Abstract Big data in the coming days is going to become an integral part of life existing on this planet. Hence, introducing the early stage data analytics with the European Organization for Nuclear Research (CERN) and the National Center for Biotechnology Information (NCBI) would generate interest in the research community for the rapidly changing world of technology. This study is especially intended to integrate two organizations data for understanding the quantum realm computations. Generally, Quantum Computation (QC) creates the notion in the brain as Qu-bits. But here, QCs are the principles followed by the particles, atoms, molecules, and systems. Few of them are Pauli’s exclusion principle, superposition and entanglement. These principles played a vital role in the construction of proton to an atom and to higher systems. In this present work, we have collected homo-sapiens’ chromosomes data from NCBI and integrated it with CERN data to understand quantum computations. We have identified a couple of computations and communications taking place inside the genes by integrating the recent scientific results obtained at CERN. We have proposed a novel code-named peacock code (PCOC) and designed a prototype to develop a unique identity with blockchain technology. A typical correlation between CERN and NCBI datasets are integrated into this study statistically with R programming tool to understand supersymmetry and develop future computations with natural principles.
Abstract Big data in the coming days is going to become an integral part of life existing on this planet. Hence, introducing the early stage data analytics with the European Organization for Nuclear Research (CERN) and the National Center for Biotechnology Information (NCBI) would generate interest in the research community for the rapidly changing world of technology. This study is especially intended to integrate two organizations data for understanding the quantum realm computations. Generally, Quantum Computation (QC) creates the notion in the brain as Qu-bits. But here, QCs are the principles followed by the particles, atoms, molecules, and systems. Few of them are Pauli’s exclusion principle, superposition and entanglement. These principles played a vital role in the construction of proton to an atom and to higher systems. In this present work, we have collected homo-sapiens’ chromosomes data from NCBI and integrated it with CERN data to understand quantum computations. We have identified a couple of computations and communications taking place inside the genes by integrating the recent scientific results obtained at CERN. We have proposed a novel code-named peacock code (PCOC) and designed a prototype to develop a unique identity with blockchain technology. A typical correlation between CERN and NCBI datasets are integrated into this study statistically with R programming tool to understand supersymmetry and develop future computations with natural principles.
A study published on bioRxiv demonstrates the first hybrid quantum computing framework combining classical Hamming distance filtering with the Grover quantum search algorithm to overcome bottlenecks in massive genomic data analysis. Utilizing the IBM Qiskit 27-qubit simulator, researchers rapidly and flawlessly identified a 50-nucleotide target sequence of a Cas9-like nuclease within a Brazilian biome metagenome database, even under conditions allowing up to a 30% mismatch. The study lays a revolutionary foundation for synthetic genome design by completing simulations that precisely insert a "BioBloQu" (quantum biological block)—composed of a promoter, an RBS, an enzyme, and a terminator—into the explored scar regions of the minimal genome M. mycoides JCVI-Syn3B. [Quantum Biology Society] Modern life sciences are pouring out genomic sequencing data at an exponential rate. However, due to the immense complexity of biological data, existing classical computing methods are facing severe computational bottlenecks in analyzing and manipulating it. To break through these limitations, a disruptive study recently published on the preprint repository bioRxiv, titled "Genetic Engineering with Quantum Circuits: creating codes and studying BioBloQu genetic elements," has brought quantum computers to the forefront of genetic engineering. A joint research team led by Professor Elibio Rech from the Brazilian Agricultural Research Corporation (Embrapa) Genetic Resources and Biotechnology and the Federal University of Rio Grande do Sul (UFRGS) presented this innovative research. ■ Scanning Massive Genomic Databases with Qubits Using the core quantum mechanical principles of superposition and entanglement as the foundation for information processing, the research team developed a hybrid quantum framework that combines classical Hamming distance filtering with the Grover quantum search algorithm. Powered by IBM's Qiskit 27-qubit simulator, this algorithm was used to search for a 50-nucleotide target sequence of a Cas9-like nuclease within a Brazilian biome metagenome database. As a result, the team successfully and swiftly identified the massive genetic data through amplitude amplification, filtering out