Hybrid Quantum-Classical Recurrent Neural Networks

TL;DR

This paper introduces a hybrid quantum-classical recurrent neural network (QRNN) architecture that leverages quantum memory and classical control to perform sequence learning tasks, demonstrating competitive results in various benchmarks.

Contribution

The paper presents the first quantum-grounded RNN model combining unitary quantum recurrence with classical nonlinear control, enabling efficient sequence learning.

Findings

Achieved competitive performance on sentiment analysis, MNIST, and language modeling.

Implemented a quantum-inspired attention mechanism improving translation tasks.

Demonstrated the model's scalability up to 14 qubits in simulation.

Abstract

We present a hybrid quantum-classical recurrent neural network (QRNN) architecture in which the recurrent core is realized as a parametrized quantum circuit (PQC) controlled by a classical feedforward network. The hidden state is the quantum state of an $n$-qubit PQC in an exponentially large Hilbert space $\mathbb{C}^{2^n}$, which serves as a coherent recurrent quantum memory. The PQC is unitary by construction, making the hidden-state evolution norm-preserving without external constraints. At each timestep, mid-circuit Pauli expectation-value readouts are combined with the input embedding and processed by the feedforward network, which provides explicit classical nonlinearity. The outputs parametrize the PQC, which updates the hidden state via unitary dynamics. The QRNN is compact and physically consistent, and it unifies (i) unitary recurrence as a high-capacity memory, (ii) partial observation via mid-circuit readouts, and (iii) nonlinear classical control for input-conditioned parametrization. We evaluate the model in simulation with up to 14 qubits on sentiment analysis, MNIST, permuted MNIST, copying memory, and language modeling. For sequence-to-sequence learning, we further devise a soft attention mechanism over the mid-circuit readouts and show its effectiveness for machine translation. To our knowledge, this is the first model (RNN or otherwise) grounded in quantum operations to achieve competitive performance against strong classical baselines across a broad class of sequence-learning tasks.