Work

Time-warping invariant quantum recurrent neural networks via quantum-classical adaptive gating

Year: 2023

Type: article

Abstract: Adaptive gating plays a key role in temporal data processing via classical recurrent neural networks (RNNs), 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 RNNs (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.

Source: Machine Learning Science and Technology

DOI: 10.1088/2632-2153/acff39

Authors: Ivana Nikoloska, Osvaldo Simeone, Leonardo Banchi, Petar Veličković

Institutions: Eindhoven University of Technology, King's College London, University of Florence, Google DeepMind (United Kingdom)

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,405