7 citations · 14 across the 5 of their papers we have counts for
6 papers · 1 filter
Tree Tensor Network Reservoir Computing: Hierarchical Ensemble with Invariant Phase Boundaries
Daiki Sasaki, Chih-Chieh Chen, Tomah Sogabe
We propose Tree Tensor Network Reservoir Computing (TTN-RC), a quantum-inspired reservoir computing framework for time-series prediction that uses the hierarchical structure of Tre…
Quantum-Circuit Framework for Two-Stage Stochastic Programming via QAOA Integrated with a Quantum Generative Neural Network
Taihei Kuroiwa, Daiki Yamazaki, Keita Takahashi +3
Two-stage stochastic programming often discretizes uncertainty into scenarios, but scenario enumeration makes expected recourse evaluation scale at least linearly in the scenario c…
Hamiltonian-Driven Architectures for Non-Markovian Quantum Reservoir Computing
Daiki Sasaki, Ryosuke Koga, Taihei Kuroiwa +3
We propose a Hamiltonian-level framework for non-Markovian quantum reservoir computing directly tailored for analog hardware implementations. By dividing the reservoir into a syste…
Parametrized Energy-Efficient Quantum Kernels for Network Service Fault Diagnosis
Hiroshi Yamauchi, Tomah Sogabe, Rodney Van Meter
In quantum kernel learning, the primary method involves using a quantum computer to calculate the inner product between feature vectors, thereby obtaining a Gram matrix used as a k…
Quantum Circuit Parameters Learning with Gradient Descent Using Backpropagation
Masaya Watabe, Kodai Shiba, Masaru Sogabe +2
Quantum computing has the potential to outperform classical computers and is expected to play an active role in various fields. In quantum machine learning, a quantum computer has…
Convolution filter embedded quantum gate autoencoder
Kodai Shiba, Katsuyoshi Sakamoto, Koichi Yamaguchi +2
The autoencoder is one of machine learning algorithms used for feature extraction by dimension reduction of input data, denoising of images, and prior learning of neural networks.…