[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82097-en":3,"doc-seo-82097-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},82097,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","A Novel Parallel QCNN Architecture with Efficient Classical Simulability","The document studies a new implementation of a Quantum Convolutional Neural Network (QCNN) for binary image classification on the MNIST dataset. A hierarchical partitioning strategy divides each input image into smaller regions, encodes them into independent quantum states, and then merges partitions via a repeated reduction procedure until a single qubit remains for measurement. This enables efficient classical simulation of a large QCNN by parallelizing across processes without exponentially increasing hardware needs, including training a 128-qubit model. Experiments compare accuracy with and without partitioning and show no performance degradation and sometimes improved results, attributed to reduced barren-plateau effects.","A Novel Parallel QCNN Architecture with Efficient  \nClassical Simulability  \nLawrence Nguyen  \nDepartment of Electrical Engineering San Jose´ State University San Jos, USA [lawrence.nguyen@sjsu.edu](lawrence.nguyen@sjsu.edu)  \nHiu Yung Wong  \nDepartment of Electrical Engineering San Jose´ State University San Jos, USA [hiuyung.wong@sjsu.edu](hiuyung.wong@sjsu.edu)  \narXiv :2607 .08928v1 [ quant-ph] 9 Jul 2026  \nAbstract—This work presents a study of an implementation of a novel Quantum Convolutional Neural Network (QCNN) for binary classification of images from the Modified National Institute of Standards and Technology (MNIST) dataset. Using a novel architecture inspired by previous QCNN and classical convolutional neural network (CNN) implementations, we use a hierarchical partitioning approach to implement a QCNN circuit that can be approximated and simulated efficiently on a classical machine for a large problem. First, the original image is partitioned such that each process handles a smaller portion of the image, which is encoded into independent states. Then, these partitions merge and combine, resulting in states that contain information from both partitions while halving the number of processes. After repeating this until one process remains, we reduce the dimensionality of the state until a single qubit remains for measurement. Using this approach, we can use multiple processes in parallel to simulate a large QCNN program without the need for exponentially growing hardware requirements asthe number of qubits increases. In our work, we use this scheme to train a 128-qubit model, which is impossible to run on any classical supercomputer without the novel architecture. We also explore the impact of this new model architecture on prediction accuracy by training it to perform binary classification on the MNIST dataset with a small number of qubits, and comparing it to a model without partitioning. Our initial findings show that partitioning images into smaller sub-images with this architecture does not degrade the model’s performance and sometimes even improves it, likely because it reduces the Barren plateaus issue in the partitioning process.  \nIndex Terms—Classification, Convolutional Neural Network, Quantum Computing, Simulation  \nI. INTRODUCTION  \nA. Motivation  \nAs the field of quantum computing continues to mature, quantum neural networks are becoming an increasingly attractive research topic. QCNNs are an example of a quantum neural network that takes many design inspirations from classical CNNs, and they use parameterized quantum circuits to classify images [1] . In a previous study, researchers trained QCNN models to classify handwritten digits with using a system of 49 qubits, achieving an accuracy of 96% using quantum hardware [2] . Simulating this number of qubits on a classical machine requires large amounts of memory, as seen in another study that simulated a 61-qubit quantum circuit using 768 terabytes of memory [3], and it is almost impossible to  \nscale to 128 qubits in the near future. When limited classical hardware is available, simulating QCNNs can serve only as educational examples with little practical value. For example, the QCNN example provided in the Qiskit machine learning tutorial uses an approach that encodes each pixel of the image being classified into a qubit [4], and only 8 qubits are used to encode 8 pixels (2×4 grid) . However, if a QCNN is to ever be useful, it must be able to classify images larger than a 2×4 grid of pixels, such as the 28×28 handwritten digits in the MNIST dataset [5] . Encoding each pixel into a single qubit becomes an unfeasible strategy unless we drastically reduce the size of the image to be classified, which carries the risk of decreasing the accuracy of the model due to the loss of pixel information.  \nPrevious studies involving both QCNNs and CNNs have employed parallelization schemes to partition larger problems into smaller subproblems [6] [7] . In the QCNN","cbCaikqWpMOMdqSd","https://ap.wps.com/l/cbCaikqWpMOMdqSd","pdf",1269795,1,10,"English","en",105,"# Introduction\n## Motivation\n## Paper Contents and Organization\n# Software Libraries, Hardware, and Dataset\n## CNN Basics and Qiskit Tutorial Background\n# Parallel Architecture Implementation\n## Image Partitioning and Data Manipulation\n## Parameter Definition\n## Training Loop\n# Experimental Results\n# Conclusion","[{\"question\":\"What problem does the proposed QCNN architecture address?\",\"answer\":\"It targets efficient classical simulation of a QCNN for image classification at a scale that would be infeasible for standard classical approaches, exemplified by training a 128-qubit model.\"},{\"question\":\"How does the hierarchical partitioning process work?\",\"answer\":\"The input image is partitioned so different processes encode separate parts into independent quantum states, then partitions merge and the number of processes is halved repeatedly until one process remains and the state is reduced to a single qubit for measurement.\"},{\"question\":\"How does partitioning affect prediction accuracy?\",\"answer\":\"Initial results indicate partitioning does not degrade performance and can improve it, likely by mitigating barren plateau issues during the partitioning 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problem does the proposed QCNN architecture address?","Question",{"text":75,"@type":76},"It targets efficient classical simulation of a QCNN for image classification at a scale that would be infeasible for standard classical approaches, exemplified by training a 128-qubit model.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the hierarchical partitioning process work?",{"text":80,"@type":76},"The input image is partitioned so different processes encode separate parts into independent quantum states, then partitions merge and the number of processes is halved repeatedly until one process remains and the state is reduced to a single qubit for measurement.",{"name":82,"@type":73,"acceptedAnswer":83},"How does partitioning affect prediction accuracy?",{"text":84,"@type":76},"Initial results indicate partitioning does not degrade performance and can improve it, likely by mitigating barren plateau issues during the partitioning 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