Quantum Embedding Search for Quantum Machine Learning
arXiv:2105.11853 · doi:10.1109/ACCESS.2022.3167398
Abstract
This paper introduces a novel quantum embedding search algorithm (QES, pronounced as "quest"), enabling search for optimal quantum embedding design for a specific dataset of interest. First, we establish the connection between the structures of quantum embedding and the representations of directed multi-graphs, enabling a well-defined search space. Second, we instigate the entanglement level to reduce the cardinality of the search space to a feasible size for practical implementations. Finally, we mitigate the cost of evaluating the true loss function by using surrogate models via sequential model-based optimization. We demonstrate the feasibility of our proposed approach on synthesis and Iris datasets, which empirically shows that found quantum embedding architecture by QES outperforms manual designs whereas achieving comparable performance to classical machine learning models.
References in corpus (33)
- Practical Bayesian Optimization of Machine Learning Algorithms
- Neural Architecture Search with Reinforcement Learning
- Supervised learning with quantum enhanced feature spaces
- Barren plateaus in quantum neural network training landscapes
- The theory of variational hybrid quantum-classical algorithms
- A Quantum Approximate Optimization Algorithm
- Neural Architecture Search: A Survey
- Quantum machine learning in feature Hilbert spaces
- Quantum Circuit Learning
- Quantum principal component analysis
- Parameterized quantum circuits as machine learning models
- Circuit-centric quantum classifiers
- Power of data in quantum machine learning
- Quantum-enhanced machine learning
- Transfer learning in hybrid classical-quantum neural networks
- Continuous-variable quantum neural networks
- Towards Quantum Machine Learning with Tensor Networks
- Automated optimization of large quantum circuits with continuous parameters
- Structure optimization for parameterized quantum circuits
- A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions
- Instantaneous Quantum Computation
- Supervised quantum machine learning models are kernel methods
- Quantum machine learning for quantum anomaly detection
- A quantum algorithm to train neural networks using low-depth circuits
- Learning to learn with quantum neural networks via classical neural networks
- Quantum approximate optimization is computationally universal
- Quantum circuit optimization with deep reinforcement learning
- Quantum embeddings for machine learning
- A quantum alternating operator ansatz with hard and soft constraints for lattice protein folding
- Contrastive Self-supervised Neural Architecture Search
- Optimizing Ansatz Design in QAOA for Max-cut
- Quantum supremacy and high-dimensional integration
- Translational Quantum Machine Intelligence for Modeling Tumor Dynamics in Oncology