New Technologies in Facebook 

In the Present computer world, the data on the Internet is getting bigger and bigger. People are living in the data world, from uploading an image on Facebook to sending good morning messages on Whatsapp. In this scenario, Big Data comes into the picture. Big Data denotes data that is beyond the limit of storage capacity. This large amount of data is very difficult to process through classical computers and it is a very big challenge at the hand for data scientists.

So big data analysis is very important in market analysis, and business intelligence, and most important is to make predictions. There are many big data analytical tools are there in the market, but there are not up to the rank in optimization and period. To overcome these problem modern computers has given birth to a new concept called quantum computing. Quantum computing is a whole new concept built on concepts of Quantum mechanics. As we know there is a world in an atom, thus leading to the establishment of a new branch in physics called quantum physics. Using these concepts of quantum mechanics the speed of processing has increased exponentially over classical computation. Scientists and researchers got a new tool to process big data analytics over classical tools. In this paper, we are going to discuss how quantum computing plays an important role in the processing of big data.

Keywords: atom, quantum computing, big data, quantum mechanics, quantum entanglement.

Introduction

Since the first transistor was developed no one could have predicted that the computer and Internet will change the world forever. We cannot imagine Airline data, Business data, Facebook data, etc. centralized data on the web. Data beyond the limit of storage capacity and processing capabilities of classical computers is called Big data. Nowadays big companies like Google, Facebook, and Amazon are managing their data in big data warehouses. Cloud infrastructure storage is very good for small business organizations. Nowadays vast increase of data in the cloud, IT industries facing a very big challenge in the processing of the data. For big data analysis, we need a system with high-end processing capability systems. As we know the processing speed of a classical computer depends on the number of transistors being used. By increasing transistors, we can increase the processing capacity or we can use HDFS(Hadoop distributed file system) and MapReduce for faster processing of data. When we talk about high-pea performance computing techniques, the word Quantum Computing has made a dramatic change in our mindset and provides processing capabilities to computation in order of 2n

for n Qubit input. The promise of quantum computers is what computations classical computers will execute in an hour but quantum computers can execute in a fraction of seconds.

Background,

Some important terms in big data analytics:

Big data: Data beyond storage capacity and processing capabilities of classical computers.

Types of data:

• Structured data: data that has pre-set of patterns, address book, Internet search, banking transactions, etc.
• Unstructured data: Data that has no pre-set pattern, like audio, video, messages and social media, etc.

Why Big data?

1. Low cost to store the data.the 
2. Powerful multicore processors.

Open source tools like SQL, Hadoop.

1. Advanced analytical techniques like Machine learning.
2. Managed big data platforms: Cloud service provider.

Quantum Computing: Quantum computing was developed on quantum mechanics. It will process data on the internet much faster than classical computers.

Qubit: In quantum computing to store the data we use qubits.

Generation of qubits:

A quantum makes use of quantum particles to be in a superposition state of two or more states at the same time. N qubits will combine or entangle to represent 2N

Values at once, which will go to allow for parallel processing.

Fig 1: Generations of qubits.

Bloch’s sphere:

In quantum mechanics, the Bloch’s sphere (also known as Poincare sphere in optics) is a geometrical representation of the pure state space of a 2-level quantum system. Alternatively, it is a pure state space of a 1qubit quantum register. The Bloch’s sphere is geometrically a sphere and correspondence between elements of the Bloch sphere and pure states can be explicitly given.

Fig 2: Bloch’s sphere

Quantum entanglement:

Quantum entanglement is one of the important principles of quantum physics. In states that, quantum entanglement means that multiple particles are linked together in such a way that, the measurement of one particle’s quantum state determines the possible quantum states of the other particles. This connection is not depending on the position of the particles in space. Even though if you separate entangled particles by billions of miles, changing one particle will induce a change in the other. Even though quantum entanglement appears to transmit information instantaneously, it doesn’t violate the classical speed of light because there’s no ‘movement’ through space.

Fig 3: Quantum entanglement process.

Impact of quantum computing on big data analysis:

Depending on some researchers says that since 2016 we are generating 2.5 quintillion bytes of data per day alone. According to some stats people sent 3.5 million text messages per minute.

In this scenario, quantum computing will give us solutions. John Preskill coined the term “quantum supremacy” to describe the potential for quantum computers to solve problems that classical computers can’t because they’re not powerful enough or fast enough.

Imagine you have a database of financial data, weather data, genetic data, etc. with 100 quintillions. A classical computer will take impractical time for searching an item or data set in a given set of data. Quantum computing doesn’t just have the potential to improve search speed or process speed. Research suggests that we may see an exponential speed increase in big data classification and Topological analysis of complex data sets. Both of these cases involve applying quantum computing to existing machine learning systems.

MIT and Google have successfully demonstrated quantum computing to obtain an exponential speed increase in data classification and regression analysis. Topological analysis is used in analyzing large data sets in big data analysis.

There are many proofs for an increase in the processing speed of the data, here is a graph of the processing of data. It is a graph between Intel processor vs Quantum computer.

FIG 4: QC vs Intel

Conclusion

In this paper, we made a brief explanation of quantum computing in big data analysis. At present, a normal home computer wouldn’t have the ability to process large amounts of data at once. Quantum computers could have the capability of reaching into a database, instantly accessing all items at once, and delivering an analysis within seconds. With quantum computers, we could uncover patterns instantly.

What’s next for AI and quantum computers? It is truly hard to say what we should expect from two areas of research that are advancing at lightning speed. At present, it seems like the first area of research to be most impacted by the melding of these two subsets of computer science will be big data.

Artificial intelligence and quantum computers, once fused, will most likely improve each other — artificial intelligence is tasked with improving quantum computing processes and vice versa. This means analysis of giant data sets will become more manageable, and perhaps, more efficient as time goes on and as these technologies progress.

Acknowledgment

We would like to thank Prof. Padma Dandannavar of KLS GIT Belagavi, for giving inspiration and support throughout the paper presentation.

References:

1. Saumya Jain, Georgia state university, Quantum Computer architectures,
2. Abhishek Pandey, Ramesh V.Quantum computing for big data analysis.Indian Journal of Science, 2015, 14(43),98-104.
3. C. Aytekin, S. Kiranyaz, and M. Gabbouj, “Quantum Mechanics in Computer Vision: Automatic Object Extraction,” IEEE International Conference on Image Processing (ICIP), pp. 2489-2493, September 2013.
4. Kanamori Y, Yoo SM, Pan WD, Sheldon FT (2006). A short survey on a quantum computer, International Journal of Computers and Applications, Vol. 28, No. 3.         

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