Quantum-enhanced causal discovery for a small number of samples

The discovery of causal relationships from observed data has attracted significant interest from disciplines such as economics, social sciences, epidemiology, and biology. In practical applications, considerable knowledge of the underlying systems is often unavailable, and real data are often associated with nonlinear causal structures, which make the direct use of most conventional causality analysis methods difficult. This study proposes a novel quantum Peter-Clark (qPC) algorithm for causal discovery that does not assume any underlying model structures. Based on the independence conditional tests in a class of reproducing kernel Hilbert spaces characterized by quantum circuits, the proposed qPC algorithm can explore causal relationships from the observed data drawn from arbitrary distributions. We conducted systematic experiments on fundamental graph parts of causal structures, demonstrating that the qPC algorithm exhibits a significantly better performance, particularly with smaller sample sizes compared to its classical counterpart. Furthermore, we proposed a novel optimization approach based on Kernel Target Alignment (KTA) for determining hyperparameters of quantum kernels. This method effectively reduced the risk of false positives in causal discovery, enabling more reliable inference. Our theoretical and experimental results demonstrate that the proposed quantum algorithm can empower classical algorithms for robust and accurate inference in causal discovery, supporting them in regimes where classical algorithms typically fail. Additionally, the effectiveness of this method was validated using the Boston Housing dataset as a real-world application. These findings demonstrate the new potential of quantum circuit-based causal discovery methods in addressing practical challenges, particularly in small-sample scenarios where traditional approaches have shown limitations.

Anti-biofouling Lensless Camera System with Deep Learning based Image Reconstruction

In recent years, there has been an increasing demand for underwater cameras that monitor the condition of offshore structures and check the number of individuals in aqua culture environments with long-period observation. One of the significant issues with this observation is that biofouling sticks to the aperture and lens densely and prevents cameras from capturing clear images. This study examines an underwater camera that applies material technologies with high inherent resistance to biofouling and computer vision technologies based on image reconstruction by deep learning to lens-less cameras. For this purpose, our prototype camera uses a coded aperture with 1k rectangular shape pinholes in a thin metal plate, such as copper, which hinder the growth of biofouling and keep the surface clean. Although images taken by lens-less cameras are usually not well formed due to lack of the traditional glass-based lens, a deep learning approach using ViT (Vision Transformer) has recently demonstrated reconstructing original photo images well and our study shows that using gated MLP (Multilayer Perceptron) also yields good results. On the other hand, a certain degree of thickness for bio-repellence materials is required to exhibit their effect the thickness of aperture is necessary to use apertures sufficiently thinner than the size of the pinholes to avoid unintentional reflection and absorption on the sidewalls. Therefore, we prepared a sufficiently thin plate for image reconstruction and now currently we conduct tests of the lens-less camera of the bio-repellence aperture with actual seawater environments to determine whether it can sufficiently demonstrate the biofouling effect compared with usual camera with only waterproof.