Benchmarking MedMNIST dataset on real quantum hardware

TL;DR

This paper benchmarks quantum machine learning models on the MedMNIST medical imaging dataset using real IBM quantum hardware, demonstrating the feasibility and challenges of practical quantum medical image classification.

Contribution

It presents the first comprehensive benchmarking of MedMNIST on real quantum hardware, incorporating noise mitigation and hardware-efficient quantum circuits for medical imaging.

Findings

Quantum models achieved promising classification accuracy.

Error mitigation techniques improved performance on noisy hardware.

Establishes a benchmark for future quantum healthcare applications.

Abstract

Quantum machine learning (QML) has emerged as a promising domain to leverage the computational capabilities of quantum systems to solve complex classification tasks. In this work, we present the first comprehensive QML study by benchmarking the MedMNIST-a diverse collection of medical imaging datasets on a 127-qubit real IBM quantum hardware, to evaluate the feasibility and performance of quantum models (without any classical neural networks) in practical applications. This study explores recent advancements in quantum computing such as device-aware quantum circuits, error suppression, and mitigation for medical image classification. Our methodology is comprised of three stages: preprocessing, generation of noise-resilient and hardware-efficient quantum circuits, optimizing/training of quantum circuits on classical hardware, and inference on real IBM quantum hardware. Firstly, we process all input images in the preprocessing stage to reduce the spatial dimension due to quantum hardware limitations. We generate hardware-efficient quantum circuits using backend properties expressible to learn complex patterns for medical image classification. After classical optimization of QML models, we perform inference on real quantum hardware. We also incorporate advanced error suppression and mitigation techniques in our QML workflow, including dynamical decoupling (DD), gate twirling, and matrix-free measurement mitigation (M3) to mitigate the effects of noise and improve classification performance. The experimental results showcase the potential of quantum computing for medical imaging and establish a benchmark for future advancements in QML applied to healthcare.