Tunable Quantum Neural Networks in the QPAC-Learning Framework

• URL: http://arxiv.org/abs/2205.01514v4
• Date: Wed, 15 Nov 2023 11:42:47 GMT
• Title: Tunable Quantum Neural Networks in the QPAC-Learning Framework
• Authors: Viet Pham Ngoc (Imperial College London), David Tuckey (Imperial College London), Herbert Wiklicky (Imperial College London)
• Abstract summary: We investigate the performances of tunable quantum neural networks in the Quantum Probably Approximately Correct (QPAC) learning framework. In order to tune the network so that it can approximate a target concept, we have devised and implemented an algorithm based on amplitude amplification. The numerical results show that this approach can efficiently learn concepts from a simple class.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we investigate the performances of tunable quantum neural networks in the Quantum Probably Approximately Correct (QPAC) learning framework. Tunable neural networks are quantum circuits made of multi-controlled X gates. By tuning the set of controls these circuits are able to approximate any Boolean functions. This architecture is particularly suited to be used in the QPAC-learning framework as it can handle the superposition produced by the oracle. In order to tune the network so that it can approximate a target concept, we have devised and implemented an algorithm based on amplitude amplification. The numerical results show that this approach can efficiently learn concepts from a simple class.

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