What is Quantum Computing?
Quantum computing is the use of quantum-mechanical phenomena such as superposition and entanglement to perform computation. A quantum computer is specifically used to perform such calculation, which can be implemented theoretically or physically. The field of quantum computing is a sub-field of quantum information science, which includes quantum cryptography and quantum communication. The idea of Quantum Computing took shape in the early 1980s when Richard Feynman and Yuri Manin expressed the idea that a quantum computer had the potential to simulate things that a classical computer could not.

The year 1994 saw further development of Quantum Computing when Peter Shor published an algorithm that was able to efficiently solve problems that were being used in asymmetric cryptography that were considered very hard for a classical computer. There are currently two main approaches to physically implementing a quantum computer: analog and digital. Analogue methods are further divided into the quantum simulation, quantum annealing, and adiabatic quantum-computation.

Basic Fundamentals of Quantum Computing
Digital quantum computers use quantum logic gates to do computation. Both approaches use quantum bits or qubits. These qubits are fundamental to Quantum Computing and are somewhat analogous to bits in a classical computer. Like a regular bit, Qubit resides in either 0 or 1 state. The specialty is that they can also be in the superposition of 1 and 0 states. However, when qubits are measured, the result is always either a 0 or a 1; the probabilities of the two outcomes depends on the quantum state they were in.

Principle of Operation of Quantum Computing
A quantum computer with a given number of quantum bits is fundamentally very different from a classical computer composed of the same number of bits. For example, representing the state of an n-qubit system on a traditional computer requires the storage of 2n complex coefficients, while to characterize the state of a classical n-bit system it is sufficient to provide the values of the n bits, that is, only n numbers.

A classical computer has a memory made up of bits, where each bit is represented by either a one or a zero. A quantum computer, on the other hand, maintains a sequence of qubits, which can represent a one, a zero, or any quantum superposition of those two qubit states; a pair of qubits can be in any quantum superposition of 4 states, and three qubits in any superposition of 8 states. In general, a quantum computer with n qubits can be in any superposition of up to different states. Quantum algorithms are often probabilistic, as they provide the correct solution only with a certain known probability.

What is the Potential that Quantum Computing offers?
Quantum Computing is such a unique field that very few people show their interest in it. There is a lot of room for development. It has a lot of scope. Some of the areas in which this is penetrating today are:

  • Cryptography – A quantum computer could efficiently solve this problem using multiple algorithms. This ability would allow a quantum computer to break many of the cryptographic systems in use today
  • Quantum SearchQuantum computers offer polynomial speedup for some problems. The most well-known example of this is quantum database search, which can be solved by Grover’s algorithm using quadratically fewer queries to the database than that is required by classical algorithms.
  • Quantum Simulation – Since chemistry and nanotechnology rely on understanding quantum systems, and such systems are impossible to simulate efficiently classically, many believe quantum simulation will be one of the most important applications of quantum computing.
  • Quantum Annealing and Adiabatic Optimization
  • Solving Linear Equations – The Quantum algorithm for linear systems of equations or “HHL Algorithm,” named after its discoverers Harrow, Hassidim, and Lloyd, is expected to provide speedup over classical counterparts.
  • Quantum Supremacy

In conclusion, Quantum computers could spur the development of breakthroughs in science, medication to save lives, machine learning methods to diagnose illnesses sooner, materials to make more efficient devices and structures, financial strategies to live well in retirement, and algorithms to direct resources such as ambulances quickly.  The scope of Quantum Computing is beyond imagination. Further developments in this field will have a significant impact on the world.


What is Natural Language Processing (NLP)?

Natural Language Processing commonly abbreviated as NLP is a subfield of computer science and artificial intelligence. It is mainly concerned with the interaction between computers and the languages humans speak, like English, Italian, French, among various others. It is used in particular to program machines to process and analyze large amounts of natural language data.

The development of NLP applications is quite challenging because computers traditionally require human beings to communicate to them through a programming language or a high-level language. Human speech, however, is not always precise, is often ambiguous and is dependent on factors like the emphasis on a particular word or expression. These are the factors that the computer finds very difficult to understand.

How does Natural Language Processing work?

Syntax and Semantic analysis are two main techniques that are used with NLP. The  Syntax is the arrangement of words in a sentence to make some grammatical sense. Different Syntax methods used are:

  • Parsing
  • Word segmentation
  • Sentence breaking
  • Morphological segmentation and
  • Stemming

The Semantic involves the use and meaning behind the words. NLP applies the algorithms to understand the grammar and meaning of the sentences. The techniques used by NLP in semantic Analysis are:

  • Named Entity Recognition
  • Natural Language Generation

The current approaches to NLP are mainly based on Deep Learning, which is a type of AI that examines and uses the patterns in data to improve programs understanding. It is basically dependent on supervised learning, which consists of a training set and a test set.

Three tools very commonly used for NLP are NLTK, Gensim, and Intel NLP Architect. Natural Language Toolkit(NLTK), is an open-source python module with data sets and tutorials. Gensim is a Python library for topic modeling and document indexing. Intel NLP Architect is also another Python library for deep learning topologies and techniques.

What are the Uses of Natural Language Processing?

Although, NLP came into existence for the first time by Alan Turing when he published an article titled “Computer Machinery and Intelligence,”. The vast use came into effect only from the 80s, after the introduction of Machine Learning. Before 1980, the most natural language processing systems were based on complex sets of hand-written rules. Writing these rules included a lot of labor and was inaccurate due to diversity in the pronunciation of a language. Introduction of Machine Learning speeded up the development of Natural Language Processing.

Natural language Processing is very widely used today in our daily routine. It finds its application in:

  • Chatbots – Chatbots handle various clients and answer their query without considerable human effort. Chatbots are trained on a vast set of data and hence process only the essential part from a conversation. Companies like Uber, Zomato use the Chatbots to minimize human involvement.
  • Voice Assistants – The most significant use of NLP is implemented for this purpose. Technological giants like Google, Microsoft, Amazon, etc. use their own personal voice assistant to help in communicating with smart devices quickly. Amazon assigns over 1000 personnel globally for enhancing its voice assistant.
  • Very brilliant use of NLP came with the name Grammarly, which is a tool that keeps on a check on writers’ write-ups, and points out grammatical errors and suggests better phrases.
  • Google translate also uses NLP to translate a webpage from one language to another by understanding its content.


What are the Challenges faced by NLP?

NLP, though a new technology with a lot of advantages isn’t completely developed. For Example – Semantic and Grammar Analysis is still a challenge for NLP. Other difficulties includes, NLP not relating to sarcasm easily, since NLP cannot figure the changing meaning of words on the basis of speaker emphasis. NLP is also challenged by the fact that the dialect of people changes with regions.

On a final note, Natural Language Processing is a very handy tool, although it is in the developing state and faces some difficulties. The recent development in the NLP has made it a gem for the Technological Giants. The future of NLP through Machine and Deep Learning seems quite bright.