It is a fundamental challenge in quantum optics to deterministically generate indistinguishable single photons through non-deterministic nonlinear optical processes, due to the intrinsic coupling of single- and multi-photon-generation probabilities in these processes. Actively multiplexing photons generated in many temporal modes can decouple these probabilities, but key issues are to minimize resource requirements to allow scalability, and to ensure indistinguishability of the generated photons. Here we demonstrate the multiplexing of photons from four temporal modes solely using fibre-integrated optics and off-the-shelf electronic components. We show a 100% enhancement to the single-photon output probability without introducing additional multi-photon noise. Photon indistinguishability is confirmed by a fourfold Hong–Ou–Mandel quantum interference with a 91±16% visibility after subtracting multi-photon noise due to high pump power. Our demonstration paves the way for scalable multiplexing of many non-deterministic photon sources to a single near-deterministic source, which will be of benefit to future quantum photonic technologies.

Synaptic Long-Term Potentiation (LTP), which is a long-lasting enhancement in signal transmission between neurons, is widely considered as the major cellular mechanism during learning and memorization. In this work, a NiOx-based memristor is found to be able to emulate the synaptic LTP. Electrical conductance of the memristor is increased by electrical pulse stimulation and then spontaneously decays towards its initial state, which resembles the synaptic LTP. The lasting time of the LTP in the memristor can be estimated with the relaxation equation, which well describes the conductance decay behavior. The LTP effect of the memristor has a dependence on the stimulation parameters, including pulse height, width, interval, and number of pulses. An artificial network consisting of three neurons and two synapses is constructed to demonstrate the associative learning and LTP behavior in extinction of association in Pavlov's dog experiment.

Although there is a huge progress in complementary-metal-oxide-semiconductor (CMOS) technology, construction of an artificial neural network using CMOS technology to realize the functionality comparable with that of human cerebral cortex containing 1010–1011 neurons is still of great challenge. Recently, phase change memristor neuron has been proposed to realize a human-brain level neural network operating at a high speed while consuming a small amount of power and having a high integration density. Although memristor neuron can be scaled down to nanometer, integration of 1010–1011 neurons still faces many problems in circuit complexity, chip area, power consumption, etc. In this work, we propose a CMOS compatible HfO2 memristor neuron that can be well integrated with silicon circuits. A hybrid Convolutional Neural Network (CNN) based on the HfO2 memristor neuron is proposed and constructed. In the hybrid CNN, one memristive neuron can behave as multiple physical neurons based on the Time Division Multiplexing Access (TDMA) technique. Handwritten digit recognition is demonstrated in the hybrid CNN with a memristive neuron acting as 784 physical neurons. This work paves the way towards substantially shrinking the amount of neurons required in hardware and realization of more complex or even human cerebral cortex level memristive neural networks.

The cold roll bonding process of a laminar metal composite consists of three steps, i.e., surface treatment, cold roll bonding, and heat treatment. The surface pretreatment is the precondition of obtaining a metal composite with a superior bonding level. The influence of the steel plate surface condition on the bonding strength is studied, based on the bonding mechanism of a laminar metal composite by cold roll bonding. Flap disc grinding is a better surface treatment than wire brushing for obtaining high bonding strength. Within a certain range, the larger the surface roughness, the higher the bonding strength. Comparing different grinding textures, viz longitudinal, transverse, and 45°with respect to the rolling direction, it was revealed that the longitudinal surface texture was more advantageous for bonding.