# A practical overview of image classification with variational tensor-network quantum circuits Source: [A practical overview of image classification with variational tensor-network quantum circuits]() Collection index: [llms.txt]() Data updated: 2025-10-25T03:51:02.518Z Authors: Diego Guala, Shaoming Zhang, Esther Cruz, Carlos A. Riofr\\'io, Johannes Klepsch, Juan Miguel Arrazola Published: 2022-09-22T14:52:21.000Z ## Abstract Circuit design for quantum machine learning remains a formidable challenge. Inspired by the applications of tensor networks across different fields and their novel presence in the classical machine learning context, one proposed method to design variational circuits is to base the circuit architecture on tensor networks. Here, we comprehensively describe tensor-network quantum circuits and how to implement them in simulations. This includes leveraging circuit cutting, a technique used to evaluate circuits with more qubits than those available on current quantum devices. We then illustrate the computational requirements and possible applications by simulating various tensor-network quantum circuits with PennyLane, an open-source python library for differential programming of quantum computers. Finally, we demonstrate how to apply these circuits to increasingly complex image processing tasks, completing this overview of a flexible method to design circuits that can be applied to industrially-relevant machine learning tasks. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()