Quantum-Enhanced Artificial Intelligence (QAI): Quantum Computing for Machine Learning beyond classical boundaries

By: Aiyaan Hasan, International Center for AI and Cyber Security Research and Innovations (CCRI), Asia University, Taiwan, rayhasan114@gmail.com


The meeting point of artificial intelligence (AI) and quantum computing has become a ground-breaking frontier in the rapidly changing field of technology. This article examines the concept of Quantum-Enhanced Artificial Intelligence (QAI), which pushes the limits of conventional machine learning by utilising the concepts of quantum computing. The study reveals the revolutionary potential of QAI in pushing machine learning skills into unexplored domains, from the fundamental ideas to real-world applications.


With classical computing, artificial intelligence—the study of machines that can learn and adapt—has made enormous advances. But the increasing complexity of issues demands a step beyond traditional boundaries.[1] Let’s introduce Quantum-Enhanced Artificial Intelligence (QAI), an area where the concepts of quantum computing hold the potential to completely transform the field of machine learning.[2]

Exploring the possibilities of superposition and entanglement, quantum computing promises exponential processing capability. This quantum advantage may be used in machine learning to process and analyse large datasets at previously unheard-of speeds, leading to new possibilities in algorithmic efficiency and problem-solving. In this piece, we explore the fundamentals of QAI, its uses, and the revolutionary possibilities it offers the artificial intelligence community.[3]

Figure : Components of Quantum-Enhanced Artificial Intelligence (QAI)

Fundamental Ideas of QAI:

The combination of conventional machine learning techniques and quantum computing is the basis of QAI. The fundamental ideas centre on the use of quantum bits, or qubits, which are able to exist in many states concurrently because of superposition. Because of their inherent parallelism, QAI algorithms can investigate several solutions at once, possibly achieving exponential speedups over their classical equivalents.[3]

Another quantum phenomena called entanglement improves QAI’s performance even further. Qubits have the ability to entangle, which implies that even when they are physically separated, the states of one qubit and another are intimately connected. Because of its distinct interconnectivity, QAI algorithms can handle information better, opening up new possibilities for tackling complicated problems.[4]

Utilising QAI in Machine Learning Applications

  1. Quantum machine learning methods: In some jobs, quantum algorithms have proven to perform better than their conventional equivalents. Examples of these are the Quantum Support Vector Machine (QSVM) and Quantum Principal Component Analysis (QPCA). These algorithms promise faster training and better performance by processing information more efficiently through the use of quantum parallelism and interference.
  2. Quantum Neural Networks: The foundation of contemporary machine learning, neural networks, is covered by QAI. With the goal of improving learning outcomes and computational capabilities, quantum neural networks investigate how to incorporate the concepts of quantum computing into the design and functioning of neural networks.
  3. Quantum-enhanced Optimisation: Machine learning frequently deals with optimisation issues, where it might be difficult to select the best option from a wide range of options. Optimisation problems that are essential to machine learning might be revolutionised by quantum algorithms, including the Quantum Approximate Optimisation Algorithm (QAOA).

Challenges and Considerations:

Despite the attractiveness of QAI, there are some difficulties. There are several challenges in developing and maintaining stable quantum computers, qubit coherence times, and converting conventional machine learning algorithms to quantum equivalents. To lessen the effects of noise and decoherence on quantum calculations, the sector also needs to create quantum error correcting methods.


In conclusion, an innovation in the field of machine learning is represented by quantum-enhanced artificial intelligence. The use of quantum computing concepts opens up new avenues for processing power and may help address issues that were thought to be intractable. We see a day when machine learning skills will surpass traditional bounds as researchers continue to investigate and solve the problems related to QAI.


  1. Taylor, R. D. (2020). Quantum artificial intelligence: a “Precautionary” US approach?. Telecommunications Policy, 44(6), 101909.
  2. Singh, M., Dhara, C., Kumar, A., Gill, S. S., & Uhlig, S. (2022). Quantum artificial intelligence for the science of climate change. In Artificial Intelligence, Machine Learning and Blockchain in Quantum Satellite, Drone and Network (pp. 199-207). CRC Press.
  3. Martın-Guerrero, J. D., Lamata, L., & Villmann, T. Quantum Artificial Intelligence: A tutorial.
  4. Lutz, R. (1990). Quantum AI. Behavioral and Brain Sciences, 13(4), 672-673.
  5. Deveci, M., Gokasar, I., Pamucar, D., Zaidan, A. A., Wen, X., & Gupta, B. B. (2023). Evaluation of Cooperative Intelligent Transportation System scenarios for resilience in transportation using type-2 neutrosophic fuzzy VIKOR. Transportation Research Part A: Policy and Practice172, 103666.
  6. Gupta, B. B., Gaurav, A., Panigrahi, P. K., & Arya, V. (2023). Analysis of artificial intelligence-based technologies and approaches on sustainable entrepreneurship. Technological Forecasting and Social Change186, 122152.
  7. Li, S., Gao, L., Han, C., Gupta, B., Alhalabi, W., & Almakdi, S. (2023). Exploring the effect of digital transformation on Firms’ innovation performance. Journal of Innovation & Knowledge8(1), 100317.
  8. Malik, M., Prabha, C., Soni, P., Arya, V., Alhalabi, W. A., Gupta, B. B., … & Almomani, A. (2023). Machine Learning-Based Automatic Litter Detection and Classification Using Neural Networks in Smart Cities. International Journal on Semantic Web and Information Systems (IJSWIS)19(1), 1-20.

Cite As

Hasan A. (2023) Quantum-Enhanced Artificial Intelligence (QAI), Insights2Techinfo, pp.1

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