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Generalization of the output of variational quantum eigensolver by parameter interpolation with low-depth ansatz
Kosuke Mitarai, Tennin Yan, Keisuke Fujii
The variational quantum eigensolver (VQE) is an attracting possible application of near-term quantum computers. Originally, the aim of the VQE is to find a ground state for a given…
Subspace-search variational quantum eigensolver for excited states
Ken M Nakanishi, Kosuke Mitarai, Keisuke Fujii
The variational quantum eigensolver (VQE), a variational algorithm to obtain an approximated ground state of a given Hamiltonian, is an appealing application of near-term quantum c…
Machine learning with controllable quantum dynamics of a nuclear spin ensemble in a solid
Makoto Negoro, Kosuke Mitarai, Keisuke Fujii +2
We experimentally demonstrate quantum machine learning using NMR based on a framework of quantum reservoir computing. Reservoir computing is for exploiting natural nonlinear dynami…
Quantum Analog-Digital Conversion
Kosuke Mitarai, Masahiro Kitagawa, Keisuke Fujii
Many quantum algorithms, such as Harrow-Hassidim-Lloyd (HHL) algorithm, depend on oracles that efficiently encode classical data into a quantum state. The encoding of the data can…
Boosting computational power through spatial multiplexing in quantum reservoir computing
Kohei Nakajima, Keisuke Fujii, Makoto Negoro +2
Quantum reservoir computing provides a framework for exploiting the natural dynamics of quantum systems as a computational resource. It can implement real-time signal processing an…
Quantum Circuit Learning
Kosuke Mitarai, Makoto Negoro, Masahiro Kitagawa +1
We propose a classical-quantum hybrid algorithm for machine learning on near-term quantum processors, which we call quantum circuit learning. A quantum circuit driven by our framew…