Iterative Quantum Feature Maps

TL;DR

The paper introduces Iterative Quantum Feature Maps (IQFMs), a hybrid framework that enhances deep quantum machine learning models by reducing quantum resource demands and noise effects through iterative, layer-wise training.

Contribution

It proposes a novel hybrid quantum-classical architecture with contrastive learning and layer-wise training to improve quantum feature maps without extensive variational parameter optimization.

Findings

IQFMs outperform quantum convolutional neural networks on noisy data.

IQFMs achieve classical neural network performance on image classification benchmarks.

The framework reduces quantum runtime and mitigates noise effects.

Abstract

Quantum machine learning models that leverage quantum circuits as quantum feature maps (QFMs) are recognized for their enhanced expressive power in learning tasks. Such models have demonstrated rigorous end-to-end quantum speedups for specific families of classification problems. However, deploying deep QFMs on real quantum hardware remains challenging due to circuit noise and hardware constraints. Additionally, variational quantum algorithms often suffer from computational bottlenecks, particularly in accurate gradient estimation, which significantly increases quantum resource demands during training. We propose Iterative Quantum Feature Maps (IQFMs), a hybrid quantum-classical framework that constructs a deep architecture by iteratively connecting shallow QFMs with classically computed augmentation weights. By incorporating contrastive learning and a layer-wise training mechanism, the IQFMs framework effectively reduces quantum runtime and mitigates noise-induced degradation. In tasks involving noisy quantum data, numerical experiments show that the IQFMs framework outperforms quantum convolutional neural networks, without requiring the optimization of variational quantum parameters. Even for a typical classical image classification benchmark, a carefully designed IQFMs framework achieves performance comparable to that of classical neural networks. This framework presents a promising path to address current limitations and harness the full potential of quantum-enhanced machine learning.