Breast Cancer Detection Using Quantum Convolutional Neural Networks: A Demonstration on a Quantum Computer

Bisarya, Aradh; We, Maouaki; Mukhopadhyay, Sabyasachi; Mishra, Niraj Kumar; Kumar, Sachin; Bk, Behera; Pk, Panigrahi; De, Dipankar

doi:10.1101/2020.06.21.20136655

Cited by 14 publications

(4 citation statements)

References 19 publications

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“…In this context, different research groups introduced the concepts of supervised machine learning modeling using QC, which deals with feature selection, parameter encoding, and parameterized circuit formation [165], [171]. For instance, in [38], [172], the team showcased the practicality of their suggested quantum embedding method through simulations and tests on standard datasets such as Iris and Breast Cancer. Their findings suggest that the quantum embedding search approach for supervised QML, i.e., the QES architecture, surpasses manual methods in terms of predictive performance.…”

Section: ) Integration Of Quantum Computing With Machinementioning

confidence: 99%

Beyond Bits: A Review of Quantum Embedding Techniques for Efficient Information Processing

Khan,

Aman,

Sikdar

2024

IEEE Access

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The existing body of research on quantum embedding techniques is not only confined in scope but also lacks a comprehensive understanding of the intricacies of the quantum embedding process. To address this critical issue, this article explores quantum encoding schemes, uncovering valuable insights into their encoding algorithms from theoretical foundations to a mathematical perspective, as well as practical applications. Initially, the article briefly overviews classical computing and the limitations associated with classical bits in representing and processing complex information. Next, the article scrutinizes a variety of quantum embedding patterns, including basis encoding, amplitude encoding, Qsample encoding, angle encoding, quantum associative memory encoding, quantum random access memory, superdense encoding, Hamiltonian encoding, and others. In addition, each technique is accompanied by mathematical formulas and examples illustrating how each strategy can be applied. Finally, the article provides a comparative analysis of different quantum embedding/encoding methods, outlining their strengths and limitations. Overall, this insightful article highlights the potential of quantum encoding techniques for efficient information processing beyond classical bits, thereby facilitating scientists and design engineers in selecting the most appropriate encoding technique to develop smart algorithms for revolutionizing the field of quantum computing.INDEX TERMS Encoding patterns, qubits, quantum computing, quantum information processing, quantum circuits.

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Section: ) Integration Of Quantum Computing With Machinementioning

confidence: 99%

Beyond Bits: A Review of Quantum Embedding Techniques for Efficient Information Processing

Khan,

Aman,

Sikdar

2024

IEEE Access

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“…In the past two years, some researchers have applied quantum techniques to diagnose different diseases. A quantum framework has been presented for breast cancer detection in [25]. The classical-quantum transfer learning method has been implemented on IBM quantum computer for the detection of COVID-19 [26].…”

Section: Literature Review On Disease Diagnosis Using Quantum Computingmentioning

confidence: 99%

A Novel Hybrid Classical- Quantum Network to Detect Epileptic Seizures

Sameer

Gupta

2022

Preprint

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BackgroundMachine learning (ML) has paved the way for scientists to develop effective computer-aided diagnostic (CAD) systems. In recent years, epileptic seizure detection using Electroencephalogram (EEG) data and deep learning models has gained much attention. However, in deep learning networks, the bottleneck is a large number of learnable parameters.MethodIn this study, a novel approach comprising a 1D-Convolutional Neural Network (CNN) model for feature extraction followed by classical-quantum hybrid layers for classification purpose has been proposed. The proposed technique has only 745 learning parameters, which is the least reported to date.ResultThe proposed method has achieved a maximum accuracy, sensitivity, and specificity of 100% for binary classification on the Bonn EEG dataset. In addition, the noise robustness of the proposed model has also been checked. To the best of the author’s knowledge, this is the first study to employ quantum machine learning (QML) to detect epileptic seizures.ConclusionThus, the developed hybrid system will help neurologists to detect seizures in online mode.

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“…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].…”

Section: Introductionmentioning

confidence: 99%

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

Akpinar,

Oduncuoglu

2024

Preprint

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Gliomas, which are the most common malignant primary brain tumors, present significant challenges in terms of varying survival rates, treatment modalities, and prognostic processes between patients with low-grade gliomas (LGGs) and high-grade gliomas (HGGs). Accurate classification and grading of LGGs and HGGs are crucial for appropriate treatment planning and assessment of overall prognosis. The classification and grading of gliomas have undergone evolution over time, and the inclusion of molecular markers in the classification of glioma tumors by the WHO Central Nervous System (CNS) Tumors Classification in 2016 and the incorporation of advanced molecular diagnostics in 2021 have improved glioma tumor characterization. However, the high cost and limited accessibility of molecular genetic tests, coupled with the time-consuming nature of obtaining results, can lead to delays in critical treatment decisions. To address these challenges, various classical artificial intelligence methods, such as machine learning and deep learning, have been applied to problems in this field, yielding a certain level of success. Nevertheless, the continuous expansion of medical data dimensions, the inherent noise level in the data, and the limitations of the classical vector space pose significant challenges that classical artificial intelligence methods struggle to overcome. Recent studies have demonstrated that the use of quantum computing and quantum artificial intelligence technologies in healthcare not only addresses these problems but also accelerates complex data analyses and processes large datasets more efficiently. This paper presents a novel hybrid quantum (or classical-quantum) computing model aimed at differentiating between LGGs and HGGs using data from The Cancer Genome Atlas (TCGA). To the best of our knowledge, this study is the first to investigate the classification of LGGs and HGGs using a hybrid classical-quantum framework within the TCGA dataset. In the classical part of the study, an ensemble feature selection method was used to identify the most important molecular markers and clinical features within the TCGA glioma dataset. In the quantum section, six variational quantum classifier (VQC) models with different hyperparameters are proposed. These classifiers are subsequently used to differentiate between LGGs and HGGs using the features obtained from the ensemble model. The computational results show that among the six VQC models, the VQC-1 model, which incorporates Rx and CX gates in the feature map and Ry, Rz and CY gates in the parameterized quantum circuit and utilizes the AQCD optimization method, achieves the highest classification accuracy of 0.74. This study provides a novel perspective on the classification of glioma tumors by combining classical and quantum computing methods.

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Breast Cancer Detection Using Quantum Convolutional Neural Networks: A Demonstration on a Quantum Computer

Cited by 14 publications

References 19 publications

Beyond Bits: A Review of Quantum Embedding Techniques for Efficient Information Processing

Beyond Bits: A Review of Quantum Embedding Techniques for Efficient Information Processing

A Novel Hybrid Classical- Quantum Network to Detect Epileptic Seizures

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

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