental error mitigation via symmetry verification in a variational quantum eigensolver, Phys. Rev. A 100, 010302(R) (2019).
 S. McArdle, X. Yuan, and S. Benjamin, Error-Mitigated Digital Quantum Simulation, Phys. Rev. Lett. 122, 180501 (2019).
 J. R. McClean, J. Romero, R. Babbush, and A. Aspuru-Guzik, The theory of variational hybrid quantum-classical algorithms, New Journal of Physics 18, 023023 (2016).
 Y. Chen, M. Farahzad, S. Yoo, and T. Wei, Detector tomography on IBM quantum computers and mitigation of an imperfect measurement, Phys. Rev. A 100, 052315 (2019).
 H. Kwon, and J. Bae, A hybrid quantum-classical approach to mitigating measurement errors in quantum algorithms, IEEE Transactions on Computers (2020).
 J. R. McClean, M. E. Kimchi-Schwartz, J. Carter, and W. A. de Jong, Hybrid quantum-classical hierarchy for mitigation of decoherence and determination of excited states, Phys. Rev. A 95, 042308 (2017).
 J. Sun, X. Yuan, T. Tsunoda, V. Vedral, S. C. Bejamin, and S. Endo, Mitigating Realistic Noise in Practical Noisy Intermediate-Scale Quantum Devices, Phys. Rev. Applied 15, 034026 (2021).
 J. Arrazola, and T. R. Bromley, Using Gaussian Boson Sampling to Find Dense Subgraphs, Phys. Rev. Lett. 121, 030503 (2018).
 M. Schuld, K. Brádler, R. Israel, D. Su, and B. Gupt, Measuring the similarity of graphs with a Gaussian boson sampler, Phys. Rev. A 101, 032314 (2020).
 C. Weedbrook, S. Pirandola, R. García-Patrón, N. J. Cerf, T. C. Ralph, J. H. Shapiro, and S. Lloyd, Gaussian quantum information, Rev. Mod. Phys. 84, 621 (2012).
 K. Brádler, P. Dallaire-Demers, P. Rebentrost, D. Su, and C. Weedbrook, Gaussian boson sampling for perfect matchings of arbitrary graphs, Phys. Rev. A 98, 032310 (2018).
 H. Qi, D. J. Brod, N. Quesada, and R. García-Patrón, Regimes of Classical Simulability for Noisy Gaussian Boson Sampling, Phys. Rev. Lett. 124, 100502 (2020).
 W. R. Clements, P. C. Humphreys, B. J. Metcalf, W. S. Kolthammer, and I. A. Walsmley, Optimal design for universal multiport interferometers, Optica 3, 1460 (2016).
 M. Jacques, A. Samani, E. El-Fiky, D. Patel, X. Zhenping, and D. V. Plant, Optimization of thermo-optic phase-shifter design and mitigation of thermal crosstalk on the SOI platform, Opt. Express 27, 10456 (2019).
 A. Serafini, Quantum Continuous Variables: A Primer of Theoretical Methods (CRC Press, 2017).
 J. Huh, G. G. Guerreschi, B. Peropadre, J. R. McClean, and A. Aspuru-Guzik, Boson sampling for molecular vibronic spectra, Nature Photonics 9, 615 (2015).
 S. Rahimi-Keshari, M. A. Broome, R. Fickler, A. Fedrizzi, T. C. Ralph, and A. G. White, Direct characterization of linear-optical networks, Opt. Express 21, 13450 (2013).
 V. Giovannetti, A. S. Holevo, and R. García-Patrón, A Solution of Gaussian Optimizer Conjecture for Quantum Channels, Commun. Math. Phys. 334, 1553 (2015).
 R. Kruse, C. S. Hamilton, L. Sansoni, S. Barkhofen, C. Silberhorn, and I. Jex, Detailed study of Gaussian boson sampling, Phys. Rev. A 100, 032326 (2019).
 M. Cerezo, Andrew Arrasmith, Ryan Babbush, Simon C. Benjamin, Suguru Endo, Keisuke Fujii, Jarrod R. McClean, Kosuke Mitarai, Xiao Yuan, Lukasz Cincio, and Patrick J. Coles, "Variational Quantum Algorithms", arXiv:2012.09265.
 Shreya P. Kumar, Leonhard Neuhaus, Lukas G. Helt, Haoyu Qi, Blair Morrison, Dylan H. Mahler, and Ish Dhand, "Mitigating linear optics imperfections via port allocation and compilation", arXiv:2103.03183.
 Saad Yalouz, Bruno Senjean, Filippo Miatto, and Vedran Dunjko, "Encoding strongly-correlated many-boson wavefunctions on a photonic quantum computer: application to the attractive Bose-Hubbard model", arXiv:2103.15021.
 Tyler Volkoff, Zoë Holmes, and Andrew Sornborger, "Universal compiling and (No-)Free-Lunch theorems for continuous variable quantum learning", arXiv:2105.01049.
The above citations are from SAO/NASA ADS (last updated successfully 2021-08-01 06:20:30). The list may be incomplete as not all publishers provide suitable and complete citation data.
On Crossref's cited-by service no data on citing works was found (last attempt 2021-08-01 06:20:28).
This Paper is published in Quantum under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Copyright remains with the original copyright holders such as the authors or their institutions.