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Hybrid Variational Quantum-Classical Framework with Adaptive Weighting and Efficiency Assessment

Dilli Hang Rai

arXiv:2609.28491v1quant-phcs.AIcs.LG

TL;DR

Prior hybrid quantum-classical image-classification studies were often restricted to binary or single-dataset settings, with reproducibility, efficiency, interpretability, and validation/test evaluation comparatively underexplored. SimAM-HVQC combines parameter-free SimAM attention and classical feature extraction before a variational quantum circuit, and the paper reports competitive performance across multiple benchmarks alongside parameter efficiency and reproducibility.

  • Problem

    Hybrid quantum-classical image-classification studies often focused on binary classification or a single dataset, while reproducibility, efficiency, interpretability, and validation/test evaluation remained comparatively underexplored.

    Source: §I · p. 1

  • Method

    SimAM-HVQC combines a parameter-free SimAM attention module and classical feature extraction before processing features with a Variational Quantum Circuit.

    Source: §I · p. 1, B. SimAM Attention · p. 2, D. Quantum Processing · p. 2

  • Results

    The paper reports competitive, promising performance across multiple benchmarks while maintaining parameter efficiency and supporting reproducibility.

    Source: §VI · p. 4

  • Takeaways & Limitations

    The authors conclude that efficiency and interpretability analyses highlight SimAM-HVQC's potential for representation learning and visual recognition tasks.

    Source: §VI · p. 4

Abstract

from arXiv · show

Hybrid quantum-classical neural networks have emerged as a promising approach for leveraging quantum computing in machine learning while mitigating current hardware limitations. This paper presents Sim-HVQC, a hybrid Deep Quantum Neural Network that couples an adaptive, parameter-free SimAM weighting module with classical feature extraction to preserve class-discriminative information prior to encoding into a Variational Quantum Circuit (VQC). Previous studies are restricted to binary classification [1] [2] [3] [4] [5]. In contrast, the proposed framework is trained and evaluated on various multi-class datasets(MNIST, KMNIST, Fashion-MNIST, and EMNIST). The framework further demonstrates reproducibility, parameter efficiency, and interpretability through multi-seed evaluation, parameter analysis, and latent/quantum feature inspection. The source code is publicly available at https://github.com/Dilli822/ SimAM-HVQC

I. INTRODUCTION

Prior hybrid quantum-neural-network studies often used binary or single-dataset settings, and reproducibility, efficiency, interpretability, and test evaluation remained underexplored. SimAM-HVQC is proposed as a parameter-efficient hybrid model for multi-class image classification, with evaluations across benchmarks and analyses of reproducibility and representations.

I. INTRODUCTION

  • Earlier HQNN studies were commonly limited to binary classification or one benchmark, with some reporting training accuracy but no validation or test results.The introduction identifies a need for broader multi-class, multi-dataset evaluation with empirical validation and test performance.Source: §I · p. 1
  • SimAM-HVQC places a parameter-free attention mechanism before quantum encoding in a hybrid model for multi-class image classification.The paper presents this as an alternative, parameter-efficient approach.Source: §I · p. 1
  • The proposed Deep Quantum Neural Network integrates a SimAM attention-enhanced classical feature extractor with a Variational Quantum Circuit.Source: §I · p. 1
  • The framework is evaluated on MNIST, Fashion-MNIST, KMNIST, and EMNIST, with multi-seed, training-data-size, and representation analyses.The analyses include latent feature representations and quantum measurement outputs.Source: §I · p. 1

II. METHODOLOGY

The methodology section covers dataset preparation, classical feature extraction, the hybrid quantum-classical architecture, and its algorithm.

II. METHODOLOGY

  • The methodology encompasses dataset preparation, classical feature extraction, the hybrid architecture, and the corresponding algorithm.Source: §II · p. 1

A. Dataset

The evaluation uses four grayscale image datasets with a standard train-validation-test split; three have 10 classes and EMNIST Balanced has 47.

A. Dataset

  • All four datasets use a standard 70:15:15 train-validation-test split.Source: A. Dataset · p. 1
  • MNIST, Fashion-MNIST, and KMNIST each contain 10 classes of 28×28 grayscale images.Source: A. Dataset · p. 1
  • EMNIST Balanced contains 47 classes of 28×28 grayscale character images.Source: A. Dataset · p. 1

B. SimAM Attention

SimAM computes parameter-free, energy-based neuron-wise attention before the classical feature extractor compresses the representation for later quantum encoding.

