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Boosting photon-number-resolved detection rates of transition-edge sensors by machine learning

Optica Quantum Optica Publishing Group 3:3 (2025) 246-246

Authors:

Zhenghao Li, Matthew JH Kendall, Gerard J Machado, Ruidi Zhu, Ewan Mer, Hao Zhan, Aonan Zhang, Shang Yu, Ian A Walmsley, Raj B Patel

Abstract:

Transition-edge sensors (TESs) are very effective photon-number-resolving (PNR) detectors that have enabled many photonic quantum technologies. However, their relatively slow thermal recovery time severely limits their operation rate in experimental scenarios compared with leading non-PNR detectors. In this work, we develop an algorithmic approach that enables TESs to detect and accurately classify photon pulses without waiting for a full recovery time between detection events. We propose two machine-learning-based signal processing methods: one supervised learning method and one unsupervised clustering method. By benchmarking against data obtained using coherent states and squeezed states, we show that the methods extend the TES operation rate to 800 kHz, achieving at least a four-fold improvement, whilst maintaining accurate photon-number assignment up to at least five photons. Our algorithms will find utility in applications where high rates of PNR detection are required and in technologies that demand fast active feed-forward of PNR detection outcomes.
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Super-resolving frequency measurement with mode-selective quantum memory

(2025)

Authors:

Shicheng Zhang, Aonan Zhang, Ilse Maillette de Buy Wenniger, Paul M Burdekin, Steven Sagona-Stophel, Anindya Rastogi, Sarah E Thomas, Ian A Walmsley
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Unveiling Hierarchical Invariants in Multiphoton Linear Optics

ArXiv 2506.12857 (2025)

Authors:

Baichuan Yang, Hao Zhan, Minghao Mi, Aonan Zhang, Liang Xu, Lijian Zhang
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Tsang’s resolution enhancement method for imaging with focused illumination

Light: Science & Applications Springer Nature 14:1 (2025) 159

Authors:

Aleksandr Duplinskii, Jernej Frank, Kaden Bearne, Alex Lvovsky

Abstract:

A widely tested approach to overcoming the diffraction limit in microscopy without disturbing the sample relies on substituting widefield sample illumination with a structured light beam. This gives rise to confocal, image scanning, and structured illumination microscopy methods. On the other hand, as shown recently by Tsang and others, subdiffractional resolution at the detection end of the microscope can be achieved by replacing the intensity measurement in the image plane with spatial mode demultiplexing. In this work, we study the combined action of Tsang’s method with image scanning. We experimentally demonstrate superior lateral resolution and enhanced image quality compared to either method alone. This result paves the way for integrating spatial demultiplexing into existing microscopes, contributing to further pushing the boundaries of optical resolution.

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Continuous-time quantum optimization without the adiabatic principle

Physical Review A American Physical Society (APS) 111:3 (2025) 032414

Authors:

Robert J Banks, Georgios S Raftis, Dan E Browne, PA Warburton

Abstract:

Continuous-time quantum algorithms for combinatorial optimization problems, such as quantum annealing, have previously been motivated by the adiabatic principle. A number of continuous-time approaches exploit dynamics, however, and therefore are no longer physically motivated by the adiabatic principle. In this work, we take Planck's principle as the underlying physical motivation for continuous-time quantum algorithms. Planck's principle states that the energy of an isolated system cannot decrease as the result of a cyclic process. We use this principle to justify monotonic schedules in quantum annealing, which are not adiabatic. This approach also highlights the limitations of reverse quantum annealing in an isolated system.
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