Explicit quantum surrogates for quantum kernel models

• URL: http://arxiv.org/abs/2408.03000v1
• Date: Tue, 6 Aug 2024 07:15:45 GMT
• Title: Explicit quantum surrogates for quantum kernel models
• Authors: Akimoto Nakayama, Hayata Morisaki, Kosuke Mitarai, Hiroshi Ueda, Keisuke Fujii,
• Abstract summary: We propose a quantum-classical hybrid algorithm to create an explicit quantum surrogate (EQS) for trained implicit models. This involves diagonalizing an observable from the implicit model and constructing a corresponding quantum circuit. The EQS framework reduces prediction costs, mitigates barren plateau issues, and combines the strengths of both QML approaches.
• Score: 0.6834295298053009
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum machine learning (QML) leverages quantum states for data encoding, with key approaches being explicit models that use parameterized quantum circuits and implicit models that use quantum kernels. Implicit models often have lower training errors but face issues such as overfitting and high prediction costs, while explicit models can struggle with complex training and barren plateaus. We propose a quantum-classical hybrid algorithm to create an explicit quantum surrogate (EQS) for trained implicit models. This involves diagonalizing an observable from the implicit model and constructing a corresponding quantum circuit using an extended automatic quantum circuit encoding (AQCE) algorithm. The EQS framework reduces prediction costs, mitigates barren plateau issues, and combines the strengths of both QML approaches.

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