Yudai Suzuki 교수

Reservoir computing is a temporal information processing system that exploits artificial or physical dissipative dynamics to learn a dynamical system and generate the target time-series. This paper proposes the use of real superconducting quantum computing devices as the reservoir, where the dissipative property is served by the natural noise added to the quantum bits. The performance of this natural quantum reservoir is demonstrated in a benchmark time-series regression problem and a practical

We describe the overall role of soft computing (SC) in signal processing and pattern recognition (SPPR) with specific applications to biomedical engineering, geoscience for mining and civil engineering human interfaces, and image processing. Detection of characteristic points in an electrocardiogram to implement an advanced ECG analyzer is presented which is carried out using both conventional SPPR techniques and self-organizing neural networks. Successful technologies for monitoring a geostruct

Investigations making use of a regular processor architecture for multilayer neural networks (NNs) are described. By comparing bus-coupling, ring, and mesh topologies, the authors theoretically analyzed the required data transmission count and calculation count for one iteration of training for a NN with one hidden layer. For a minimum data transmission count, an optimal number of processor elements (PEs) exists in the case of mesh, whereas no global optimum occurs for the bus-coupling and ring

Variational quantum algorithms (VQAs) are promising methods that leverage noisy quantum computers and classical computing techniques for practical applications. In VQAs, the classical optimizers such as gradient-based optimizers are utilized to adjust the parameters of the quantum circuit so that the objective function is minimized. However, they often suffer from the so-called vanishing gradient or barren plateau issue. On the other hand, the normalized gradient descent (NGD) method, which empl

Abstract Quantum kernel (QK) methods exploit quantum computers to calculate QKs for the use of kernel-based learning models. Despite a potential quantum advantage of the method, the commonly used fidelity-based QK suffers from a detrimental issue, which we call the vanishing similarity issue; the exponential decay of the expectation value and the variance of the QK deteriorates implementation feasibility and trainability of the model with the increase of the number of qubits. This implies the ne

The study of human face recognition is becoming more and more popular. For these tasks, there is a need to cut off the facial region from the image. Here we propose a method which can automatically cut off the full human facial region from an image with natural background by the use of genetic algorithms and SNAKES. The use of genetic algorithms enables to search globally for the approximate size and position of the face and SNAKES enables to search robustly from this approximate contour to a mo

Quantum kernel methods have been actively examined from both theoretical and practical perspectives due to the potential of quantum advantage in machine learning tasks. Despite a provable advantage of fine-tuned quantum kernels for specific problems, widespread practical usage of quantum kernel methods requires resolving the so-called vanishing similarity issue, where exponentially vanishing variance of the quantum kernels causes implementation infeasibility and trainability problems. In this wo

The GIM department can function as a diagnostic consultant for inpatients with diagnostic problems admitted to other specialty departments in hospitals where hospitalist or other similar systems are not adopted.

Quantum kernel method is a machine learning model exploiting quantum computers to calculate the quantum kernels (QKs) that measure the similarity between data. Despite the potential quantum advantage of the method, the commonly used fidelity-based QK suffers from a detrimental issue, which we call the vanishing similarity issue; detecting the difference between data becomes hard with the increase of the number of qubits, due to the exponential decrease of the expectation and the variance of the

Abstract Feature selection plays an essential role in improving the predictive performance and interpretability of trained models in classical machine learning. On the other hand, the usability of conventional feature selection can be limited for quantum machine learning (QML) tasks; the technique may not provide a clear interpretation on embedding quantum circuits for classical data tasks and, more importantly, is not applicable to quantum data tasks. In this work, a feature selection method is

We implemented a pruning algorithm for a self-organizing tree (S-TREE) using cluster validity. The S-TREE algorithm need to set the limit of the number of nodes U to prune extra nodes beforehand. However, the implemented algorithm is not necessary to set U for pruning. Setting U in advance is difficult because the value of U depends on the problem. Moreover, U might be an obstacle to self-organization and prevent the formation of natural clusters. The usefulness of the algorithm was examined by

Variational quantum algorithms (VQAs) are promising methods that leverage noisy quantum computers and classical computing techniques for practical applications. In VQAs, the classical optimizers such as gradient-based optimizers are utilized to adjust the parameters of the quantum circuit so that the objective function is minimized. However, they often suffer from the so-called vanishing gradient or barren plateau issue. On the other hand, the normalized gradient descent (NGD) method, which empl

With recent advancements in autonomous driving, there has been growing interest in using CS (Chirp Sequence) radar for precise sensing. Unlike other in-vehicle sensing techniques, CS radar offers cost-effectiveness, compactness, and greater resilience to environmental influences. However, the widespread adoption of CS radar may lead to radar-to-radar interference, causing issues like target non-detection and false detection. To tackle this challenge, a method utilizing RNN (Recurrent Neural Netw