Classical ML and quantum computing mutually accelerate each other through error correction, control, simulation data, and quantum-native learning, forming a virtuous cycle toward quantum intelligence.
New perspectives on quantum kernels through the lens of entangled tensor kernels
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abstract
Quantum kernel methods are one of the most explored approaches to quantum machine learning. However, the structural properties and inductive bias of quantum kernels are not fully understood. In this work, we introduce the notion of entangled tensor kernels - a generalization of product kernels from classical kernel theory - and show that all embedding quantum kernels can be understood as an entangled tensor kernel. We discuss how this perspective allows one to gain novel insights into both the unique inductive bias of quantum kernels, and potential methods for their dequantization.
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quant-ph 1years
2026 1verdicts
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The Virtuous Cycle of Quantum-Classical Machine Learning
Classical ML and quantum computing mutually accelerate each other through error correction, control, simulation data, and quantum-native learning, forming a virtuous cycle toward quantum intelligence.