Clinical Data Classification With Noisy Intermediate Scale Quantum Computers

Abstract

Quantum machine learning has experienced a significant progress in both software and hardware development in the recent years and has emerged as an applicable area of near-term quantum computers. In this work, we investigate the feasibility of utilizing quantum machine learning (QML) on real clinical datasets. We propose two QML algorithms for data classification on IBM quantum hardware: a quantum distance classifier (qDS) and a simplified quantum-kernel support vector machine (sqKSVM). We utilize these different methods using the linear time quantum data encoding technique (\({\text{log}}_{2}N\)) for embedding classical data into quantum states and estimating the inner product on 15-qubit IBMQ Melbourne quantum computer. We match the predictive performance of our QML approaches with prior QML methods and with their classical counterpart algorithms for three open-access clinical datasets. Our results imply that the qDS in small sample and feature count datasets outperforms kernel-based methods. In contrast, quantum kernel approaches outperform qDS in high sample and feature count datasets. We demonstrate that the \({\text{log}}_{2}N\) encoding increases predictive performance with up to +2% area under the receiver operator characteristics curve across all quantum machine learning approaches, thus, making it ideal for machine learning tasks executed in Noisy Intermediate Scale Quantum computers.

Quantum support vector machines for classification and regression on a trapped-ion quantum computer

“…Low-rank approximation in the noisy quantum kernel. A popular approach to reducing the noise in the quantum kernel is to use a depolarizing model (Hubregtsen et al 2022;Moradi et al 2022); however, such a noise model may not necessarily be suited for real quantum devices, because there are various sources of noise. In addition, at the time of conducting our quantum experiment, we were not able to access the full control of native quantum gates of the trapped-ion quantum computer in the cloud service.…”

“…In recent years, there has been remarkable progress in quantum hardware (de Leon et al 2021), opening the path for the implementation of NISQ algorithms. Previous studies on quantum kernels have explored the use of various quantum hardware platforms, such as superconducting qubits (Havlíček et al 2019;Djehiche and Löfdahl 2021;Heredge et al 2021;Peters et al 2021;Wang et al 2021;Hubregtsen et al 2022;Krunic et al 2022), trapped-ion qubits (Moradi et al 2022), Gaussian Boson Sampling (Schuld et al 2020;Giordani et al 2023), neutral-atom qubits (Albrecht et al 2023), and nuclear-spin qubits (Kusumoto et al 2021). Owing to quantum decoherence and the noise of quantum gates, one can typically perform a limited number of quantum operations on NISQ devices.…”

“…Previous studies have demonstrated the implementation of different NISQ algorithms on trapped-ion quantum computers; in particular, researchers have recently used the IonQ Harmony quantum processor and reported interesting results in quantum machine learning (Johri et al 2021;Ishiyama et al 2022;Rudolph et al 2022), finance (Zhu et al 2022), quantum chemistry (Nam et al 2020;Zhao et al 2023), and the generation of pseudo-random quantum state (Cenedese et al 2023). A recent study has shown the feasibility of implementing QSVM with a simple quantum circuit on a trapped-ion quantum computer (Moradi et al 2022); nonetheless, further investigation is necessary to understand the full potential of the quantum kernel method on this platform using a different quantum kernel and various datasets.…”

Beyond Limits: Charting New Horizons in Glioma Tumor Classification through Hybrid Quantum Computing with The Cancer Genome Atlas (TCGA) Data

“…Among the various quantum algorithms proposed as solutions to medical challenges, quantum-enhanced AI/machine learning methods stand out for their broad applicability. Recently, quantum computing and quantum AI methods have been shown to have numerous applications in the field of healthcare, including rapid genome analysis and sequencing [51,52], disease detection [53][54][55][56], classification [57][58][59][60][61], identification of new drug applications [62][63][64]. Furthermore, quantum computing models play a significant role in predicting the mutations of genes that are particularly critical in the pathogenesis and diagnosis of specific cancer types, such as GBM [65].…”

Effects of fitness function in genetically auto-generated quantum feature maps

“…Applying quantum mechanics on machine learning [4,21] is expected to have a performance on speeding up calculations. As for the classifier, quantum kernel method via support vector machine [10], among other development by different research groups [5,9,13,15,16,18,19,22], has been attested a powerful mean of using high dimensional quantum state space. These studies show the possibility of machine learning using quantum computers, which may have a boost in the future.…”