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arXiv · 2301.08173

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

Ivana Nikoloska·Osvaldo Simeone·Leonardo Banchi·Petar Veličković
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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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Ivana Nikoloska, Osvaldo Simeone, Leonardo Banchi, Petar Veličković. 2023-01-19. Time-Warping Invariant Quantum Recurrent Neural Networks via Quantum-Classical Adaptive Gating. https://arxiv.org/abs/2301.08173

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