Domain-Aware Quantum Circuit for QML

• URL: http://arxiv.org/abs/2512.17800v1
• Date: Fri, 19 Dec 2025 17:02:58 GMT
• Title: Domain-Aware Quantum Circuit for QML
• Authors: Gurinder Singh, Thaddeus Pellegrini, Kenneth M. Merz,,
• Abstract summary: We present a Domain-Aware Quantum Circuit (DAQC) that leverages image priors to guide locality-preserving encoding and entanglement.<n>The design employs interleaved encode--train cycles, where entanglement is applied among qubits hosting neighboring pixels, aligned to device connectivity.<n>We evaluate DAQC on MNIST, FashionMentangle, and PneumoniaMNIST datasets.
• Score: 0.7999703756441755
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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