Quantum Physics and Big Data: The Future of Data Science?

Data analysis by high-powered computers, secure transfers through quantum entanglement… discover the different ways in which quantum physics will transform Big Data and artificial intelligence over the coming years.

Today’s computers allow for the analysis of massive data sets. However, this process is slow, expensive, energy-intensive… and limited by storage capacity.

Technological innovation has been severely hampered. In the near future, quantum physics could help overcome these obstacles…

Quantum Physics and Machine Learning

Physics Professor Rupak Chatterjee of the Stevens Institute of Technology is developing quantum systems for Big Dataand more specifically for Machine Learning algorithms.

As he points out, to speed up Big Data analysis, it is not enough to use more hardware. For example, two PCs will not necessarily be twice as fast only one.

On the other hand, quantum computers, based on quantum physics rather than classical physics, provide several advantages over a traditional computer. They are faster, offer more storage capacity, and can handle highly complex problems.

This is because the particles underlying the quantum system, such as photons and electrons, are atomically small. They therefore allow us to store more data in a limited physical space.

By the way, the principle of superposition specific to quantum physics allows a basic unit of information to exist in several states at the same time. A quantum computer can therefore host and represent larger volumes of data simultaneously. Rather than storing the data bits in a single state, in binary form (1 or 0), the quantum system can maintain both states simultaneously.

Both states can then be manipulated simultaneously using the concept of “quantum parallelism”.. They can be calculated and analysed at the same time, in parallel, which speeds up the process enormously.

I mean, come on, the quantum entanglement phenomenon makes the state of a particle or bit of data inextricably linked to other things it interacts with. This phenomenon increases the complexity of calculations, and makes it possible to discover relationships in highly complex structures.

That’s why quantum computers can be exploited to solve problems too complex for a conventional computer. Quantum algorithms are used to accelerate Machine Learning, and more specifically pattern discovery in complex data sets.

In collaboration with Ting Yu, another physics professor, Chatterjee developed a quantum approach to support vector machine algorithms using quantum optics: the interaction between photos of light and matter.

A support vector machine type algorithm is a category of supervised learning model to classify the entered data into two pre-existing choices. For each new piece of information, the model tries to predict how to classify it based on the knowledge gained from classifying the previous ones.

These algorithms allow to separate data based on certain characteristics into binary categories. For example, is it going to rain? Yes, or no. However, by integrating this data into a photonic quantum system, more complex calculations can be performed more quickly.

In another project, Rupak Chatterjee used superconducting processors to develop and test K-quantum mean clustering algorithms. A clustering algorithm allows data to be grouped according to their common characteristics rather than being classified into binary categories.

This type of algorithm is very useful for predict the answers to an open-ended questionon an unsupervised learning model. It can also be used for client segmentation or the identification and prediction of regional phenomena such as crime or the spread of disease.

By replacing the traditional Euclidean distance formula with a quantum distance formulaChaterjee was able to develop an algorithm that delivers faster results with increased accuracy for the cluster data.

Rupak Chatterjee’s experiences show that the revolutionary potential of quantum physics for Machine Learning. The results of these two projects were published in the journal Quantum Information & Computation.

Quantum physics and megadata

Humanity is generating more and more data. These data are increasingly complexThese are diverse, and it is currently impossible to process, manage and understand them all.

Machine Learning systems are a valuable aid in analyzing information…but they’re not enough. The most powerful analytical systems are based on topology: the branch of mathematics that studies the properties of geometric objects preserved by deformation, such as an elastic that can be stretched without breaking it.

Topological systems are particularly useful for analyzing connections in complex networks, such as the internal wiring of the brain or the global interconnections of the Internet. Unfortunately, even the most powerful supercomputers cannot solve the most complex problems.

However, researchers at MIT, the University of Waterloo and the University of Southern California have developed a new approach based on physics and quantum computing. The fruits of their work were published in the journal Nature Communication.

Algebraic topology is at the heart of this new approach. Its objective is to reduce the impact of unavoidable distortions occurring when someone collects data about the real world.

In a topological description, the basic characteristics of the data are considered to be identical, regardless of how stretched, compressed or distorted they are. These basic topological attributes are important in attempting to reconstruct the underlying patterns of the real world that the data is supposed to represent.

This approach is appropriate for any type of data set, but it is requires too many resources for conventional computers. This is where quantum mechanics makes a difference.

Using the example of a 300-point data set, the analysis of all topological features would require a computer the size of the universe. This would require 2 power 300 processing units (roughly the number of particles in the universe).

Now, with a quantum computer, 300 quantum bits are sufficient. Such a machine could come into being in the next few years.

This approach could prove useful in many situations. For example, it could be used to understand the interconnections of the brainby applying it to data sets from electroencephalograms or MRI scans. This would reveal the connections between neurons.

It could also be applied to the global economy or social networks. Quantum physics will reveal the full potential of Big Data, hitherto held back by the limitations of traditional computers .

Quantum entanglement to secure data transfers

There is no doubt that quantum computing will have a strong impact on Big Data and computing power more generally in the years to come. However, there are another element of quantum theory that could lead to even more change.

As a reminder, the quantum bits or qubits can have several states simultaneously. This allows quantum computers to run several thousand times faster than conventional computers.

It is clear that this exponential increase in power will enable speed up Big Data analysis. But that’s not all.

The quantum entanglement” phenomenon could revolutionise Big Data by protecting data transfers from hackers and speeding up communication. This phenomenon involves “attaching” two particles together, and changes in one particle have the same effect on the other.

This can happen even if the particles are separated by an infinite distance. In practice, the known record is 300 kilometers. It was recorded in an experiment conducted by NTT Basic Research Laboratories in Kanagawa, Japan. Instantaneous changes occurred on the two bound particles.

The implications for Big Data are far-reaching. Indeed, this phenomenon could be exploited to transporting data without the possibility of intercepting it unless you have the other particle. And this technology is already in its infancy.

The China has already launched a satellite that is impossible to pirate…based on quantum entanglement. However, for the time being, this satellite is not yet used for communication, but to develop new methods of communicating messages protected against hackers and capable of reaching a speed “faster than light”.

Within a few years, communications based on quantum entanglement could therefore become widespread. Moreover, this technology could also accelerate the development and use of quantum entanglement. the democratization of quantum computing.

For good reason, security risks are currently one of the main problems of quantum computing. Developing this technology means open Pandora’s boxbecause it’s a double-edged sword.

Quantum computers would be able to bypass very quickly current encryption techniquesincluding the latest standards. However, if data can only be communicated between two particles in the same state, this threat is no longer present.

Data transfers can therefore be completely secure. This could be a a real revolution for data storage.

In conclusion, quantum physics will soon turn the world of Big Data upside down. New doors will be opened, with analyses infinitely faster than at present

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