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Mitigating photon loss in linear optical quantum circuits

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Researchers James Mills and Rawad Mezher introduced a novel photon loss mitigation technique for linear optical quantum circuits that outperforms postselection, the current standard method. Their approach uses "recycled probabilities" derived from loss-affected output statistics, amplifying ideal (lossless) probability signals through classical postprocessing, though producing biased estimators. Analytical and numerical evidence shows these methods reduce combined bias and statistical errors better than postselection, extending computational limits of near-term photonic quantum devices. Unlike zero-noise extrapolation, which fails to improve on postselection at any loss rate, this technique demonstrates superior performance for realistic loss scenarios in discrete-variable optical circuits. The work provides a scalable solution for photon loss, a major obstacle in photonic quantum computing, by repurposing discarded data rather than eliminating it.

Mitigating photon loss in linear optical quantum circuits

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AbstractPhoton loss rates set an effective upper limit on the size of computations that can be run on current linear optical quantum devices. We present a family of techniques designed to mitigate the effects of photon loss on both output probabilities and expectation values derived from noisy linear optical circuits composed of an input of $n$ photons, an $m$--mode interferometer, and $m$ single photon detectors. Central to these techniques is the construction $\textit{recycled probabilities}$. Recycled probabilities are constructed from output statistics affected by loss, and are designed to amplify the signal of the ideal (lossless) probabilities. Classical postprocessing techniques then take recycled probabilities as input and output a set of loss-mitigated probabilities, or expectation values. Our postprocessing methods result in biased estimators of the lossless probabilities. Nevertheless, we provide both analytical and numerical evidence that these methods can be applied, up to large sample sizes, to produce output probabilities with lower combined bias and statistical errors than the statistical errors of the output probabilities obtained from postselection. Therefore, these methods can outperform postselection - currently the standard method of coping with photon loss when sampling from discrete variable linear optical quantum circuits. In contrast, we provide evidence that the popular zero-noise extrapolation technique cannot improve on the performance of postselection for any photon loss rate.Featured image: Photon loss limits the scalability of photonic quantum computers. Instead of discarding runs with lost photons, our method recycles information from these outcomes and uses classical postprocessing techniques to produce more accurate results than postselection.Popular summaryPhotonic quantum computers process information using photons travelling through networks of beamsplitters and phase shifters. One of the main obstacles to scaling up such devices is photon loss, which causes errors in the output statistics of the computation. The standard approach for dealing with photon loss is postselection, where computation runs with lost photons are discarded. This approach quickly becomes extremely inefficient as the fraction of usable data decreases exponentially with circuit size. In this work we introduce new classical postprocessing techniques which reuse statistics from computation outcomes with lost photons that would normally be thrown away in postselection. We provide analytical and numerical evidence that these methods can produce more accurate computational outputs than postselection for realistic loss rates. Our results demonstrate that sophisticated postprocessing methods can significantly extend the capabilities of near-term photonic quantum devices.► BibTeX data@article{Mills2026mitigatingphoton, doi = {10.22331/q-2026-03-16-2030}, url = {https://doi.org/10.22331/q-2026-03-16-2030}, title = {Mitigating photon loss in linear optical quantum circuits}, author = {Mills, James and Mezher, Rawad}, journal = {{Quantum}}, issn = {2521-327X}, publisher = {{Verein zur F{\"{o}}rderung des Open Access Publizierens in den Quantenwissenschaften}}, volume = {10}, pages = {2030}, month = mar, year = {2026} }► References [1] E. Knill, R. Laflamme, and G. J. Milburn. ``A scheme for efficient quantum computation with linear optics''. Nature 409, 46–52 (2001). https://doi.org/10.1038/35051009 [2] R. Raussendorf, J. Harrington, and K. Goyal. ``Topological fault-tolerance in cluster state quantum computation''. New Journal of Physics 9, 199 (2007). https://doi.org/10.1088/1367-2630/9/6/199 [3] Grégoire de Gliniasty, Paul Hilaire, Pierre-Emmanuel Emeriau, Stephen C. Wein, Alexia Salavrakos, and Shane Mansfield. ``A Spin-Optical Quantum Computing Architecture''. Quantum 8, 1423 (2024). https://doi.org/10.22331/q-2024-07-24-1423 [4] Sara Bartolucci, Patrick Birchall, Hector Bombín, Hugo Cable, Chris Dawson, Mercedes Gimeno-Segovia, Eric Johnston, Konrad Kieling, Naomi Nickerson, Mihir Pant, Fernando Pastawski, Terry Rudolph, and Chris Sparrow. ``Fusion-based quantum computation''. Nature Communications 14, 912 (2023). https://doi.org/10.1038/s41467-023-36493-1 [5] Robert Raussendorf and Hans J. Briegel. ``A One-Way Quantum Computer''.

