6 citations · 7 across the 4 of their papers we have counts for
17 papers · 1 filter
Efficient state preparation for multivariate Monte Carlo simulation
Hitomi Mori, Kosuke Mitarai, Keisuke Fujii
Quantum state preparation is a task to prepare a state with a specific function encoded in the amplitude, which is an essential subroutine in many quantum algorithms. In this paper…
Quantum-inspired algorithm applied to extreme learning
Iori Takeda, Souichi Takahira, Kosuke Mitarai +1
Quantum-inspired singular value decomposition (SVD) is a technique to perform SVD in logarithmic time with respect to the dimension of a matrix, given access to the matrix embedded…
Parametric t-Stochastic Neighbor Embedding With Quantum Neural Network
Yoshiaki Kawase, Kosuke Mitarai, Keisuke Fujii
t-Stochastic Neighbor Embedding (t-SNE) is a non-parametric data visualization method in classical machine learning. It maps the data from the high-dimensional space into a low-dim…
Learning temporal data with variational quantum recurrent neural network
Yuto Takaki, Kosuke Mitarai, Makoto Negoro +2
We propose a method for learning temporal data using a parametrized quantum circuit. We use the circuit that has a similar structure as the recurrent neural network which is one of…
Quadratic Clifford expansion for efficient benchmarking and initialization of variational quantum algorithms
Kosuke Mitarai, Yasunari Suzuki, Wataru Mizukami +2
Variational quantum algorithms are considered to be appealing applications of near-term quantum computers. However, it has been unclear whether they can outperform classical algori…
Variational Quantum Algorithms
M. Cerezo, Andrew Arrasmith, Ryan Babbush +8
Applications such as simulating complicated quantum systems or solving large-scale linear algebra problems are very challenging for classical computers due to the extremely high co…