A Hybrid of Quantum Computing and Machine Learning Is Spawning New Ventures

At the intersection of two challenging computational and technological problems may lie the key to better understanding and manipulating quantum randomness

4 min read
At the intersection of two challenging computational and technological problems, may lie the key better understanding and manipulating quantum randomness
Illustration: : Richard Kail/Science Photo Library

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.

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The Femtojoule Promise of Analog AI

To cut power by orders of magnitude, do your processing with analog circuits

12 min read
A list of characters and letters in blue, white black and yellow.

Machine learning and artificial intelligence (AI) have already penetrated so deeply into our life and work that you might have forgotten what interactions with machines used to be like. We used to ask only for precise quantitative answers to questions conveyed with numeric keypads, spreadsheets, or programming languages: "What is the square root of 10?" "At this rate of interest, what will be my gain over the next five years?"

But in the past 10 years, we've become accustomed to machines that can answer the kind of qualitative, fuzzy questions we'd only ever asked of other people: "Will I like this movie?" "How does traffic look today?" "Was that transaction fraudulent?"

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