Decision making is critical in our daily lives and for society in general and is finding evermore practical applications in information and communication technologies. Herein, we demonstrate experimentally that single photons can be used to make decisions in uncertain, dynamically changing environments. Using a nitrogen-vacancy in a nanodiamond as a single-photon source, we demonstrate the decision-making capability by solving the multi-armed bandit problem. This capability is directly and immediately associated with single-photon detection in the proposed architecture, leading to adequate and adaptive autonomous decision making. This study makes it possible to create systems that benefit from the quantum nature of light to perform practical and vital intelligent functions.

Reinforcement learning involves decision-making in dynamic and uncertain environments and constitutes a crucial element of artificial intelligence. In our previous work, we experimentally demonstrated that the ultrafast chaotic oscillatory dynamics of lasers can be used to efficiently solve the two-armed bandit problem, which requires decision-making concerning a class of difficult trade-offs called the exploration–exploitation dilemma. However, only two selections were employed in that research; hence, the scalability of the laser-chaos-based reinforcement learning should be clarified. In this study, we demonstrated a scalable, pipelined principle of resolving the multi-armed bandit problem by introducing time-division multiplexing of chaotically oscillated ultrafast time series. The experimental demonstrations in which bandit problems with up to 64 arms were successfully solved are presented where laser chaos time series significantly outperforms quasiperiodic signals, computer-generated pseudorandom numbers, and coloured noise. Detailed analyses are also provided that include performance comparisons among laser chaos signals generated in different physical conditions, which coincide with the diffusivity inherent in the time series. This study paves the way for ultrafast reinforcement learning by taking advantage of the ultrahigh bandwidths of light wave and practical enabling technologies.

Probe electrospray ionization (PESI) has recently been developed, in which the electrospray was generated from a solid needle instead of by using a capillary. In this paper, the characteristics of probe electrospray ionization were studied based on the measurement of spray current, optical microscopy, and PESI mass spectrometry. In the experiment, the solid needle was moved up and down a vertical axis, and a small amount of sample was repeatedly loaded to the needle when the tip of the needle touched the surface of the liquid sample at the lowest position. After the application of high voltage, a liquid droplet was formed on the tip of the solid needle probe, with its size was determined by the size of the needle tip. The liquid flow rate to the tip, as indicated by the spray current, depends on the voltage applied to the needle as well as the loaded liquid amount. Stable electrospray can be maintained until the total consumption of liquid sample. The kilohertz current pulsation takes place in the case of overloading the sample to the needle. The influences of the applied voltage and the liquid flow rate on the PESI mass spectra were also examined.