CQP Researchers Publish Paper on Active Temporal Multiplexing of Photons in arXiv

A paper co-authored by researchers including Gabriel Mendoza, Raffaele Santagati, Jack Munns, Lizzy Hemsley, Mateusz Piekarek, Enrique Martin-Lopez, Graham Marshall, Damien Bonneau, Mark Thompson and Jeremy O'Brien from the Centre for Quantum Photonics has been published in arXiv this month.

Figure 1: Illustration of the principle of temporal multiplexing. A non-deterministic generation process is repeated in time with period T; on heralded success, an active optical switching network and delay lines offset photons into output time bins spaced by an integer multiple of the input period and in sync with the system clock cycle. With a suffi- ciently low-loss switching network, the generation probability per clock cycle is increased.

Quantum information science promises powerful new technologies and fundamental scientific discoveries. Photonic qubits are appealing for their low noise properties-the cost is the non-deterministic nature of many processes, including photon generation and entanglement. Active multiplexing can increase the success probability of such processes above a required threshold, and spatial multiplexing of up to four heralded photon sources shows great promise. The cost is a proliferation of hardware. Temporal multiplexing-repeated use of the same hardware components-has been proposed as an alternative and is likely to be essential to greatly reduce resource complexity and system sizes. Requirements include the precise synchronization of a system of low-loss switches, delay lines, fast photon detectors, and feed-forward. Here we demonstrate multiplexing of 8 'bins'-four temporal and two spatial-from a heralded photon source. We show enhanced photon emission statistics, observing an increase in both the triggering and heralded photon rates. Despite its current limitations due to extrinsic sources of loss, this system points the way to harnessing temporal multiplexing in quantum technologies, from single-photon sources to large-scale computation.

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