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.
Citation edge evidence
- Variational quantum algorithms→Quantum circuit learningLegacy edge — source detail unavailable
- Supervised learning with quantum-enhanced feature spaces→Quantum circuit learningLegacy edge — source detail unavailable
- Noisy intermediate-scale quantum algorithms→Quantum circuit learningLegacy edge — source detail unavailable
- Superconducting Qubits: Current State of Play→Quantum circuit learningLegacy edge — source detail unavailable
- Quantum circuit learning→Quantum Computation and Quantum InformationLegacy edge — source detail unavailable
- Quantum circuit learning→Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum C…Legacy edge — source detail unavailable
- Quantum circuit learning→A variational eigenvalue solver on a photonic quantum processorLegacy edge — source detail unavailable
- Quantum circuit learning→Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communi…Legacy edge — source detail unavailable
- Quantum circuit learning→Quantum Algorithm for Linear Systems of EquationsLegacy edge — source detail unavailable
- Quantum circuit learning→Hardware-efficient variational quantum eigensolver for small molecules and quantum magnetsLegacy edge — source detail unavailable
- Quantum circuit learning→Pattern recognition and machine learningLegacy edge — source detail unavailable
- Quantum circuit learning→Quantum Support Vector Machine for Big Data ClassificationLegacy edge — source detail unavailable
- Quantum circuit learning→Quantum computation and quantum informationLegacy edge — source detail unavailable
- Quantum Support Vector Machine for Big Data Classification→Quantum Algorithm for Linear Systems of EquationsLegacy edge — source detail unavailable
- Quantum Support Vector Machine for Big Data Classification→Pattern recognition and machine learningLegacy edge — source detail unavailable
- A variational eigenvalue solver on a photonic quantum processor→Quantum Algorithm for Linear Systems of EquationsLegacy edge — source detail unavailable
- Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets→A variational eigenvalue solver on a photonic quantum processorLegacy edge — source detail unavailable
- Supervised learning with quantum-enhanced feature spaces→Quantum Support Vector Machine for Big Data ClassificationLegacy edge — source detail unavailable
- Supervised learning with quantum-enhanced feature spaces→Hardware-efficient variational quantum eigensolver for small molecules and quantum magnetsLegacy edge — source detail unavailable
- Superconducting Qubits: Current State of Play→Quantum Algorithm for Linear Systems of EquationsLegacy edge — source detail unavailable
- Superconducting Qubits: Current State of Play→A variational eigenvalue solver on a photonic quantum processorLegacy edge — source detail unavailable
- Superconducting Qubits: Current State of Play→Hardware-efficient variational quantum eigensolver for small molecules and quantum magnetsLegacy edge — source detail unavailable
- Superconducting Qubits: Current State of Play→Supervised learning with quantum-enhanced feature spacesLegacy edge — source detail unavailable
- Variational quantum algorithms→Quantum Algorithm for Linear Systems of EquationsLegacy edge — source detail unavailable
- Variational quantum algorithms→A variational eigenvalue solver on a photonic quantum processorLegacy edge — source detail unavailable
- Variational quantum algorithms→Hardware-efficient variational quantum eigensolver for small molecules and quantum magnetsLegacy edge — source detail unavailable
- Variational quantum algorithms→Supervised learning with quantum-enhanced feature spacesLegacy edge — source detail unavailable
- Variational quantum algorithms→Quantum Computation and Quantum InformationLegacy edge — source detail unavailable
- Noisy intermediate-scale quantum algorithms→Quantum Algorithm for Linear Systems of EquationsLegacy edge — source detail unavailable
- Noisy intermediate-scale quantum algorithms→A variational eigenvalue solver on a photonic quantum processorLegacy edge — source detail unavailable
- Noisy intermediate-scale quantum algorithms→Hardware-efficient variational quantum eigensolver for small molecules and quantum magnetsLegacy edge — source detail unavailable
- Noisy intermediate-scale quantum algorithms→Supervised learning with quantum-enhanced feature spacesLegacy edge — source detail unavailable
- Noisy intermediate-scale quantum algorithms→Superconducting Qubits: Current State of PlayLegacy edge — source detail unavailable
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
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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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