Quantum Computation for Big Information Processing

INTRODUCTION

Big data (BD), a topic of hot discussion embodying almost all circles, arrived with a fervor that parallels some of the most significant movements in the history of computing. Data nowadays is produced at an exponentially growing rate, which puts a big challenge for the analysis of this mighty resource and limits possibilities for knowledge to be gained. Digital media devices have already been more than

92% in 2002 [1], while the size of this new data was also more than 5 exabytes (5 x 10¹⁸ bytes). It puts a huge contest to analyze such gigantic scale data even though computer systems today are much faster than those in the 1930s. Every day 2.5 exabytes of data is produced in the world with the bang in digital technologies, making it equivalent to the memory of 5 million laptops or 150 million phones. Quite a few efficient methods such as sampling, data condensation, density-based approaches, grid-based approaches, divide and conquer, incremental learning, and distributed computing, have been presented in response to the problems of analyzing large-scale data [2]. Of course, these methods are constantly used to improve the performance of the operators in analytics process, the results of these methods illustrate that w'ith the efficient methods at hand, we may be able to analyze the large-scale data in a reasonable time. Principal component analysis (PCA) [3], a dimensional reduction method, is a typical example that is aimed to accelerate the process of data analytics by reducing the input data volume. Sampling [4], which can also be used to speed up the computation time of data analytics, is another reduction method that reduces the data computations of data clustering. Following Moore’s law for several decades, although the advances of computer systems and internet technologies have witnessed the development of computing hardware, the problems of handling large-scale data still exist when we are entering the age of big data. An intelligent way of processing, organizing, and extracting hidden value from gigantic data piles like big data is desired since complexity, size of data sets upswings balloons year after year. Quantum computing is the answer. Big data and machine learning have strapped the limits of current information technology (IT) infrastructure for handling large troves of datasets effectually which gave birth to the fresh and exciting archetype of quantum computing (QC) that has the power to dramatically upsurge the speed [5]. Quantum computing permits for quick detection, analysis, integration, extensive search and diagnosis from bulky sprinkled data sets to quickly uncover patterns. Quantum computers wall be able to complete complex calculations in mere seconds—same calculations that would take today’s computers thousands of years to solve [6].

Encouraging developments in the quantum computing direction have already started. Open quantum assembly language (QASM) and software development kits (SDKs) like quantum information science kit (QISKit) are fresh languages that open new horizons for the research and development in the new' paradigm of quantum computing. Open Fermion, an open-source quantum computing platform, was launched by Google in October 2017. It may bring revolutions in the dimensionality reduction of input and output data of the Harrow-Hassidim-Lloyd (HHL: a quantum algorithm for linear systems of equations) algorithm [7]. The restricted computing power of prevailing computers has encountered bottlenecks in the application of quantum computing in quantum simulation machine learning and the number and quality of data processed are faced with many problems. The quantum computing and quantum algorithms emergence may become an effective means for disruption of these bottlenecks. Also, quantum principal component analysis (PCA) is more efficient than traditional PCA exponential. A good wealth of quantum computing algorithms could be found from the literature like Grover’s algorithm [7], The Deutsch-Jozsa algorithm [8-10], Shor’s algorithm [11], Simon’s algorithm [12], Quantum Fourier transform (QFT) [13]. Ying et al. [14] discuss some potential meeting points between quantum computing and artificial intelligence (AI) as:

• • Quantum algorithms for learning (QAL)
• • Quantum algorithms for decision-making (QAD)
• • Quantum search (QS)
• • Quantum game theory (QGT)
• • Semantic analysis (SA)
• • Natural language (NL)
• • Quantum Bayesian networks (QBN)
• • Quantum neural networks (QNN)
• • Quantum genetic algorithms (QGA)

WHAT DOES THAT MEAN FOR BIG DATA?

The progress in these fields critically relies on processing power. The computational requirement of big data analytics (BDA) is currently placing a considerable strain on computer systems. Since 2005, the focus has been shifted to parallelism using multiple cores instead of a single fast processor. However, many problems in big data cannot be solved simply by using more and more cores. Splitting up the work among multiple processors is used but its implementation is complex. The problems need to be solved sequentially where the preceding step is equally important. At the Large Hadron Collider (LHC) in the European Council for Nuclear Research (CERN), Geneva, particles are accelerated, traveling at almost the speed of light within a 27 km ring such that 600 million collisions take place in a second wherein only one of the 1 million collisions is chosen for pre-selection. In the pre-selection process, only 1 out of 10,000 events are passed to a grid of processor cores that further choose 1 out of 100 possible events, hence, making the data process at 10 GB/s. At LHC, 5 trillion bits of data are captured every second and after discarding 99% of the data, it still analyzes 25 petabytes of data a year.

Such is the power of quantum computing but the current resources lack the potential for its possible application in big data. If it were possible, the computing would be useful for specific tasks such as factoring large numbers that are useful in cryptography, weather forecasting, searching through large unstructured data sets in a fraction of the time, identification of patterns and anomalies, etc. The developments in quantum computing could actually make encryption obsolete in a jiffy. With such computing powers, it would be one day possible to make large datasets that would probably store complete information such as the genetics of every single human that existed, and machine learning algorithms could find patterns in the characteristics of these humans while also protecting the identities of the humans. Also, clustering and classification of data would become a much faster task. The future says quantum computers will allow faster analysis and integration of our enormous data sets, which will improve and transform our machine learning and artificial intelligence capabilities.

Quantum computing seems to have exceptional promise in big data analytics in the future. Fruitful work in the recent few years has been carried out on applications of quantum computing on big data analytics. The present work may be considered as an initiative in the same direction. The main contribution of the paper is to present the study in the area of quantum computing for big data analytics focusing mainly on the healthcare sector. Also, we present a model explaining the way out of how quantum computing can tackle the complex issues presented by big data w'hich will become a platform for further research in the same challenging field.