All that glitters is not quantum AI
Why hasn’t the field of artificial intelligence created the equivalent of human intelligence? Is it because the problem, “artificial general intelligence,” isn’t well understood, or is because we just need much faster computers, specifically quantum computers?
The latter view is the source of a vibrant field of research, “Quantum Machine Learning,” or QML.
But a bit of skepticism is warranted.
“We need to look through a skeptical eye at the idea that quantum makes things faster and therefore can make machine learning advances,” says Jennifer Fernick, the head of engineering at NCC Group, a cyber-security firm based in Manchester, U.K.
Fernick was a keynote speaker a week ago at the O’Reilly A.I. conference in New York. She sat down this week to tell ZDNet why she’s skeptical about all the hype that’s emerging in the pairing of quantum and A.I.
“Right now, if we look at work in QML, people are experimenting with things such as, could we build a Support Vector Machine (SVM) or a Boltzmann Machine — can we build these existing canonical machine learning models — in the quantum machine,” observes Fernick. She is referring to two older models of machine learning that emerged in the 1990s, prior to today’s deep learning systems.
“That’s cool,” she says, “but it’s not inherently a revolution for A.I.”
Indeed, recent research by IBM has attempted to show that even today’s simple quantum systems, such as a 2-qubit model, can theoretically go well beyond what “classical” computers using the flow of electrons can compute.
Also: Is IBM’s AI demonstration enough for a quantum killer app?
The IBM work is part of a recent craze to find uses for quantum computing before large systems are commercially viable. The trends is known as “shallow quantum circuits,” also referred to as “Noise Intermediate State Quantum Devices,” or “NISQ.”
However, attempts in NISQ to speed up a shallow machine learning task, such as SVM or Boltzmann Machines, may not really be achieving much, she reflected.
“Quantum computing can make certain things faster if the underlying math has a structure that is exploitable via quantum and we have the right quantum algorithms,” she says. “Before we jump on the bandwagon, we need to ask, What are the true algorithmic innovations?”
In the case of cryptography, one of Fernick’s areas of focus as a security specialist, quantum computing is “clearly worth it,” she says.
A quantum computer can render trivial the operation of “factoring” a given number into its component prime numbers where a classical computer would find it impossible.
“For machine learning, I feel like we’ve translated that same enthusiasm with cryptanalysis, but there is not even the theoretical demonstration that we are going to have that same impact,” says Fernick.
Fernick’s skepticism finds inspiration in the relatively young field of computational complexity theory. In particular, she is enamored of the work of Massachusetts Institute of Technology’s Scott Aaronson, who is associate professor of electrical engineering and computer science.
Aaronson, whom Fernick deems the most interesting mathematician alive, has pointed out that simply being able to speed up computing of a given learning model in A.I. may not be the key to artificial general intelligence. Is it the case that simulating a human mind is an operation that requires exponential computing time, or is it not? he asks. If it were, it might be the case that the speed-up from quantum would genuinely be an advantage.