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Could not fetch ADS cited-by data during last attempt 2026-03-16 08:43:06: No response from ADS or unable to decode the received json data when getting the list of citing works.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. AbstractPhoton loss rates set an effective upper limit on the size of computations that can be run on current linear optical quantum devices. We present a family of techniques designed to mitigate the effects of photon loss on both output probabilities and expectation values derived from noisy linear optical circuits composed of an input of $n$ photons, an $m$--mode interferometer, and $m$ single photon detectors. Central to these techniques is the construction $\textit{recycled probabilities}$. Recycled probabilities are constructed from output statistics affected by loss, and are designed to amplify the signal of the ideal (lossless) probabilities. Classical postprocessing techniques then take recycled probabilities as input and output a set of loss-mitigated probabilities, or expectation values. Our postprocessing methods result in biased estimators of the lossless probabilities. Nevertheless, we provide both analytical and numerical evidence that these methods can be applied, up to large sample sizes, to produce output probabilities with lower combined bias and statistical errors than the statistical errors of the output probabilities obtained from postselection. Therefore, these methods can outperform postselection - currently the standard method of coping with photon loss when sampling from discrete variable linear optical quantum circuits. In contrast, we provide evidence that the popular zero-noise extrapolation technique cannot improve on the performance of postselection for any photon loss rate.Featured image: Photon loss limits the scalability of photonic quantum computers. Instead of discarding runs with lost photons, our method recycles information from these outcomes and uses classical postprocessing techniques to produce more accurate results than postselection.Popular summaryPhotonic quantum computers process information using photons travelling through networks of beamsplitters and phase shifters. One of the main obstacles to scaling up such devices is photon loss, which causes errors in the output statistics of the computation. The standard approach for dealing with photon loss is postselection, where computation runs with lost photons are discarded. This approach quickly becomes extremely inefficient as the fraction of usable data decreases exponentially with circuit size. In this work we introduce new classical postprocessing techniques which reuse statistics from computation outcomes with lost photons that would normally be thrown away in postselection. We provide analytical and numerical evidence that these methods can produce more accurate computational outputs than postselection for realistic loss rates. Our results demonstrate that sophisticated postprocessing methods can significantly extend the capabilities of near-term photonic quantum devices.► BibTeX data@article{Mills2026mitigatingphoton, doi = {10.22331/q-2026-03-16-2030}, url = {https://doi.org/10.22331/q-2026-03-16-2030}, title = {Mitigating photon loss in linear optical quantum circuits}, author = {Mills, James and Mezher, Rawad}, journal = {{Quantum}}, issn = {2521-327X}, publisher = {{Verein zur F{\"{o}}rderung des Open Access Publizierens in den Quantenwissenschaften}}, volume = {10}, pages = {2030}, month = mar, year = {2026} }► References [1] E. Knill, R. Laflamme, and G. J. Milburn. ``A scheme for efficient quantum computation with linear optics''. Nature 409, 46–52 (2001). https://doi.org/10.1038/35051009 [2] R. Raussendorf, J. Harrington, and K. Goyal. ``Topological fault-tolerance in cluster state quantum computation''. New Journal of Physics 9, 199 (2007). https://doi.org/10.1088/1367-2630/9/6/199 [3] Grégoire de Gliniasty, Paul Hilaire, Pierre-Emmanuel Emeriau, Stephen C. Wein, Alexia Salavrakos, and Shane Mansfield. ``A Spin-Optical Quantum Computing Architecture''. Quantum 8, 1423 (2024). https://doi.org/10.22331/q-2024-07-24-1423 [4] Sara Bartolucci, Patrick Birchall, Hector Bombín, Hugo Cable, Chris Dawson, Mercedes Gimeno-Segovia, Eric Johnston, Konrad Kieling, Naomi Nickerson, Mihir Pant, Fernando Pastawski, Terry Rudolph, and Chris Sparrow. ``Fusion-based quantum computation''. Nature Communications 14, 912 (2023). https://doi.org/10.1038/s41467-023-36493-1 [5] Robert Raussendorf and Hans J. Briegel. ``A One-Way Quantum Computer''.

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