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About 255214zdt85547o PDF
The document discusses quantum machine learning (QML) and provides an example of a quantum squared-distance classifier. The example uses amplitude encoding to represent input data as quantum states. It applies a Hadamard transform to create distance measures between the input states and calculates probabilities to predict labels, similarly to a classical squared-distance classifier. The key advantages of the quantum approach are that it can leverage interference effects and be implemented on a quantum computer to potentially solve certain machine learning problems more efficiently.
Detailed Information
| Author: | ['Zekrifa Djabeur Mohamed Seifeddine'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 102 |
| Language: | English |
| Format: | |
| Price: | FREE |
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