Quantum circuit learning
Kosuke Mitarai, Makoto Negoro, Masahiro Kitagawa, Keisuke Fujii
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 framework learns a given task by tuning parameters implemented on it. The iterative optimization of the parameters allows us to circumvent the high-depth circuit. Theoretical investigation shows that a quantum circuit can approximate nonlinear functions, which is further confirmed by numerical simulations. Hybridizing a low-depth quantum circuit and a classical computer for machine learning, the proposed framework paves the way toward applications of near-term quantum devices for quantum machine learning.
What this paper cites, inside the corpus
What cites it, inside the corpus
| Paper | Year | Cited |
|---|---|---|
| Variational quantum algorithms | 2021 | 3,226 |
| Supervised learning with quantum-enhanced feature spaces | 2019 | 2,548 |
| Noisy intermediate-scale quantum algorithms | 2022 | 1,770 |
| Superconducting Qubits: Current State of Play | 2019 | 1,467 |
Links
Topics
| Quantum Computing Algorithms and Architecture | Computer Science |
| Quantum Information and Cryptography | Computer Science |
| Neural Networks and Reservoir Computing | Computer Science |
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