Quantum artificial intelligence (QAI) has the potential to revolutionise many different industries by improving and speeding up established artificial intelligence (AI) techniques, despite being in its infancy.

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Quantum AI, or QAI for short, is the result of fusing AI with quantum computing. The goal of this study is to improve the efficiency of AI systems by employing the same quantum mechanical effects (such as superposition and entanglement) that quantum computers use.

Quantum computing is still in its infancy and has many technical hurdles to jump before it can be used to implement QAI. Despite this, there is significant investment in QAI research, and many AI experts view QAI as a critical area for future AI development. Some preliminary experiments have been conducted to see if Quantum AI can be used to enhance machine learning (ML) algorithms; however, this field is still in its experimental stages and is not mature enough for practical applications.

Enhanced Quantum Artificial Intelligence Algorithms

QAI’s performance can be improved through the development of more efficient quantum algorithms; however, this requires not only cutting-edge knowledge but also premier expertise in quantum mechanics, computer science, and mathematical optimisation.

The first step in using quantum algorithms is defining the nature of the problem you wish to address. For instance, these might occur in the realms of optimisation, simulation, or cryptography.

The next step is to select an appropriate quantum algorithm. The quantum circuit needs to be optimised as much as possible after an appropriate algorithm has been chosen to use in solving the problem. This lowers the noise output of the system by reducing the number of qubits needed to implement the algorithm and the number of gates needed to do so.

Since noise and other anomalies can cause errors in quantum systems (computers), error correction must be applied once optimisation is complete. Error-correction methods must be used in this case. Using surface code to protect the quantum state and improve calculation accuracy is one approach to reducing the error rate of quantum computations.

The quantum algorithm can be tested by running experiments and iterations on a real quantum computer or a quantum simulator. After the algorithm has been put through its paces, its shortcomings can be assessed, and adjustments can be made to improve its performance.

Working in tandem with subject matter experts could be the missing link needed to optimise the algorithm’s performance. These individuals will benefit from studying mathematics, computer science, and quantum mechanics. Their expertise could be used to critique the quantum algorithm, which would then lead to more testing, possible improvements, and ultimately, better results.

Is there hope for a better future thanks to quantum AI?

The short answer is “yes,” as rigorous QAI techniques that have been applied to some of the fields mentioned above have the potential to alter people’s lives in many ways.

Keep in mind, though, that we are still in the research and development phase of this technology; getting past the early stages of development could take a long time. However, when we do, QAI will undeniably bring about substantial changes to the world.

In 2022, IonQ and Hyundai Motor will work together to implement quantum machine learning to optimise the computation process for road sign image classification and simulation in a real-world test environment.