Ever since Charles Babbage's conceptual, unrealised Analytical Engine in the 1830s, computer science has been trying very hard to race ahead of its time. Particularly over the last 75 years, there have been many astounding developments – the first electronic programmable computer, the first integrated circuit computer, the first microprocessor. But the next anticipated step may be the most revolutionary of all. Quantum computing is the technology that many scientists, entrepreneurs and big businesses expect to provide a, well, quantum leap into the future. If you've never heard of it there's a helpful video doing the social media rounds that's got a couple of million hits on YouTube.
After decades of hype and headlines, quantum computers are finally poised to demonstrate their superiority over conventional machines. Precisely when this will happen is a bit fuzzy, though. What's more, it will be a while yet before these magical machines will have any noticeable impact on our lives. The point at which a quantum machine should be able to perform computations too complex to model on any conventional machine, a landmark known as "quantum supremacy," is believed to be about 49 qubits, the quantum equivalent of the bits that represent 1 or 0 in a conventional computer. Google's researchers appear to be leading in the race for a 49-qubit machine (see "Google's New Chip Is a Stepping Stone to Quantum Computing Supremacy").
The first third of the 20th century saw the collapse of many absolutes. Albert Einstein's 1905 special relativity theory eliminated the notion of absolute time, while Kurt Gödel's 1931 incompleteness theorem questioned the notion of absolute mathematical truth. Most profoundly, however, quantum mechanics raised doubts on the notion of absolute objective reality. Is Schrödinger's cat dead or alive? Nearly 100 years after quantum mechanics was introduced, scientists still are not in full agreement on what it means.
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.