A Hybrid of Quantum Computing and Machine Learning Is Spawning New Ventures
Machine learning, the field of AI that allows Alexa and Siri to parse what you say and self-driving cars to safely drive down a city street, could benefit from quantum computer-derived speedups, say researchers. And if a technology incubator program in Toronto, Canada has its way, there may even be quantum machine learning startup companies launching in a few years too.
Research in this hybrid field today concentrates on either using nascent quantum computers to speed up machine learning algorithms or, using conventional machine learning systems, to increase the power, durability, or effectiveness of quantum computer systems. An ultimate goal in the field is to do both — use smaller quantum-computer-based machine learning systems to better improve, understand, or interpret large datasets of quantum information or the results of large-scale quantum computer calculations. This last goal will of course have to wait till large-scale quantum information storage and full-fledged quantum computers come online. Google has said they want to make a 49-qubit quantum computer by year’s end, so a machine that’s the hundreds or thousands of qubits that might benefit from such secondary quantum technologies may still take years.
However, says Peter Wittek, research fellow at the Institute of Photonic Sciences in Castelldefels, Spain, researchers haven’t waited for super-duper quantum computers to begin experimenting and theorizing about the future of the field. Quantum machine learning, even in its earliest incarnations, still holds promise.
“To build universal quantum computers… is a big engineering challenge,” says Wittek, who’s also academic director at the Creative Destruction Lab startup incubator affiliated with the University of Toronto’s Rotman School of Management. “But it turns out for quantum machine learning you need something less.” Just like quantum cryptography and quantum random number generation have matured as technologies in the absence of big quantum computers, he says, so too might quantum machine learning find niches to expand into in the near term.
Wittek, author of the 2014 book Quantum Machine Learning: What Quantum Computing Means to Data Mining, says the field took off after a 2008 quantum algorithm called HHL (after its three creators Aram Harrow, Avinathan Hassidim, and Seth Lloyd). HHL solves vast linear algebra problems involving many degrees of freedom, potentially faster than could be solved on any traditional supercomputer. And since no small part of machine learning involves just these sorts of high-degree-of-freedom (high-dimension) algebra problems, some machine learning researchers have jumped on the HHL bandwagon. HHL-based quantum machine learning algorithms have proliferated in the technical literature over the past few years.
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