Artificial intelligence and machine learning paves the way to achieve greater technical feats. In this endeavor to hone these techniques, quantum machine learning is budding to serve as an important tool. Using the techniques of deep learning and supervised learning in the quantum framework, we are able to propose a quantum neural network and showcase its implementation. We consider the application of cancer detection to demonstrate the working of our quantum neural network. Our focus is to train the network of ten qubits in a way so that it can learn the label of the given data set and optimize the circuit parameters to obtain the minimum error. Thus, through the use of many algorithms, we are able to give an idea of how a quantum neural network can function.
Artificial intelligence and machine learning paves the way to achieve greater technical feats. In this endeavor to hone these techniques, quantum machine learning is budding to serve as an important tool. Using the techniques of deep learning and supervised learning in the quantum framework, we are able to propose a quantum neural network and showcase its implementation. We consider the application of cancer detection to demonstrate the working of our quantum neural network. Our focus is to train the network of ten qubits in a way so that it can learn the label of the given data set and optimize the circuit parameters to obtain the minimum error. Thus, through the use of many algorithms, we are able to give an idea of how a quantum neural network can function.
Deep learning have paved the way for scientists to achieve great technical feats. In an endeavor to hone and perfect these techniques, quantum deep learning is a promising and important tool to utilize to the fullest. Using the techniques of deep learning and supervised learning in a quantum framework, we are able to propose a quantum convolutional neural network and showcase its implementation. Using the techniques of deep learning and supervised learning in a quantum framework, we are able to propose a quantum convolutional neural network and showcase its implementation. We keep our focus on training of the ten qubits system in a way so that it can learn from labeling of the breast cell data of Wisconsin breast cancer database and optimize the circuit parameters to obtain the minimum error. Through our study, we also have showcased that quantum convolutional neural network can outperform its classical counterpart not only in terms of accuracy but also in the aspect of better time complexity.
Experts predict that the use of artificial intelligence and quantum computing will transform medicine including medical imaging. One of the common malignant tumors in women and seriously threatens women’s physical and mental health is breast cancer. The high incidence and mortality of breast cancer are seriously threatening women’s physical and mental health. The long time it took to get breast cancer’s test result and the conditions which make delay without being treated which caused the loss of lives due to latency. Early screening for breast cancer via mammography, ultrasound (US) and magnetic resonance imaging (MRI) can significantly improve the prognosis of patients. Artificial intelligence has been extensively researched for breast cancer screening and has demonstrated good performance in image identification tests. This research was introduced analytical estimation for quantum Convolutional Neural Network for breast cancer detection, such as in the identification, segmentation and classification of lesions, breast density assessment and breast cancer risk assessment. Objectives will be to: To detect the breast cancer on a quantum convolutional neural network algorithm using image recognition, to create and develop a CNN and QCNN algorithm that will detect breast Cancer using Deep Learning and to early predict pathological complete response to neoadjuvant chemotherapy and survival analysis using Deep Learning. Data was analyzed through the use of descriptive and deep learning models like CNN and QCNN models. This research used also Google Collab to analyze the data and used data analysis tools python as programming language. From the study results, we have demonstrated the efficacy of quantum convolutional neural networks (QCNNs) to detect breast cancer cells. Using the techniques of deep learning and supervised learning in the quantum framework, we have proposed and tested the effectiveness of quantum CNN on the quantum simulator available at the IBM quantum experience platform.
A promising combination of artificial intelligence and quantum computing, quantum neural networks (QNNs) have the potential to completely transform the way neural networks interpret and learn from data. QNNs can outperform classical neural networks in terms of speed, efficiency, and scalability by utilizing the special qualities of quantum physics, such as superposition, entanglement, and quantum parallelism. The basic ideas of QNNs, their structures, and the ways in which quantum algorithms can be incorporated into machine learning processes to improve problem-solving skills are all examined in this study. current developments in QNNs, emphasizing how they can be used for challenging applications including pattern recognition, optimization, and high-dimensional data space decision-making. We also go over the difficulties in creating scalable QNN models and the possible future applications of QNNs in artificial intelligence. By utilizing the potential of quantum computing, QNNs have the potential to advance artificial intelligence by providing answers to issues that are now intractable through traditional methods.
Abstract Quantum machine learning is gaining attention for its ability to uncover complex patterns that classical models may overlook. In this research, we explore the use of a quantum neural network (QNN) for classifying prostate cancer based on clinical data. We started with a real-world dataset and applied essential preprocessing steps to prepare it for training. The QNN was built using a ZZFeatureMap for encoding and a variational circuit combining rotation gates and entanglement. Training was performed with the COBYLA optimizer using an 80–20 train-test split. The model achieved strong results, with 87.5% accuracy, 89% precision, and over 90% recall. These outcomes suggest that QNNs offer meaningful potential for improving cancer detection tasks, particularly in medical domains where capturing subtle feature interactions is crucial.
One of the most promising new directions in quantum computing and artificial intelligence convergence is the Quantum Neural Networks (QNNs) that can potentially revolutionize the manner in which intelligent systems are designed, trained, and implemented. Although traditional deep learning models have proven to be incredibly successful in fields like computer vision, natural language processing and autonomous systems, they are becoming limited by computational bottlenecks, energy use, and scalability constraints when operating on large high-dimensional data. The distinctive principles of quantum mechanics (superposition, entanglement, and quantum parallelism) may be applied to the QNNs to provide a distinct computational paradigm with the ability to execute exponentially complicated computations more effectively than his classical counterparts. This article gives an in-depth analysis of QNNs as the new frontier of artificial intelligence. We start with the description of the theoretical foundations of quantum computation and their realization in the form of neural networks. We then examine state-of-the-art QNN architectures, such as hybrid quantum-classical models that help overcome present quantum hardware limitations in relation to the capabilities of classical machine learning. Some of the upcoming applications are showcased including molecular simulation and drug discovery and financial modeling, optimization, cryptography and intelligent automation. Such applications reveal the revolutionary power of QNNs to tackle the issues that cannot be tackled by traditional systems. Nonetheless, there are also major obstacles on the way to a practical implementation of QNN. Problems that still remain a barrier to widespread adoption include quantum decoherence, noise in near-term quantum devices, and the inability to scale qubit architectures or to train large-scale quantum models. In order to overcome these obstacles, we speak about the current research work on the topic of error correction, variational algorithms, and hybrid system designs. Also we examine the ethical and societal ramifications of quantum-accelerated AI, such as security, data sovereignty, and technological equity globally.
Early diagnosis of esophageal cancer is critical for increasing the chances of successful treatment and reducing mortality rates. Traditional machine learning models for diagnosing esophageal lesions use previously collected data on specific local features, but these methods can be slow and computationally intensive. As a result, researchers are exploring alternative training platforms for deep learning models, including quantum computers. These computers can take advantage of the properties of quantum entanglement and superposition to solve complex problems.In this study, we propose a Hybrid Quantum-Classical Neural Network using deep learning and supervised learning techniques in a quantum framework. Our focus is on classifying esophageal cancer to demonstrate the network’s capabilities. We trained a network of two qubits to learn the label of a given dataset and optimize circuit parameters to minimize error. The proposed Hybrid Quantum-Classical Neural Network showed high accuracy in classifying esophageal cancer.