Researchers Develop and Test Binary Quantum Classifier Using Quantum Circuits
A physics undergraduate thesis from the Università degli Studi di Milano presents a binary quantum classifier built on quantum circuits, tested on handwritten digit recognition and high-energy particle collision data. The work uses Qibo, an open-source quantum simulation framework, and evaluates multiple optimization strategies across varying circuit depths and training set sizes. The study highlights both the feasibility and current limitations of quantum classifiers relative to classical convolutional neural networks.
Lorenzo Confalonieri's bachelor's thesis, supervised by Adrián Pérez-Salinas and Stefano Carrazza, investigates quantum machine learning by implementing a binary quantum classifier using parameterized quantum circuits. The classifier was tested on two datasets: a reduced MNIST set (handwritten zeros and ones) and simulated high-energy proton-proton collision events from LHC-like conditions, including scenarios with and without pile-up effects. The study systematically varied the number of circuit layers (Ansatz depth) and training set sizes while comparing multiple classical minimizers for optimizing the circuit parameters. Performance was evaluated using ROC curves, AUC scores, confusion matrices, and test accuracy. For pile-up collision images, the quantum classifier's results were benchmarked against a small convolutional neural network, revealing both the promise and the practical constraints of near-term quantum approaches. The thesis concludes that a functional binary quantum classifier can be constructed with current tools, but notes meaningful performance gaps compared to classical methods at comparable scales.
What's missing
As a bachelor's thesis preprint, this work has not undergone formal peer review. Key open questions include how the classifier scales to larger, non-reduced datasets and deeper circuits given quantum hardware noise; whether the comparisons to the convolutional neural network controlled for model parameter counts; and how results would change on actual quantum hardware versus simulation.
What different sources said
- arXiv physicsCenter
Towards the implementation of a quantum classifier
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