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About QNNs Are Kernel Methods
This technical manuscript summarizes the mathematical similarities between quantum computing and kernel methods, and how this connection can be used to interpret quantum machine learning models as kernel methods. Specifically, it discusses how encoding data into quantum states is analogous to a feature map in kernel methods, and how measurements of these encoded quantum states correspond to computing inner products in a Hilbert space, similar to how kernels work. The manuscript aims to provide a comprehensive review of this connection and demonstrate that both near-term variational quantum algorithms and fault-tolerant quantum algorithms can be formulated as classical kernel methods.
Detailed Information
| Author: | ['sashwat'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 25 |
| Language: | English |
| Format: | |
| Price: | FREE |
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