The fusion of quantum computing and machine learning has become a booming research area. Matching the two could be the catalyst for the next chapter of 21st century innovation.
George Musser, author of Spooky Action at a Distance, asks if this match-up could ever live up to these high expectations. He once claimed that “the job of physics is to naturalise the supernatural. To domesticate magic and to pull it into the realm of natural explanation.” When it comes to the weird world of quantum mechanics, physicists grapple with this on a daily basis.
Quantum Machine Learning
Neural networks and quantum processors have one thing in common: It is amazing they work at all. It was never obvious that you could train a network, and for decades most people doubted it would ever be possible.
In the early 1990s, Elizabeth Behrman, a physics professor at Wichita State University, began working to combine quantum physics with artificial intelligence — in particular, the then-maverick technology of neural networks. Most people thought she was mixing oil and water. “I had a heck of a time getting published,” she recalled. “The neural-network journals would say, ‘What is this quantum mechanics?’ and the physics journals would say, ‘What is this neural-network garbage?’”
Today the mashup of the two seems the most natural thing in the world. Neural networks and other machine-learning systems have become the most disruptive technology of the 21st century. They out perform humans, beating us not just at tasks most of us were never really good at, such as chess and data-mining, but also at the very types of things our brains evolved for, such as recognising faces, translating languages and negotiating four-way stops.
These systems have been made possible by vast computing power, so it was inevitable that tech companies would seek out computers that were not just bigger, but a new class of machine altogether.
The Power of Quantum
After decades of research, quantum computers have nearly enough power to perform calculations beyond any other computer on Earth. Their killer app is usually said to be factoring large numbers, which are the key to modern encryption.
“Manipulation of large matrices and large vectors are exponentially faster on a quantum computer,” states Seth Lloyd, a quantum pioneer at the MIT.
Lloyd estimates that 60 qubits would be enough to encode an amount of data equivalent to that produced by humanity in a year, and 300 could carry the classical information content of the observable universe. (The biggest quantum computers at the moment, built by IBM, Intel and Google, have 50-ish qubits.)
Quantum Machine Learning
Rudimentary quantum processors are uncannily matched to the needs of machine learning. They manipulate vast arrays of data in a single step, pick out subtle patterns that classical computers are blind to, and don’t choke on incomplete or uncertain data.
Google, Microsoft, IBM and other tech giants are now pouring money into quantum machine learning, and a startup incubator at the University of Toronto is devoted to it.
“There is a natural combination between the intrinsic statistical nature of quantum computing and machine learning,” argues Johannes Otterbach, a physicist at Rigetti Computing, a quantum-computer company in Berkeley.
Musser recognises some tremendous challenges in the way.
The real frontier of machine learning is in generative models, which do not simply recognize puppies and kittens, but can generate novel archetypes — animals that never existed, but are every bit as cute as those that did. They might even figure out the categories of “kitten” and “puppy” on their own, or reconstruct images missing a tail or paw.
“These techniques are very powerful and very useful in machine learning, but they are very hard to achieve with a quantum computer,” revealed Mohammad Amin, the chief scientist at D-Wave.
Once you do manage to enter your data, you need to store it in such a way that a quantum system can interact with it without collapsing the ongoing calculation.
“That’s an additional huge technological problem beyond the problem of building a quantum computer itself,” says Scott Aaronson, a computer scientist at the University of Texas. “The impression I get from the experimentalists I talk to is that they are frightened. They have no idea how to begin to build this.” Aaronson is according to Musser, always the voice of sobriety when it comes to quantum computing.
“Neural networks and quantum processors have one thing in common: It is amazing they work at all. It was never obvious that you could train a network, and for decades most people doubted it would ever be possible,” Musser reveals.
“It is not obvious that quantum physics could ever be harnessed for computation, since the distinctive effects of quantum physics are so well hidden from us. And yet both work — not always, but more often than we had any right to expect. On this precedent, it seems likely that their union will also find its place.”
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