Quantum RNNs and LSTMs Through Entangling and Disentangling Power of Unitary Transformations

• URL: http://arxiv.org/abs/2505.06774v1
• Date: Sat, 10 May 2025 22:56:18 GMT
• Title: Quantum RNNs and LSTMs Through Entangling and Disentangling Power of Unitary Transformations
• Authors: Ammar Daskin,
• Abstract summary: We discuss how quantum recurrent neural networks (RNNs) and their enhanced version, long short-term memory (LSTM) networks, can be modeled.<n>In particular, we interpret entangling and disentangling power as information retention and forgetting mechanisms in LSTMs.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we discuss how quantum recurrent neural networks (RNNs) and their enhanced version, long short-term memory (LSTM) networks, can be modeled using the core ideas presented in Ref.[1], where the entangling and disentangling power of unitary transformations is investigated. In particular, we interpret entangling and disentangling power as information retention and forgetting mechanisms in LSTMs. Therefore, entanglement becomes a key component of the optimization (training) process. We believe that, by leveraging prior knowledge of the entangling power of unitaries, the proposed quantum-classical framework can guide and help to design better-parameterized quantum circuits for various real-world applications.

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