Semantic Scholar
Open Access
2018
1560 sitasi
Quantum circuit learning
K. Mitarai
M. Negoro
M. Kitagawa
K. Fujii
Abstrak
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.
Topik & Kata Kunci
Penulis (4)
K
K. Mitarai
M
M. Negoro
M
M. Kitagawa
K
K. Fujii
Akses Cepat
Informasi Jurnal
- Tahun Terbit
- 2018
- Bahasa
- en
- Total Sitasi
- 1560×
- Sumber Database
- Semantic Scholar
- DOI
- 10.1103/PhysRevA.98.032309
- Akses
- Open Access ✓