Predicting toxicity by quantum machine learning
arXiv:2008.07715 · doi:10.1088/2399-6528/abd3d8
Abstract
In recent years, parameterized quantum circuits have been regarded as machine learning models within the framework of the hybrid quantum-classical approach. Quantum machine learning (QML) has been applied to binary classification problems and unsupervised learning. However, practical quantum application to nonlinear regression tasks has received considerably less attention. Here, we develop QML models designed for predicting the toxicity of 221 phenols on the basis of quantitative structure activity relationship. The results suggest that our data encoding enhanced by quantum entanglement provided more expressive power than the previous ones, implying that quantum correlation could be beneficial for the feature map representation of classical data. Our QML models performed significantly better than the multiple linear regression method. Furthermore, our simulations indicate that the QML models were comparable to those obtained using radial basis function networks, while improving the generalization performance. The present study implies that QML could be an alternative approach for nonlinear regression tasks such as cheminformatics.
arXiv commentmain: 4 tables, 6 figures; appendices: 2 tables, 2 figures
References in corpus (29)
- Quantum Computing in the NISQ era and beyond
- Quantum Machine Learning
- A variational eigenvalue solver on a quantum processor
- Hardware-efficient Variational Quantum Eigensolver for Small Molecules and Quantum Magnets
- Supervised learning with quantum enhanced feature spaces
- The theory of variational hybrid quantum-classical algorithms
- Quantum machine learning in feature Hilbert spaces
- Quantum Circuit Learning
- Quantum Chemistry in the Age of Quantum Computing
- Quantum Convolutional Neural Networks
- Parameterized quantum circuits as machine learning models
- An adaptive variational algorithm for exact molecular simulations on a quantum computer
- Circuit-centric quantum classifiers
- Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms
- Quantum Approximate Optimization Algorithm: Performance, Mechanism, and Implementation on Near-Term Devices
- Quantum optimization using variational algorithms on near-term quantum devices
- Data re-uploading for a universal quantum classifier
- Quantum machine learning: a classical perspective
- Quantum chemistry calculations on a trapped-ion quantum simulator
- Robust data encodings for quantum classifiers
- Hierarchical quantum classifiers
- Towards Quantum Machine Learning with Tensor Networks
- The Expressive Power of Parameterized Quantum Circuits
- Quantum Risk Analysis
- Quantum computational finance: Monte Carlo pricing of financial derivatives
- Training of Quantum Circuits on a Hybrid Quantum Computer
- Quantum annealing versus classical machine learning applied to a simplified computational biology problem
- Quantum generative adversarial learning in a superconducting quantum circuit
- Concatenated tensor network states