How Quantum Computing Will Change the Way We Use AI

Hand demonstrating digital brain hologram in circuit style over abstract blue background.

The benefits of AI are not fully realized; however, with the support of quantum computing, we will see these technologies reach their potential. (Image: Prostockstudio via Dreamstime)

The way technology evolves is stunning to most people. In the computing realm, developments take place at an incredible rate, rendering prevalent technologies obsolete quickly. With time, computing devices continue to get smaller, yet their performance and capabilities get enhanced. Quantum computing is in a league of its own. Quantum computers are made to execute tasks with greater enhanced precision and efficiency than regular computers. They rely on classical computers, though. 

Quantum computing and artificial intelligence are mutually beneficial

While Quantum computing is being hailed as a ground-breaking technology, the truth is it cannot achieve its potential on its own. Technology experts opine that to leverage the benefits of quantum computing, using it in alliance with Artificial Intelligence will be necessary. Both of these are transformational technologies and they complement each other. It is true that AI tools are being used on classical computers but the computational capabilities of the latter bottleneck the output of AI to an extent. Using Quantum computing can help AI achieve its full potential. This will help researchers and scientists break new ground in the near future.

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Part of a D-Wave Quantum Processor.
‘The rainbow of colors you see in these photos are iridescent thin-film effects, like a butterfly’s wing, and serve as a poetic complement to the mind-bending physics of these processors, which harness the refractive echoes across trillions of parallel universes to compute in a fundamentally different way from any classical computer, says Steve Jurvetson.
(Image: Steve Jurvetson via Flickr)

The problems of using Quantum Computers are there. In the computation phase, any slight disturbance in the system causes the failure of quantum computation. This is called de-coherence. So a quantum computer has to be kept isolated from external interference when the computation phase is active. In quantum computing, error correction can never be overlooked. When AI is coupled with quantum computing, these problems can be largely eliminated. 

The amalgamation of AI and quantum computing and it’s use cases

Expert quantum algorithm researcher Samuel Fernández Lorenzo says: “In classical computing we know how to solve problems thanks to computer language (AND, OR NOT) used when programming. Operations that are not feasible in bit computing can be performed with a quantum computer. In a quantum computer all the numbers and possibilities that can be created with N qubits are superimposed (if there are 3 qubits, there will be 8 simultaneous possible permutations.) With 1,000 qubits the exponential possibilities far exceed those that we have in classical computing.” (The Quantum Insider).

The use of quantum computing and AI could aid in scientific modeling and discoveries.
The use of quantum computing and AI could aid in scientific modeling and discoveries. (Image: ProductionPerig via Dreamstime)

Below are some use cases in society:

  • Handling Gargantuan Amounts Of Data – Anyone who has dealt with AI and ML (Machine learning) tools and tech knows such technologies are used to deal with a mammoth amount of data on a daily basis. Processing and analyzing this data using traditional computing methods and devices can be tedious after a point. Quantum computers are better equipped to process large amounts of data.
  • Developing Enhanced Models – The businesses in sectors like finance, pharmaceutical, life sciences are wary of leveraging classical computing models. They require complex models that are hard for classical computers to handle. Quantum computers are better equipped to deal with such complex models. This will help researchers and scientists in wrapping up their studies faster, with more accuracy in the future.
  • Specialized Algorithms – According to the views of Lorenzo, in most AI industrial applications supervised learning is deployed. Quantum Machine Learning experts are now looking forward to developing enhanced quantum computer algorithms. He says: “In this area, based on the different QML — quantum machine learning — proposals that have already been set forth, it is likely that we’ll start seeing acceleration – which, in some cases, could be exponential – in some of the most popular algorithms in the field, such as ‘support vector machines’ and certain types of neural networks.” (The Quantum Insider). Quantum computing is useful for deploying reinforcement learning and non-supervised learning.
  • Using Various Datasets – Traditional computing devices fare poorly with data placed in various datasets. It is not about the abundance of data as such. However, quantum computers handle data obtained from varying sets much better. 

Summing it up

The fast-growing quantum computing market is poised to reach a staggering $2.2 Billion by 2026, as per the latest reports. However, focus should be given to combining the technology with AI. This will fetch benefits for the businesses in various sectors and their end-users will also benefit in the long run. 

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