Can Quantum Computing Impact the Applications of Artificial Intelligence?

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Quantum computing enhances AI by increasing its speed, efficiency and accuracy. It utilizes qubits and operates non-linearly, outperforming conventional computers. This breakthrough enables quantum computing to be applied in various AI use cases. Industries such as maritime logistics, electric vehicles, semiconductors, luminescence and power are already benefiting from quantum computing's problem-solving capabilities.

Quantum computing, a groundbreaking field that harnesses the principles of quantum mechanics to process information, holds immense potential to revolutionize the world of technology and science. By leveraging the extraordinary properties of quantum bits, or qubits, such as superposition and entanglement, quantum computers have the capability to surpass the limitations of traditional computing systems, offering unprecedented speed, efficiency, and accuracy.

Compared to classical computers that operate in a linear fashion, quantum computing operates on a fundamentally different level. This fundamental difference enables quantum computers to tackle complex calculations and algorithms exponentially faster and with higher precision. Consequently, the emergence of quantum computing paves the way for transformative advancements in various domains, particularly in the realm of artificial intelligence (AI).

What is quantum computing?

Let’s explore the concept of quantum computing through an analogy. Imagine you have a large library and you’re trying to find a specific book. In traditional computing, you would search for the book by examining each bookshelf and book one by one until you find the desired one. This linear approach can be time-consuming and inefficient, especially if the book you’re looking for is located toward the end of the library.

When using quantum computing, however, you can imagine that each book in the library represents a different possibility or solution. Instead of searching linearly, a quantum computer can explore all the books simultaneously, thanks to the concept of superposition. It can consider all possible paths at once and instantly identify the location of the desired book.

Quantum computing utilizes qubits, which can represent multiple states simultaneously, combining both 0 and 1. This allows quantum computers to perform parallel computations and analyze a vast number of possibilities in a fraction of the time it would take for classical computers to do the same.

Impact on artificial intelligence

As pointed out, quantum computing has multiple use cases across industries and it has helped solve complex problems. A few use cases are described below.


Traffic management

Consider an ambulance racing through rush-hour traffic, carrying a critically ill patient. Every passing moment is of utmost importance. The driver urgently needs to identify the least congested routes to ensure a swift and efficient journey. While conventional computers analyze road conditions sequentially, quantum computing possesses the remarkable ability to simultaneously evaluate all potential routes, enabling it to swiftly determine the most optimal suggestion.

Medical care

In the case of treating a critically ill patient with complex conditions, hospitals often convene a medical board comprising specialists from diverse fields. These experts collaborate to explore different treatment options and find the most effective solution. However, this approach can be time-consuming, and slow down decision-making. Quantum computing, on the other hand, has the potential to revolutionize this process.

By inputting various possibilities into a quantum computing system and providing it with historical data that corresponds to similar medical conditions, quantum computing can rapidly evaluate the potential effectiveness of each approach and offer optimal suggestions. This quantum advantage enables healthcare professionals to obtain valuable insights in a significantly shorter time frame. (Also Read: 9 Uses of Generative AI in Healthcare)

Machine learning

Quantum computing can provide the ideal stage for machine learning by providing the right data faster. Machine learning is about computers learning from data and being able to create or understand patterns, just like the human brain does. However, in many cases, machine learning may be constrained by the poor quality of data and the slow availability of data. Quantum computing can potentially compute huge volumes of data quickly and provide the same to machine learning.

Cryptography and security

Cryptography and security are about securing data from unauthorized access. Quantum computing can potentially take cryptography and security to another level where unauthorized access to data becomes much harder than before. However, there are two ways to view the role of quantum computing in cryptography and security. One view is that quantum computing can use qubits to calculate all the possible ways of data breach attempts and provide appropriate data to fortify the information. But the opposite view is that quantum computing can also be counterproductive because hackers can use it to quickly calculate the various possible ways to breach a server that contains highly confidential data.


For all the massive advantages quantum computing can potentially offer, there are a few drawbacks. That doesn’t mean that it’s a bad idea, it just means that it’s worth first identifying how much of the narrative is hype and how much, substantial. Here are some points that provide a reality check to the hype around quantum computing.

  • Quantum computing is expensive and so far, beyond the reach of organizations that don’t have big and sustained budgets for it. A quantum computing studio resembles the computer rooms of the time when computers had just started- they were huge, expensive, and required maintenance. Not every organization can do that.
  • Quantum computing may be more effective than regular computing but it’s extremely sensitive to noise or data. This means that the data you feed to it must be accurate and in a format that it accepts, otherwise, it will generate errors. Error correction has been one of the biggest challenges with quantum computing. The errors it generates when it cannot process the noise are extremely complex and time-consuming to correct.
  • Concerns around the misuse of quantum computing are huge. For all its limitations, quantum computing can crack open the toughest encryptions. Think of the catastrophizing consequences when hackers with malicious intentions use quantum computing. Countries have been trying to acquire a first-mover advantage with regard to that. President Joe Biden of the United States of America signed the Quantum Computing Cybersecurity Preparedness Act to enable the Office of Management and Budget (OMB) to adopt quantum computing. But it’s not possible for all the countries to do it uniformly and that inequality creates a problem among nations.


Quantum computing is a great prospect, waiting to do great things, as clear from the various use cases. However, there remain problems of cost, sustenance, affordability, data availability, and the issue of ethics. Out of that list, data availability and ethics seem to be the biggest challenges facing the technology, as costs are expected to decrease over time. Certain countries are ahead in terms of expediting the development of quantum computing but there is no guarantee or framework that these countries will not use quantum computing in ways that are detrimental to other countries.


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Kaushik Pal
Technology writer
Kaushik Pal
Technology writer

Kaushik is a technical architect and software consultant with over 23 years of experience in software analysis, development, architecture, design, testing and training. He has an interest in new technologies and areas of innovation. He focuses on web architecture, web technologies, Java/J2EE, open source software, WebRTC, big data and semantic technologies. He has demonstrated expertise in requirements analysis, architectural design and implementation, technical use cases and software development. His experience has covered various industries such as insurance, banking, airlines, shipping, document management and product development, etc. He has worked on a wide range of technologies ranging from large scale (IBM…