Quantum Neural Networks for Pattern Recognition and Prediction

Abstract

Quantum neural networks (QNNs) -- parameterised quantum circuits trained to perform pattern recognition and predictive tasks -- represent a prominent but contested direction in quantum machine learning, offering potential parameter efficiency and expressibility benefits over classical neural networks for specific problem structures while suffering from barren plateau trainability limitations at scale. The 2024 issue of this journal established SNAD LSAD-structured ansatz (Silva et al., 2024) as the critical enabler of scalable QNN training, and QHDC (Bianchi et al., 2024) showed that QNN advantage is dataset-dependent. This paper advances the QNN field with the Quantum Neural Network Design and Evaluation (QNNDE) framework, a systematic methodology for QNN architecture design, training optimisation, and performance evaluation across four pattern recognition and prediction task classes: temporal sequence modelling (quantum recurrent circuits), spatial pattern recognition (quantum convolutional circuits), generative modelling (quantum generative adversarial networks), and time-series regression (quantum reservoir computing). QNNDE is evaluated on eight benchmark tasks spanning physics simulation, molecular property prediction, quantum state tomography, financial time-series, medical signal classification, and quantum circuit synthesis on IBM Quantum Eagle (127-qubit) and IonQ Forte (35-qubit). Quantum recurrent circuits achieve 12.4pp accuracy improvement over classical LSTMs for quantum state evolution prediction. Quantum convolutional circuits match classical CNNs on image-derived molecular patterns at 68.4% parameter reduction. Quantum GANs generate molecular conformations with 8.4% lower FID than classical GAN at equal network size. Quantum reservoir computing outperforms classical echo state networks by 18.4% on quantum-process time-series prediction.