Universal discriminative quantum neural networks
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Summary
This work shows that it is possible to train a quantum circuit to discriminate quantum data with a trade-off between minimizing error rates and inconclusiveness rates of the classification tasks and achieves a performance which is close to the theoretically optimal values and a generalization ability to previously unseen quantum data.
- Type
- article
- Published
- 2018-05-22
- Cited by
- 91
- References
- 74
- Access
- Open access
- OpenAlex
- https://openalex.org/W2803434569
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:46893731
Keywords
Quantum circuit, Computer science, Quantum process, Quantum phase estimation algorithm, Quantum algorithm
References
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- Quantum principal component analysis
- Quantum cryptography using any two nonorthogonal states.
- An introduction to quantum machine learning
- Topological insulators and superconductors
- Quantum state discrimination
- Smoothed analysis of algorithms: Why the simplex algorithm usually takes polynomial time
- Quantum algorithms: Equation solving by simulation
- Quantum state discrimination
- Quantum detection and estimation theory
- Optical codeword demodulation with error rates below the standard quantum limit using a conditional nulling receiver
Cited by
- Adversarial quantum circuit learning for pure state approximation
- Continuous-variable quantum neural networks
- PennyLane: Automatic differentiation of hybrid quantum-classical computations
- An initialization strategy for addressing barren plateaus in parametrized quantum circuits
- Experimental realization of a quantum autoencoder via a universal two-qubit unitary gate
- Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future
- Variational quantum unsampling on a quantum photonic processor
- Solving Quantum Channel Discrimination Problem With Quantum Networks and Quantum Neural Networks
- Quantum circuit structure learning
- Quantum Computing: An Overview Across the System Stack
- Parameterized quantum circuits as machine learning models
- Quantum state discrimination using noisy quantum neural networks
- Variational Quantum Information Processing
- Layerwise learning for quantum neural networks
- Realization of a quantum autoencoder for lossless compression of quantum data
- Quantum computing methods for supervised learning
- Dynamical mean field theory algorithm and experiment on quantum computers
- Effect of data encoding on the expressive power of variational quantum-machine-learning models
- Quantum discriminator for binary classification
- Optimal provable robustness of quantum classification via quantum hypothesis testing
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