Quantum computing takes aim at AI

But operators of AI systems need to think creatively about how to use quantum computing alongside AI to get the full benefits, he adds. IT leaders must envision future AI workloads to find a place for quantum AI, he suggests.

“There is real strategic advantage in simulating multiple future pathways to see where GPUs come under stress, especially with billions of agents coming online in the next few years, greater needs for governance and regulatory compliance, and new ways to deliver autonomous decision-making with more confidence,” he says. “The door that opens is hybrid workloads where GPUs, CPUs, and QPUs each do what they do best.”

Quantum challenges

Quantum solutions still have some disadvantages, however, that make them unlikely replacements for GPUs in AI tasks, other experts say. GPUs are well suited to the dense matrix operations that dominate current machine learning problems, whereas many proposed quantum algorithms come with significant data-loading, measurement, and error-correction challenges, says Arjun Kudinoor, quantum security advisor at cybersecurity vendor Protegrity.