Domain-Aware Quantum Circuit for QML

TL;DR

This paper introduces a Domain-Aware Quantum Circuit (DAQC) that uses image priors and locality-preserving encoding to improve quantum machine learning on noisy hardware, achieving state-of-the-art results on real quantum devices.

Contribution

The paper proposes a novel quantum circuit design that leverages image priors and locality-preserving encoding to enhance QML performance on NISQ devices, with competitive results on real hardware.

Findings

DAQC achieves performance comparable to classical deep learning models.

DAQC outperforms existing quantum circuit search baselines.

The design effectively mitigates barren-plateau issues in quantum training.

Abstract

Designing parameterized quantum circuits (PQCs) that are expressive, trainable, and robust to hardware noise is a central challenge for quantum machine learning (QML) on noisy intermediate-scale quantum (NISQ) devices. We present a Domain-Aware Quantum Circuit (DAQC) that leverages image priors to guide locality-preserving encoding and entanglement via non-overlapping DCT-style zigzag windows. The design employs interleaved encode-entangle-train cycles, where entanglement is applied among qubits hosting neighboring pixels, aligned to device connectivity. This staged, locality-preserving information flow expands the effective receptive field without deep global mixing, enabling efficient use of limited depth and qubits. The design concentrates representational capacity on short-range correlations, reduces long-range two-qubit operations, and encourages stable optimization, thereby mitigating depth-induced and globally entangled barren-plateau effects. We evaluate DAQC on MNIST, FashionMNIST, and PneumoniaMNIST datasets. On quantum hardware, DAQC achieves performance competitive with strong classical baselines (e.g., ResNet-18/50, DenseNet-121, EfficientNet-B0) and substantially outperforming Quantum Circuit Search (QCS) baselines. To the best of our knowledge, DAQC, which uses a quantum feature extractor with only a linear classical readout (no deep classical backbone), currently achieves the best reported performance on real quantum hardware for QML-based image classification tasks. Code and pretrained models are available at: https://github.com/gurinder-hub/DAQC.