The convergence of quantum computing and artificial intelligence (AI) holds transformative potential, especially in areas requiring high computational power such as large scale data analysis, optimisation, and pattern recognition. Quantum computing, based on principles like superposition and entanglement, can process and store information in entirely new ways. When applied to machine learning (ML), it promises substantial speedups and the ability to tackle previously intractable problems. However, developing quantum-enhanced machine learning solutions is complex and requires specialised tools. Here, open source frameworks play a crucial role by offering accessible platforms and libraries that allow researchers, developers, and organisations to experiment and advance the field without the need for expensive proprietary software.
How quantum-enhanced AI works
Quantum-enhanced AI represents a major leap in computing by leveraging the principles of quantum mechanics to overcome the limitations of classical computing. Unlike classical computing, which relies on bits that represent either 0 or 1, quantum computing uses qubits, which can exist in a state of 0, 1, or both simultaneously through superposition. Furthermore, qubits can be entangled, meaning the state of one qubit can directly influence the state of another, regardless of the distance between them. These properties allow quantum computers to perform complex computations at extraordinary speeds, achieving levels of parallelism that are unattainable with classical systems.
Figure 1 shows the flow of quantum data (QD) through the quantum CPU and its interaction with machine learning applications like biometric recognition and autonomous driving.
Quantum mechanics principles: Superposition and entanglement
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