Jim McGregor
Over the past year, there has been increasing focus on how quantum computers fit into and link to classic computing architectures. Quantum computers could act as an accelerator to perform complex calculations for certain tasks that are beyond the capabilities of even classical supercomputers. The classical computers or servers are used for preprocessing in the development of quantum algorithms and circuits and for postprocessing to manage the errors, improve the results, and complete the processing task. As is evident from the growing number of AI use cases, AI can enhance classical computing capabilities. So, it stands to reason that AI could also enhance quantum computing capabilities and several companies are working towards achieving this goal.
Even though many people and companies are starting to combine quantum and AI into a single term, the two are very distinct technologies. AI is the training and use of neural network models developed and run on classical computing platforms powered by CPUs, GPUs, NPUs, DSPs, FPGAs, and other traditional binary-processing logic elements. Quantum computers use alternative compute architectures, such as superconducting transmon qubits, to solve very complex problems using quantum physics. While the two require different hardware, software, and support systems, the integration of the two is moving forward, especially for the benefit of quantum computing. IBM is one of the companies paving the way for AI to complement quantum computing development.
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