Time-Warping Invariant Quantum Recurrent Neural Networks via Quantum-Classical Adaptive Gating

• URL: http://arxiv.org/abs/2301.08173v3
• Date: Fri, 9 Jun 2023 08:40:45 GMT
• Title: Time-Warping Invariant Quantum Recurrent Neural Networks via Quantum-Classical Adaptive Gating
• Abstract summary: This paper builds on quantum recurrent neural networks (QRNNs), a dynamic model with quantum memory, to introduce a novel class of temporal data processing quantum models. The model, referred to as time warping-invariant QRNN (TWI-QRNN), augments a QRNN with a quantum-classical adaptive gating mechanism. Its capacity to successfully implement time-warping transformations is experimentally demonstrated on examples with classical or quantum dynamics.
• Score: 26.52114287391472
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Adaptive gating plays a key role in temporal data processing via classical recurrent neural networks (RNN), as it facilitates retention of past information necessary to predict the future, providing a mechanism that preserves invariance to time warping transformations. This paper builds on quantum recurrent neural networks (QRNNs), a dynamic model with quantum memory, to introduce a novel class of temporal data processing quantum models that preserve invariance to time-warping transformations of the (classical) input-output sequences. The model, referred to as time warping-invariant QRNN (TWI-QRNN), augments a QRNN with a quantum-classical adaptive gating mechanism that chooses whether to apply a parameterized unitary transformation at each time step as a function of the past samples of the input sequence via a classical recurrent model. The TWI-QRNN model class is derived from first principles, and its capacity to successfully implement time-warping transformations is experimentally demonstrated on examples with classical or quantum dynamics.

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