Quantum Computing and its impact on AI

Before understanding Quantum Computing in detail and its affect on AI, lets first take a quick tour of understanding towards Quantum Physics.

Quantum Physics — A brief introduction

Quantum Physics : It is the part of physics that describes the smallest things in our universe, such as molecules , atoms, subatomic particles etc. Basically we and everything around us in made from quantum physics. In Quantum Physics we describe everything as wave rather than particle.

Quantum Physics === Quantum Mechanics
(can be used interchangeably)

A Quantum wave is not a physical or sound wave but it is an abstract mathematical description. To get the real world properties like position or momentum of an electron we have to do mathematical operations on this wave function. With Quantum Physics we don’t know anything with infinite detail, we can only predict probabilities that thing will happen and it look like this is a fundamental feature of universe.

A simple quantum wave function


How Quantum Physics helps in computing and thus become Quantum Computing ?

Idea of Superposition: In our day to day life everything what we see is in defined state, for e.g a light bulb. A light bulb can be either OFF or ON. But in quantum world, object can exist in both states together. A hypothetical atomic- level light bulb could simultaneously be both ON and OFF. This feature becomes the backbone of Quantum Computing.

Quantum- Superposition

Bits : In classical computers, the small unit of information is the bit which can hold a value of either 0 or 1, but never both at same time. As a result each bit can hold just one piece of information.

Classical/ Binary Computing : This is traditional computing system where, information is stored in bits that are represented logically by either a 0 (off) or a 1 (on).

Qubits : Using the idea of superposition, concept of bits in classical computing is leveraged to a certain level, quantum bits which are known as Qubits. These are the quantum equivalent of classical bits. One fundamental difference is that, due to superposition, qubits can simultaneously hold values of both 0 and 1. A Quibit can be any two level quantum system, such as a spin and a magnetic field , or a single photon, like Photons horizontal and vertical polarization, 0 and 1 are possible states.

Classical Bits and Qubits

Entanglement Property : Qubits exhibits this weird and unintuitive property, a close connection that make each of the qubits react to a change in other’s state instantaneously no matter how far they are. This means when measuring just one entangled qubit, you can directly deduce properties of its partners without having a look.

Qubit Manipulation : A normal logic gate gets a simple set of inputs and produces one definite output whereas the quantum gate manipulate an input of superposition rotates probabilities and produces another superposition as its output. So quantum computers sets up some qubits, applies quantum gates to entangle them and manipulate probabilities then finally measures the outcome, collapsing superposition to an actual sequence of 0s and 1s.

Impact of Quantum Computing on Artificial Intelligence :

Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving.
The ideal characteristic of artificial intelligence is its ability to rationalize and take actions that have the best chance of achieving a specific goal. A subset of artificial intelligence is machine learning, which refers to the concept that computer programs can automatically learn from and adapt to new data without being assisted by humans. Deep learning techniques enable this automatic learning through the absorption of huge amounts of unstructured data such as text, images, or video.

Quantum AI:

How does quantum AI work ?

Recently, Google announced TensorFlow Quantum(TFQ): an open-source library for quantum machine learning, in collaboration with the University of Waterloo, X, and Volkswagen. The aim of TFQ is to provide the necessary tools to control and model natural or artificial quantum systems. TFQ is an example of a suite of tools that combines quantum modeling and machine learning techniques

Tensorflow Quantum


  • Convert quantum data to the quantum datasets: Quantum data can be represented as a multi-dimensional array of numbers which is called as quantum tensors. TensorFlow processes these tensors in order to represent create a datasets for further use.
  • Choose quantum neural network models: Based on the knowledge of the quantum data structure, quantum neural network models are selected. The aim is to perform quantum processing in order to extract information hidden in an entangled state.
  • Sample or Average: Measurement of quantum states extracts classical information in the form of samples from the classical distribution. The values are obtained from the quantum state itself. TFQ provides methods for averaging over several runs involving steps (1) and (2).
  • Evaluate a classical neural networks model — Since quantum data is now converted into classical data, deep learning techniques are used to learn the correlation between data.

I really hope you enjoyed this article, If you have any suggestions for improving this post or just wanted to chat about these topics feel free to contact me: aditya.pareek97@gmail.com and you can also find me easily on LinkedIn

Experienced web developer, Quantum Physics/Computing enthusiasts. Extensively worked in Front-end development

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