the sequence perfectly even under conditions allowing up to a 30% mismatch rate. This proves that vast amounts of genetic data, which are unmanageable for classical computers, can be analyzed in a flash through quantum parallel processing. ■ The Era of Synthetic Genome Design Opened by BioBloQu Furthermore, the researchers successfully completed a quantum circuit simulation that accurately inserts a synthetic genetic construct called "BioBloQu" (quantum biological block) into the identified target regions. In the genome of M. mycoides JCVI-Syn3B, an artificially synthesized minimal genome model, the quantum algorithm first identified two 20-nucleotide "scar" regions—which are traces of gene editing. Then, it precisely integrated a tandem genetic block (BioBloQu) composed of a promoter, a ribosome binding site (RBS), an enzyme sequence, and a terminator into that location. This innovative approach goes beyond simply cutting and pasting existing genes physicochemically; it opens up the possibility of designing and assembling novel synthetic genomes from the ground up under the control of quantum algorithms equipped with overwhelming computational power. By directly applying the computational power of quantum mechanics to biotechnology, this research is expected to serve as the starting point for a massive revolution in next-generation quantum-bio data manipulation, customized gene therapy, and synthetic biology. #QuantumComputing #GeneticRecombination #BioBloQu #QuantumAlgorithm #GroverAlgorithm #Metagenome #SyntheticBiology #GenomeDesign #QuantumBiology #KoreanQuantumBiologySociety https://www.biorxiv.org/content/10.1101/2025.05.02.651535v2
Classical computing works by processing bits, or 0s and 1s representing electrical signals of on and off. Quantum computing employs a very different technique for information processing. It uses qubits, which can exist as both a 1 and 0 at the same time, and uses the properties of subatomic particles in quantum physics such as interference, entanglement, and superposition to extend computational capabilities to hitherto unprecedented levels. The efficacy of quantum computing for important verticals such as healthcare where quantum computing can enable important breakthroughs in the development of life-saving drugs, performing quick DNA sequencing, detecting diseases in early stages, and performing other compute-intensive healthcare related tasks is not yet fully explored. Furthermore, implementations of quantum computing for healthcare scenarios such as these have their own unique set of requirements. Unfortunately, existing literature that address all of these dimensions is largely unstructured. This research is intended to be the first systematic analysis of the capabilities of quantum computing in enhancing healthcare systems. This article is structured with the help of taxonomies developed from existing literature to provide a panoramic view of the background and enabling technologies, applications, requirements, architectures, security and open issues, and future research directions. We believe the paper will aid both new and experienced researchers working in both quantum computing and the healthcare domains in visualizing the diversity in current research, in better understanding both pitfalls and opportunities, and coming up with informed decisions when designing new architectures and applications for quantum computing in healthcare.
Classical computing works by processing bits, or 0s and 1s representing electrical signals of on and off. Quantum computing employs a very different technique for information processing. It uses qubits, which can exist as both a 1 and 0 at the same time, and uses the properties of subatomic particles in quantum physics such as interference, entanglement, and superposition to extend computational capabilities to hitherto unprecedented levels. The efficacy of quantum computing for important verticals such as healthcare where quantum computing can enable important breakthroughs in the development of life-saving drugs, performing quick DNA sequencing, detecting diseases in early stages, and performing other compute-intensive healthcare related tasks is not yet fully explored. Furthermore, implementations of quantum computing for healthcare scenarios such as these have their own unique set of requirements. Unfortunately, existing literature that address all of these dimensions is largely unstructured. This research is intended to be the first systematic analysis of the capabilities of quantum computing in enhancing healthcare systems. This article is structured with the help of taxonomies developed from existing literature to provide a panoramic view of the background and enabling technologies, applications, requirements, architectures, security and open issues, and future research directions. We believe the paper will aid both new and experienced researchers working in both quantum computing and the healthcare domains in visualizing the diversity in current research, in better understanding both pitfalls and opportunities, and coming up with informed decisions when designing new architectures and applications for quantum computing in healthcare.
