Abstract This paper presents a parallel quantum–classical hybrid architecture for image classification that integrates classical feature extraction with parallel quantum processing while maintaining competitive performance and improving parameter efficiency. Low-level and intermediate image features are first extracted by a shared convolutional neural network and then processed by multiple parallel quantum branches. Each branch employs a multilayer variational quantum circuit with a rotation–entanglement–rotation structure. To enhance representation diversity, two encoding-specific strategies are introduced. For amplitude encoding, deterministic spatial permutations generate distinct structural views of the same feature map across branches. For angle encoding, a layer-wise data re-uploading mechanism injects complementary feature subsets into different branches and circuit layers. Heterogeneous entanglement topologies with dynamic routing further diversify qubit interactions without increasing the number of trainable parameters. The measured expectation values from all branches are concatenated and fed into a compact linear classifier. Experiments on MNIST, CIFAR-10, and a thermal image dataset show that the proposed architecture achieves 98.35% accuracy on MNIST and competitive results on the other two datasets, while using substantially fewer parameters in the classification head than a fully classical baseline with the same feature extractor. These results demonstrate that parallel quantum processing combined with structured data embedding can serve as an effective and parameter-efficient decision stage for image classification.
In this paper we have introduced a novel method for gamma hadron separation in Imaging Atmospheric Cherenkov Telescopes (IACT) using Quantum Machine Learning. IACTs captures images of Extensive Air Showers (EAS) produced from very high energy gamma rays. We have used the QML Algorithms, Quantum Support Vector Classifier (QSVC) and Variational Quantum Classifier (VQC) for binary classification of the events into signals (Gamma) and background(hadron) using the image parameters. MAGIC Gamma Telescope dataset is used for this study which was generated from Monte Carlo Software Coriska. These quantum algorithms achieve performance comparable to standard multivariate classification techniques and can be used to solve variety of real-world problems. The classification accuracy is improved by hyper parameter tuning. We propose a new architecture for using QSVC efficiently on large datasets and found that clustering enhance the overall performance.
We present a quantum optical pattern recognition method for binary classification tasks. Without direct image reconstruction, it classifies an object in terms of the rate of two-photon coincidences at the output of a Hong-Ou-Mandel interferometer, where both the input and the classifier parameters are encoded into single-photon states. Our method exhibits the same behaviour of a classical neuron of unit depth. Once trained, it shows a constant
O(1) complexity in the number of computational operations and photons required by a single classification. This is a superexponential advantage over a classical neuron (that is at least linear in the image resolution). We provide simulations and analytical comparisons with analogous neural network architectures.