Spatiotemporal optical specification of quantum-dot network for
optical reservoir computing
Naoya Tate1, Yuki Miyata1, Suguru Shimomura 2, Takahiro Nishimura3, Yusuke Ogura2, and Jun Tanida2
1

Faculty of Information Science and Electrical Engineering, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan
2
Graduate school of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka 565-0871, Japan
3
Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan
E-mail:tate@ed.kyushu-u.ac.jp

Reservoir computing[1] is a type of recurrent neural network that can execute a computational task by utilizing fixed
random weights of a network structure. Generally, such architecture is suitable for various applications that handle
temporal data, such as speech recognition and dynamic prediction of stock prices. Recently, various concepts have been
demonstrated experimentally by utilizing not only advanced electronics but also other physical technologies[2,3]. In thisstudy, we propose the implementation of an optical reservoir based on a quantum dot (QD) network. The electronic
state of each QD is excited by irradiation of light, and this energy is allowed to transfer to the surrounding QDs in thenetwork. As a result, the energy transfer and corresponding emissions from the network are expected to exhibit
spatiotemporal optical variations of nonlinear input-output relations, which is strongly required in the development of aphysical reservoir. Furthermore, using nanometer-sized QDs, a miniaturized and lower energy-consumption reservoir isexpected to be realized. Here, some experimental results are shown to demonstrate the energy transfer andcorresponding spatiotemporal emissions of the QD network.
Figures 1(a) and (b) show the appearance and schematic of the sample, respectively. As shown, three types of QDs(Emission peak wavelengths λem = 540, 580, and 620 nm) were randomly dispersed in each polymer solution as
individual QD networks, and each layer was stacked in an order such that it induces energy transfer between thedifferent types of QDs. The sample was then irradiated by a pulsed laser (λex = 515 nm), and the emission results
were obtained using a spectrometer and a single-photon detector. The size of each observation area wasconsiderably lower than the 10-μm square. As shown in Fig. 1(c), the emission spectra obtained at different areas
show different spectra referred to as the spatial variation of the QD network. Furthermore, as shown in Fig. 1(d),emission at λ = 538 nm from each area shows different decay rates, which corresponds to delay of energy transfer
in each QD network, referred to as the temporal variation of the QD network.

Figure 1: (a) Appearance and (b) schematic of the sample; (c) Spatio- and (d) temporal-optical variation ofthe sample.

In this study, we proposed a QD network as a key component of an optical reservoir. The experimental results
using the QD network exhibit spatiotemporal variations of input–output relations, which is due to nanoscaleenergy transfer. In a future study, we would discuss a method for the enhancement of spatiotemporal variation of
the QD network, and verify its superiority in advanced reservoir computing.
This work was supported by JST CREST Grant Number JPMJCR18K2, Japan.
References[1] H. Jaeger, et al., “Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication,” Science 304, 78–80 (2004).
[2] Y. Paquot, et al., “Optoelectronic reservoir computing,” Sci. Rep. 2, 287 (2012).
[3] C. Du, et al., “Reservoir computing using dynamic memristors for temporal information processing,” Nat. Commun. 8, 2204 (2017).