Big Data in Healthcare

Quantum mechanics and big data analysis

Big data sets can be staggering in size. Therefore, its analysis remains daunting even with the most powerful modern computers. For most of the analysis, the bottleneck lies in the computer's ability to access its memory and not in the processor. The capacity, bandwidth or latency requirements of memory hierarchy outweigh the computational requirements so much that supercomputers are increasingly used for big data analysis. An additional solution is the application of quantum approach for big data analysis.

Quantum computing and its advantages

The common digital computing uses binary digits to code for the data whereas quantum computation uses quantum bits or qubits. A qubit is a quantum version of the classical binary bits that can represent a zero, a one, or any linear combination of states (called superpositions) of those two qubit states. Therefore, qubits allow computer bits to operate in three states compared to two states in the classical computation. This allows quantum computers to work thousands of times faster than regular computers. For example, a conventional analysis of a dataset with n points would require 2ⁿ processing units whereas it would require just n quantum bits using a quantum computer. Quantum computers use quantum mechanical phenomena like superposition and quantum entanglement to perform computations .

Quantum algorithms can speed-up the big data analysis exponentially. Some complex problems, believed to be unsolvable using conventional computing, can be solved by quantum approaches. For example, the current encryption techniques such as RSA, public-key (PK) and Data Encryption Standard (DES) which are thought to be impassable now would be irrelevant in future because quantum computers will quickly get through them. Quantum approaches can dramatically reduce the information required for big data analysis. For example, quantum theory can maximize the distinguishability between a multilayer network using a minimum number of layers. In addition, quantum approaches require a relatively small dataset to obtain a maximally sensitive data analysis compared to the conventional (machine-learning) techniques. Therefore, quantum approaches can drastically reduce the amount of computational power required to analyze big data. Even though, quantum computing is still in its infancy and presents many open challenges, it is being implemented for healthcare data.

Applications in big data analysis

Quantum computing is picking up and seems to be a potential solution for big data analysis. For example, identification of rare events, such as the production of Higgs bosons at the Large Hadron Collider (LHC) can now be performed using quantum approaches. At LHC, huge amounts of collision data (1PB/s) is generated that needs to be filtered and analyzed. One such approach, the quantum annealing for ML (QAML) that implements a combination of ML and quantum computing with a programmable quantum annealer, helps reduce human intervention and increase the accuracy of assessing particle-collision data. In another example, the quantum support vector machine was implemented for both training and classification stages to classify new data. Such quantum approaches could find applications in many areas of science. Indeed, recurrent quantum neural network (RQNN) was implemented to increase signal separability in electroencephalogram (EEG) signals. Similarly, quantum annealing was applied to intensity modulated radiotherapy (IMRT) beamlet intensity optimization. Similarly, there exist more applications of quantum approaches regarding healthcare e.g. quantum sensors and quantum microscopes.