We assess the overall performance of our quantum key distribution (QKD) system implementing the measurement-device-independent (MDI) protocol using components with varying capabilities such as different single-photon detectors and qubit preparation hardware. We experimentally show that superconducting nanowire single-photon detectors allow QKD over a channel featuring 60 dB loss, and QKD with more than 600 bits of secret key per second (not considering finite key effects) over a 16 dB loss channel. This corresponds to 300 and 80 km of standard telecommunication fiber, respectively. We also demonstrate that the integration of our QKD system into FPGAbased hardware (instead of state-of-the-art arbitrary waveform generators) does not impact on its performance. Our investigation allows us to acquire an improved understanding of the trade-offs between complexity, cost and system performance, which is required for future customization of MDI-QKD. Given that our system can be operated outside the laboratory over deployed fiber, we conclude that MDI-QKD is a promising approach to information-theoretic secure key distribution.

Random-number generation is an enabling technology for fields as varied as Monte Carlo simulations and quantum information science, particularly secure quantum key distribution. Here, we propose and demonstrate an approach to random-number generation that satisfies the specific requirements for quantum key distribution. In our scheme, vacuum fluctuations of the electromagnetic field inside a laser cavity are sampled in a discrete manner in time and amplified by injecting current pulses into the laser. Random numbers can be obtained by interfering the laser pulses with another independent laser operating at the same frequency. Using only off-the-shelf optoelectronics and fiber-optic components at a 1.5-μm wavelength, we experimentally demonstrate the generation of high-quality random bits at a rate of up to 1.5 GHz. Our results show the potential of the new scheme for practical information-processing applications. KEYWORDSoptical interferometry, phase noise, quantum cryptography, random-number generation Quantum Engineering. 2019;1:e8.wileyonlinelibrary.com/journal/que2

Abstract-We experimentally realize a measurement-deviceindependent quantum key distribution (MDI-QKD) system based on cost-effective and commercially available hardware such as distributed feedback (DFB) lasers and field-programmable gate arrays (FPGA) that enable time-bin qubit preparation and timetagging, and active feedback systems that allow for compensation of time-varying properties of photons after transmission through deployed fibre. We examine the performance of our system, and conclude that its design does not compromise performance. Our demonstration paves the way for MDI-QKD-based quantum networks in star-type topology that extend over more than 100 km distance.

The possibility for quantum and classical communication to coexist on the same fibre is important for deployment and widespread adoption of quantum key distribution (QKD) and, more generally, a future quantum internet. While coexistence has been demonstrated for different QKD implementations, a comprehensive investigation for measurement-device independent (MDI) QKDa recently proposed QKD protocol that cannot be broken by quantum hacking that targets vulnerabilities of single-photon detectors -is still missing. Here we experimentally demonstrate that MDI-QKD can operate simultaneously with at least five 10 Gbps bidirectional classical communication channels operating at around 1550 nm wavelength and over 40 km of spooled fibre, and we project communication rates in excess of 10 THz when moving the quantum channel from the third to the second telecommunication window. The similarity of MDI-QKD with quantum repeaters suggests that classical and generalised quantum networks can co-exist on the same fibre infrastructure. Key rate (R ) Data rate (in Gbps) Key rate for 2x20km Co-propagating (1310nm) Key rate for 2x20km Bidirectional (1310nm) Key rate for 2x40km Co-propagating (1310nm) Key rate for 2x40km Bidirectional (1310nm) Key rate for 2x20km Co-propagating (1532nm) Key rate for 2x20km Bidirectional (1532nm) Key rate for 2x40km Co-propagating (1532nm) Key rate for 2x40km Bidirectional (1532nm) Key rate experimental 2x20 km Bidirectional Key rate experimental 2x40 km Bidirectional Key rate (R ) Data rate (in Gbps) Key rate for 2x20km Co-propagating (1310nm) Key rate for 2x20km Bidirectional (1310nm) Key rate for 2x40km Co-propagating (1310nm) Key rate for 2x40km Bidirectional (1310nm) Key rate for 2x20km Co-propagating (1532nm) Key rate for 2x20km Bidirectional (1532nm) Key rate for 2x40km Co-propagating (1532nm) Key rate for 2x40km Bidirectional (1532nm) Key rate experimental 2x20 km Bidirectional Key rate experimental 2x40 km Bidirectional Key rate (R ) Data rate (in Gbps) Key rate for 2x20km Co-propagating (1310nm) Key rate for 2x20km Bidirectional (1310nm) Key rate for 2x40km Co-propagating (1310nm) Key rate for 2x40km Bidirectional (1310nm) Key rate for 2x20km Co-propagating (1532nm) Key rate for 2x20km Bidirectional (1532nm) Key rate for 2x40km Co-propagating (1532nm) Key rate for 2x40km Bidirectional (1532nm) Key rate experimental 2x20 km Bidirectional Key rate experimental 2x40 km Bidirectional Key rate for 2x20km Co-propagating (1310nm) Key rate for 2x20km Bidirectional (1310nm) Key rate for 2x40km Co-propagating (1310nm) Key rate for 2x40km Bidirectional (1310nm) Key rate for 2x20km Co-propagating (1532nm) Key rate for 2x20km Bidirectional (1532nm) Key rate for 2x40km Co-propagating (1532nm) Key rate for 2x40km Bidirectional (1532nm) Key rate experimental 2x20 km Bidirectional Key rate experimental 2x40 km Bidirectional Key rate (R ) Data rate (in Gbps) Key rate for 2x20km Co-propagating (1310nm) Key rate for 2x20km Bidirectional (1310nm) Key rate for 2x40km Co-propagating (1310nm) Key rate for 2x40km Bidi...

This paper explores how to best identify clients for housing services based on their homeless shelter access patterns. We utilize the number of shelter stays and episodes of shelter use for a client within a specified time window. Thresholds are then applied to these values to determine if that individual is a good candidate for housing support. Using new housing referral impact metrics, we explore a range of threshold and time window values to determine which combination both maximizes impact and identifies good candidates for housing as soon as possible. New insights are also provided regarding the characteristics of the “under-the-radar” client group who are typically not identified for housing support.

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