Quantum Computing & Artificial Intelligence Applications

During the recent decade, IBM has come up with the full-fledged manufacturing of Quantum Computer (QC). Although the idea was circling during the 20th century. These computers are said to be the fastest machines ever invented. Let alone its comparison with conventional computers, QC has in certain cases outperformed supercomputers. Which brings the audience to a baffling question i.e. Is QC going to be useful in Artificial Intelligence (AI) applications? Well, this blog digs the answer.

What Is Quantum Computer?

A Quantum computer is a machine that functions on quantum physics principles for data storage and computations. This is profoundly advantageous when a machine is performing a complex task. And this is exactly where a QC outperforms classic computers.

Unlike a classic computer, a QC has a basic unit of memory in the form of quantum bits or qubits. In the making of qubits, physical systems are involved such as Electron spinning and Photon orientation. So, qubits are units in QC and binary bits (0,1, in which information is coded) are in classic computers.

In a nutshell, 8 bits in a classic computer can represent any number between 0 to 225. Whereas in a quantum computer, the same representation of numbers is done by 8 qubits.

Use of the quantum computer

Quantum computer is increasingly becoming a necessity for big tech companies. Given its ability to solve complex problems, here are some major areas where its application can be fruitful.

    • Aviation

    • Forecasting

    • Cryptography

    • Data Analytics

    • Pattern Matching

    • Medical Research

    • Self-Driving Vehicles

Artificial Intelligence and Quantum Computing

Well, there is a common belief that Quantum computing is going to diminish or even replace the significance and existence of Artificial Intelligence. According to experts, it is a complete falsehood. In fact, quantum computing is a new impetus for AI. Which implies that it would enhance the application of AI.

Currently, quantum computing is still in its nascent stage and it may have a limited scope. However, on a large scale, quantum computing affects certain tasks in AI which are mentioned below.

Stimulation Modeling

This is the process that generates and evaluates the digital prototype of a physical model before making it public.


In AI, the optimization problem is the problem of searching for the best possible solution among all feasible solutions. It varies when it comes to variables. Variables can be continuous or discrete.


Just like in statistics, data sampling in AI is a technique rendered to manipulate and analyze the subset of data collected as a sample. Which resultantly helps us identify patterns and trends in the larger data set being analyzed.

Benefits of Quantum Computing in AI: Technically

Less time in training

The most significant feature of QC is that it can process multiple numbers of dimensions. Traditionally, a classical perceptron (an artificial neuron) processes an input of N dimension. When it comes to QC, there is a Quantum Perceptron and it processes 2N-dimension. With this ability, QC can render a remarkable increase in the number of dimensions it can process.

Better results

It is now widely understood that classifying the patterns in simple images can be far more facile for a quantum perceptron. The quantum model of the perceptron is being used as an elementary nonlinear classifier of simple patterns. And that is highly efficient and accurate.

Achieving Parallelism

The primitive examples of quantum algorithms are insanely fast. So much so that none of the deterministic classical algorithms can outperform quantum algorithms. Since its ability to simultaneously analyze f(0) and f(1), QC solves the problem complex in nature. This possibility stems from quantum parallelism. Quantum parallelism permits computing 2N entries for a state that has n-qubits. Simply put, massive growth in computing space can be achieved from a linear or simple growth in the number of qubits.

Benefits of Quantum Computing in AI: In Real-life Applications

Fraud Detection & Cyber Security

QC has given a great edge to internet security. With the AI algorithm in place, QCs are assisting law enforcement agencies to better protect and surveil the internet.

Financial Services & Healthcare

These are just two of many industries that are exploiting the advantages of QC with the help of AI. Due to the extra load of data, these industries need a faster system.

Solving Mathematical Problems

Solving mathematical problems is the hardest task. It can take years to be solved. But thanks to QC which enhanced the AI. The problems that took years to be solved by a classical computer, QC now is bringing solutions to the same problems in a few months.


Quantum computers remained limited only to theories. But that was three decades ago. Now its existence is challenging traditional computing machines. Its performance is so remarkable than one could ever imagine. Therefore, it is becoming more and more practical and not to mention necessary for the tasks too complex for classical computers. However, what about AI? Well, there is a farce circling that QC would replace AI. Despite its smartness more than any other machine in the world, QC will only enhance the applications of AI. It is still in its nascent stage. So, it is too early to assume anything negative about QC.


Writer at BCD Apps

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