At present, there is great demand for novel computing devices and architectures that can overcome the limitations of conventional technologies based solely on electron transfer, including the need to reduce energy consumption and solve computationally demanding problems. In particular, conventional digital computers have difficulty in dealing with intractable problems in which the number of possible solutions increases exponentially as a function of the problem size (referred to as ‘combinatorial explosion’). It is also challenging for conventional computers to make efficient and adaptive decisions in the uncertain, dynamically changing environments seen in real-world applications. A promising solution is near-field nanophotonics, which has been extensively studied with the aim of unveiling and exploiting light-matter interactions that occur at a scale below the wavelength of light. Recent progress made in experimental technologies—both in nanomaterial fabrication, such as quantum dots (QDs), and in characterization—is driving further advancements in the field. We have shown that the dynamics of optical energy transfer mediated by near-field interactions can be exploited to solve solution-searching and decision-making problems.1–4 This suggests that computing systems based on near-field nanophotonics may one day be able to exhibit intellectual abilities. When QDs share common resonant energy levels mediated by optical near-field interactions, optical energy is transferred from smaller QDs to larger ones. This process has been experimentally demonstrated in various quantum nanostructures, such as those fabricated in indium gallium arsenide, zinc oxide, and cadmium selenide. In addition, we have shown this transfer of optical Figure 1. (a) Quantum-dot-based decision maker (QDM) consisting of five quantum dots (denoted QDLL, QDML, QDS, QDMR, and QDLR) interacting via optical near-fields. The subscripts 1, 2, and 3 denote the (1,1,1), (2,1,1), and (2,2,2) energy levels, respectively, while U represents the various optical near-field interactions. (b) Quick adaptation of the QDM to a dynamically changing environment, which in this case is the change of reward probabilities between two slot machines (PA and PB). Softmax: Best conventional algorithm.