B. SimAM Attention

  • SimAM refines the input feature map before dimensionality reduction using a parameter-free, energy-based formulation.Source: B. SimAM Attention · p. 2
  • The channel-wise mean and variance and each neuron's inverse energy contribute to SimAM's attention computation.The passage identifies λ as a regularization parameter and σ(·) as the sigmoid function.Source: B. SimAM Attention · p. 2
  • Activations more distinguishable from the underlying channel distribution receive larger attention coefficients.The refined representation is passed to the classical feature extractor for compression and subsequent quantum encoding.Source: B. SimAM Attention · p. 2

C. Classical Neural Network

The classical network learns a discriminative latent representation from the SimAM-enhanced input, producing features for downstream quantum processing and classification.

C. Classical Neural Network

D. Quantum Processing

The compressed classical representation is encoded into a variational quantum circuit, transformed by trainable operations, and read out through Pauli-Z measurements as features for classification.

D. Quantum Processing

  • The compressed representation F ∈ RQ is encoded into a variational quantum circuit for feature transformation.Source: D. Quantum Processing · p. 2
  • Pauli-Z expectation measurements produce Q quantum features that are forwarded to the final classifier.Source: D. Quantum Processing · p. 2
  • The circuit uses 8 qubits and 6 strongly entangling layers, with 144 trainable quantum parameters.Source: D. Quantum Processing · p. 2
  • Table I summarizes datasets, model architecture, quantum-circuit configuration, and training hyperparameters.Source: Table I · p. 2

III. EXPERIMENTAL SETUP

The experimental setup summarizes datasets, architecture, circuit configuration, and training hyperparameters; the model architecture connects classical feature extraction with variational quantum processing.

III. EXPERIMENTAL SETUP

  • Table I summarizes the datasets, model architecture, quantum-circuit configuration, and training hyperparameters.Source: §III · p. 2
  • The input image passes through parameter-free SIMAM, then a classical extractor reduces 784 dimensions to 256, 128, and Q features.Source: Figure 2 · p. 3
  • The Q features are scaled and angle-encoded into a VQC; Pauli-Z measurements then feed a classical classifier for the final prediction.Source: Figure 2 · p. 3

IV. RESULTS

The results section identifies a comparison with existing methods and an analysis across dataset-utilization levels, but the supplied passages give no numerical outcomes.

IV. RESULTS

  • Table II is designated as a comparison of proposed-method performance with existing methods.Source: Table II · p. 3
  • Table III is designated as reporting performance at different dataset-utilization levels.Source: Table III · p. 3

V. REPRODUCIBILITY, DATA-EXPERIMENT, ABLATION STUDY AND REPRESENTATION ANALYSIS

SimAM-HVQC is assessed through data-use and ablation analyses, computational efficiency, and inspection of latent and quantum representations. The reported findings include improved prediction with SimAM, structured representations, and reasonable performance with limited training data.

V. REPRODUCIBILITY, DATA-EXPERIMENT, ABLATION STUDY AND REPRESENTATION ANALYSIS

  • The model achieves reasonable performance with limited training data and benefits from increased dataset availability.Source: §V · p. 3
  • Including parameter-free SimAM improves accuracy and reduces classification loss across multiple benchmark datasets.Source: §V · p. 3
  • Latent representations become more structured with depth: the 8-dimensional quantum space retains clear class organization, while the classifier space has the most distinct clusters.The input space has 16.8% PCA variance, compared with 52.0–62.9% projected variance for quantum representations.Source: §V · p. 3, Figure 3 · p. 4
  • Occlusion-based qubit sensitivity maps identify different input regions as influential for individual measurements, suggesting complementary representations from the compressed latent embedding.Source: §V · p. 3
  • For n=200, SimAM improves accuracy and prediction confidence (Wilcoxon p = 4.54× 10−25; Cohen’s d = 0.349).The efficiency analysis also reports low quantum parameter count and sub-millisecond inference latency, suggesting limited computational overhead.Source: Figure 5 · p. 4, §V · p. 4
  • Across six variational layers, the eight qubits develop distinct response patterns, with inter-qubit correlations growing through circuit depth and final Pauli-Z outputs forming the classifier’s quantum feature vector.Source: §V · p. 4, Figure 4 · p. 4

VI. CONCLUSION

SimAM-HVQC demonstrates competitive performance across multiple standard benchmarks while maintaining parameter efficiency and supporting reproducibility. The paper presents its efficiency and interpretability analyses as supporting its potential for representation learning and visual recognition.

VI. CONCLUSION

  • SimAM-HVQC demonstrates competitive and promising performance across multiple standard benchmark datasets.Source: §VI · p. 4
  • The framework maintains parameter efficiency and supports reproducibility.Source: §VI · p. 4
  • Efficiency and interpretability analyses highlight the framework’s potential for representation learning and visual recognition tasks.Source: §VI · p. 4
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