Automatic and effective discovery of quantum kernels

Abstract

Quantum computing can empower machine learning models by enabling kernel machines to leverage quantum kernels for representing similarity measures between data. Quantum kernels are able to capture relationships in the data that are not efficiently computable on classical devices. However, there is no straightforward method to engineer the optimal quantum kernel for each specific use case. We present an approach to this problem, which employs optimization techniques, similar to those used in neural architecture search and AutoML, to automatically find an optimal kernel in a heuristic manner. To this purpose we define an algorithm for constructing a quantum circuit implementing the similarity measure as a combinatorial object, which is evaluated based on a cost function and then iteratively modified using a meta-heuristic optimization technique. The cost function can encode many criteria ensuring favorable statistical properties of the candidate solution, such as the rank of the Dynamical Lie Algebra. Importantly, our approach is independent of the optimization technique employed. The results obtained by testing our approach on a high-energy physics problem demonstrate that, in the best-case scenario, we can either match or improve testing accuracy with respect to the manual design approach, showing the potential of our technique to deliver superior results with reduced effort.

Accepted into IEEE Transactions on Emerging Topics in Computational Intelligence

References in corpus (19)

• Quantum Computing in the NISQ era and beyond
• Quantum Machine Learning
• Supervised learning with quantum enhanced feature spaces
• Quantum machine learning in feature Hilbert spaces
• Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms
• Power of data in quantum machine learning
• Challenges and Opportunities in Quantum Machine Learning
• A rigorous and robust quantum speed-up in supervised machine learning
• Kernel Mean Embedding of Distributions: A Review and Beyond
• Learning Decentralized Controllers for Robot Swarms with Graph Neural Networks
• Diagnosing Barren Plateaus with Tools from Quantum Optimal Control
• Spectral Bias and Task-Model Alignment Explain Generalization in Kernel Regression and Infinitely Wide Neural Networks
• Universal expressiveness of variational quantum classifiers and quantum kernels for support vector machines
• Quantum anomaly detection in the latent space of proton collision events at the LHC
• Unravelling physics beyond the standard model with classical and quantum anomaly detection
• Asymptotic learning curves of kernel methods: empirical data v.s. Teacher-Student paradigm
• Generating a state $t$-design by diagonal quantum circuits
• Efficient solvability of Hamiltonians and limits on the power of some quantum computational models
• Quantum Advantage Seeker with Kernels (QuASK): a software framework to speed up the research in quantum machine learning

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• A Cyclic Layerwise QAOA Training
• Learning Reduced Representations for Quantum Classifiers
• Integrated Encoding and Quantization to Enhance Quanvolutional Neural Networks
• Quantum-Stable Robust Principal Component Analysis: Theory and Evidence from NISQ Regimes