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Perturbation theory with quantum signal processing
Kosuke Mitarai, Kiichiro Toyoizumi, Wataru Mizukami
Perturbation theory is an important technique for reducing computational cost and providing physical insights in simulating quantum systems with classical computers. Here, we provi…
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…
Pricing multi-asset derivatives by variational quantum algorithms
Kenji Kubo, Koichi Miyamoto, Kosuke Mitarai +1
Pricing a multi-asset derivative is an important problem in financial engineering, both theoretically and practically. Although it is suitable to numerically solve partial differen…
Simulation and performance analysis of quantum error correction with a rotated surface code under a realistic noise model
Mitsuki Katsuda, Kosuke Mitarai, Keisuke Fujii
The demonstration of quantum error correction (QEC) is one of the most important milestones in the realization of fully-fledged quantum computers. Toward this, QEC experiments usin…
Local variational quantum compilation of a large-scale Hamiltonian dynamics
Kaoru Mizuta, Yuya O. Nakagawa, Kosuke Mitarai +1
Implementing time evolution operators on quantum circuits is important for quantum simulation. However, the standard way, Trotterization, requires a huge numbers of gates to achiev…
Identification of topological phases using classically-optimized variational quantum eigensolver
Ken N. Okada, Keita Osaki, Kosuke Mitarai +1
Variational quantum eigensolver (VQE) is regarded as a promising candidate of hybrid quantum-classical algorithm for the near-term quantum computers. Meanwhile, VQE is confronted w…
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…
Constructing Local Bases for a Deep Variational Quantum Eigensolver for Molecular Systems
Luca Erhart, Kosuke Mitarai, Wataru Mizukami +1
Current quantum computers are limited in the number of qubits and coherence time, constraining the algorithms executable with sufficient fidelity. The variational quantum eigensolv…