Abstract
The need for IR4 technologies with faster and accurate results for huge health datasets is required. Healthcare has made a lot of advancements with technologies and has certain issues in terms of carbon emission. This in turn sets the patient's life in risk. The study proposes a model for all the healthcare sectors. The study deals with the various quantum neural networks implemented on diabetes retinopathy and COVID-19 images. The quantum computing model outperformed all other machine learning and deep learning models giving more accurate results in less time. The revolution of quantum computing has proven the healthcare in diabetes retinopathy and COVID-19 detection to be faster. It can henceforth be implemented for early warning of the diseases and provide treatment for the patients.
TopIntroduction
The key area in which quantum computing helps optimizing pricing is risk analysis in health care. In this sector of health, triple aim is developed for system performance. It aims at the goal to advance the patient care experience, improve the population health, and minimize per capita health care costs. The approach with 3D in single aim, this model has worked great in guiding the optimization of health systems — improved clinical experience — leading to the creation of the quadruple aim model.
The idea is to achieve better outcome, improved clinical experience, improved patient experience and lower costs. But the authors proposed new model (pentacle aim) which has five goals shown in figure 1 with a zero carbon emission with addition. Since the healthcare generates largest carbon emission. Every mention of health care refers to patient diagnosis but the carbon emitted from this sector pollutes the other area of living, making worst for survival.
TopFew Use Cases - Quantum Computing - Healthcare
Healthcare centers obtain lot of benefits from the business and scientific activity in the era of QC. This powerful technology helps to create innovation with next-generation technologies, organization need to take certain steps before implementing QC.
Every organization identifies, enables, and hires quant champions in its organization—including both technology and healthcare professionals. They serve as focal point to connect quant expertise with healthcare requirements.
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Explore and prioritize.
Explore potential QC use cases and prioritize the ones with the largest impact for the organization.
Implement the applications of QC and experiments with real quant computers. This allows driving toward quant advantage and provides the employees with hands-on enablement. It is accelerated by combining a quant ecosystem, a group of industry and technology participants sharing risks and rewards in quant computing (Flöther et al., 2020).
TopQuantum Computing Powered By Artificial Intelligence
Quantum computing has enabled and challenged the human intelligence in determining drugs and remedies against diseases which have no medication. The accuracy and precision of medicines for AIDS and Cancer were found to be near future. Till now, there is no medication for few common ailments, but now the diagnosis may be possible by QC powered artificial intelligence based simulations and drug research processes. Hence, QC powered AI can prove to be a boon for researchers across the world. The positive point is the newer potential of drug reactions and future risks would be declined with the help of Quantum Computing Powered Artificial Intelligence Research Computers (QACPAIRC) (Mehta et al., 2019).
One of the major fertile areas for quantum computing (QC) is AI that relies on processing huge amounts of complex datasets (Taulli, 2020). A large scale quant computer can be built using a controllable quant system, provided the physical system meets the following requirements, called as the DiVincenzo criteria (Sahni & Srivastava, 2015). QC promises to deliver quick analysis and integration of huge data sets that improves and transforms the capabilities of machine learning and artificial intelligence (Marr, n.d.).
Key Terms in this Chapter
Quanvolutional Neural Network: Quanvolutional layers operate on input data by locally transforming the data using a number of random quantum circuits, in a way that is similar to the transformations performed by random convolutional filter layers.
Quantum Computing: Quantum computing is an area of computing focused on developing computer technology based on the principles of quantum theory. It uses quantum bits or qubits.
TensorFlow Quantum: TensorFlow Quantum (TFQ) is a Python framework for quantum machine learning.
Diabetic retinopathy: It is an eye condition that can cause vision loss and blindness in people who have diabetes.
Net Carbon Zero Emissions: It refers to achieving an overall balance between greenhouse gas emissions produced and greenhouse gas emissions taken out of the atmosphere.
Hybrid CNN: It utilizes a Deep Neural Network (DNN) to effectively memorize global features by one-dimensional (1D) data and utilizes a CNN to generalize local features by two-dimensional (2D) data.
Quantum Machine Learning: Quantum machine learning (QML) is built on two concepts: quantum data and hybrid quantum-classical models.
Quantum Circuit: In quantum information theory, a quantum circuit is a model for quantum computation, similar to classical circuits, in which a computation is a sequence of quantum gates, measurements, initializations of qubits to known values, and possibly other actions.
COVID-19: Coronavirus disease ( COVID -19) is an infectious disease caused by the SARS-CoV-2 virus.
Healthcare: It is the various services for the prevention or treatment of illness and injuries. Nobody wants to pay more for health care.