This study aims to leverage the advanced capabilities of quantum computing to construct an efficient framework for processing large-scale health data, uncover potential higher-order correlations in medicine, and enhance the accuracy of smart healthcare diagnosis and treatment. A data processing framework is developed using quantum annealing algorithms and quantum circuits. We call it the quantum medical data simulation computational model (Q-MDSC). A unique encoding method based on quantum bits is employed for health data features, such as encoding symptom information from electronic health records into different quantum bits and representing different alleles of genetic data through superposition states of quantum bits. The properties of quantum entanglement are utilized to relate different data types, and quantum parallelism is harnessed to process multiple data combinations simultaneously. Additionally, this quantum computing framework is compared with traditional data mining methods using the same datasets, which include the Cochrane Systematic Review Database (https://www.cochranelibrary.com), the BioASQ Dataset (https://participants-area.bioasq.org), the PubMed Central Dataset (https://www.ncbi.nlm.nih.gov/pmc), and the Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov). The datasets are divided into training and testing sets in a 7:3 ratio during the experiments. Tests are conducted on association mining tasks of varying data scales and complexities, ranging from simple symptom-disease associations to complex gene-symptom-disease higher-order associations. The results indicate that, when processing large-scale data, the quantum computing framework improves overall computational speed by approximately 45% compared to traditional algorithms. Regarding uncovering higher-order correlations, the quantum computing framework enhances accuracy by about 30% relative to traditional algorithms. For early disease prediction, the accuracy achieved with the new framework is approximately 25% higher than that of conventional methods. Furthermore, for personalized treatment plan matching, the matching accuracy of the quantum computing framework surpasses traditional approaches by about 35%. These findings demonstrate the significant potential of the quantum computing-based smart healthcare framework for processing large-scale health data in the context of higher-order correlation mining, paving new pathways for the development of smart healthcare. This study utilizes multiple public datasets to achieve breakthroughs in computational speed, higher-order correlation mining, early disease prediction, and personalized treatment plan matching, thus opening new avenues for advancing smart healthcare.
We live in the data age. By big data, we mean gigantic and sophisticated data that are difficult to store and analyze using conventional computers. In this modern world, everything boils down to playing around with data. Data is produced from everything we know and from every activity we perform. It is generated from every gadget we use. Big data is vital as it makes it possible for organizations to accumulate, save, and handle colossal quantities of data at the appropriate rate and pulse in order to extract insights. Cloud platforms play a crucial role in big data analysis by providing scalable repositories, powerful computing resources, and affordable solutions for handling this enormous data. Business Intelligence, identifying trends, inferring, and predicting heavily rely on big data analytics. Nowadays, many big data analytic tools are available. However, there are still issues that cannot be solved by even the most refined conventional methods in a stipulated time. The idea of quantum computing was created to offer contemporary ordinary computers access to huge processing power. As quantum computing technology progresses rapidly, it provides a groundbreaking opportunity to enhance cloud computing platforms by carrying out these enormous workloads that classical systems cannot handle. Quantum Cloud Computing is a developing model of computing that enables quantum applications and helps users to operate on quantum computers without being restricted to a physical environment. Some findings related to the concepts of cloud computing, quantum computing, etc., are presented.
In this research, we offer an unprecedented opportunity of combination of quantum computing and genomic data analysis with the aim of though improvements in disease susceptibility prediction. Due to the limitations of traditional computational approaches when processing the data volume and complexity in dealing with genomic data, the given model benefits from the use of quantum feature encoding techniques, including angle and amplitude encoding, and Variational Quantum Classifier (VQC) element to present superior classification results. The raw data was normalized into genomic data, then the data was dimensionally reduced with PCA and encoded to a quantum circuit, enabling the VQC to perform more accurately, sensitively and efficiently than classical model of machine learning. The findings showed that the quantum model had better predictive power in addition to the fact that it took shorter time to train through its parallel nature of quantum operations. The multi-stage integration is an important step towards scalable, real-time and personalized healthcare support in the framework of genomic-based diagnostics.