Problem
Research questions and friction points this paper is trying to address.
Quantum Kernels Optimization
Machine Learning Efficiency
Automated Design
Automatic and Effective Discovery of Quantum Kernels
🤖 AI Summary
To address the challenges of labor-intensive, expert-dependent manual design of quantum kernels in quantum machine learning, this paper proposes the first automated search framework specifically tailored for quantum kernels. Methodologically, it models quantum circuits as differentiable combinatorial structures, integrating neural architecture search with AutoML paradigms and supporting general-purpose metaheuristic optimizers (e.g., genetic algorithms); crucially, it introduces physics-informed metrics—such as dynamical Lie algebra rank—to guide the search while jointly enforcing statistical and expressive capacity constraints. Experiments on high-energy physics datasets demonstrate that the discovered quantum kernels achieve test accuracies comparable to or exceeding those of handcrafted kernels, substantially reducing reliance on domain expertise. This work establishes the first physically interpretable and scalable framework for automated quantum kernel discovery, introducing a novel paradigm for quantum machine learning.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Quantum Kernels
Automated Optimization
Machine Learning in Quantum Computing
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