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The article correctly identifies that while early hopes for using D-Wave systems to train machine learning models have not materialized, the two fields now exhibit a symbiotic relationship where each advances the other. Their primary convergence lies in complementary problem-solving domains—such as materials science and chemistry—and shared resource constraints rather than direct competition. Consequently, collaboration between AI developers and quantum hardware engineers represents the most viable path forward for both technologies to reach practical utility.
AI and quantum computers will be frenemies
IN 2013 GOOGLE and NASA, America’s space agency, launched a quantum computing lab. The idea was to use a specialised quantum computer built by D-Wave, a Canadian firm, to improve the onerous task of training of machine-learning algorithms. Alas, the lab was ahead of its time not just once, but twice over. Neither machine learning—soon to be rechristened “artificial intelligence”—nor quantum computing were then mature technologies, as D-Wave’s boss, Alan Baratz, now concedes. A paper published in Nature Physics the year after the lab was founded showed that D-Wave’s machine was no better at AI work than a normal graphics-processing unit (GPU). Yet the fates of AI and quantum computing remain entwined. Some see…
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