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In recent years, artificial intelligence (AI) has advanced at a breakneck pace. AI systems have beaten the worlds best players at complex games like chess, Go, and poker. Natural language processing algorithms can now hold remarkably human-like conversations. Computer vision technologies enable self-driving cars and other robotic automation.

With AI progressing so rapidly, there is a palpable sense that we are approaching and perhaps will soon surpass human-level artificial general intelligence (AGI).

Compounding this excitement around emerging AI capabilities is the long-simmering potential of quantum computing. Quantum computers promise almost inconceivable processing power stemming from uniquely quantum mechanical phenomena. As such, there is understandable hype around quantum computing acceleration ushering in an era of unbelievable machine intelligence.

However, this perspective assumes computational muscle alone can unlock artificial superintelligence. The reality is more nuanced. While quantum computing will undoubtedly assist AI progress in certain regards, raw processing power is not enough to achieve the flexible, general, and creative intelligence we associate with human minds.

Developing advanced algorithms and aggregation of quality data are equally, if not more, important. And there are open questions around how we integrate noisy, error-prone quantum computers with machine learning workloads.

Quantum computing offers tremendous potential for AI, but it is not a panacea to ignite an imminent computing revolution that suddenly cracks open artificial general intelligence.

For at least the past half-century, the processing capability of classical computers has steadily doubled approximately every two years. This exponential growth rate, known as Moore's Law, has allowed modern computers to perform incredible numbers of calculations per second and hold astounding amounts of memory.

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Quantum Computing and AI: Processing Power Isn't Everything - Medium