4 papers
Fast and memory-efficient classical simulation of quantum machine learning via forward and backward gate fusion
Yoshiaki Kawase
While real quantum devices have been increasingly used to conduct research focused on achieving quantum advantage or quantum utility in recent years, executing deep quantum circuit…
The effect of the number of parameters and the number of local feature patches on loss landscapes in distributed quantum neural networks
Yoshiaki Kawase
Quantum neural networks hold promise for tackling computationally challenging tasks that are intractable for classical computers. However, their practical application is hindered b…
Distributed Quantum Neural Networks via Partitioned Features Encoding
Yoshiaki Kawase
Quantum neural networks are expected to be a promising application in near-term quantum computing, but face challenges such as vanishing gradients during optimization and limited e…
Quantum Kernel t-Distributed Stochastic Neighbor Embedding
Yoshiaki Kawase, Kosuke Mitarai, Keisuke Fujii
Data visualization is important in understanding the characteristics of data that are difficult to see directly. It is used to visualize loss landscapes and optimization trajectori…