The surprising pit stop on the road to AI quantum computing integration
Blockchain is the “first step” here, according to D-Wave Quantum CEO Dr. Alan Baratz.
D-Wave Quantum’s recent claim of “quantum supremacy” in identifying optimal sensors was noteworthy beyond the flashy headline and scientific results. Per the company, it would’ve required more than the world’s annual electricity consumption for a supercomputer to have solved the problem (oh, and nearly 1 million years).
When I do a 30,000-foot scan of the tech space, I see artificial intelligence (which requires so much compute and energy) and quantum computing (which is seemingly offering lots of compute with less energy required). It just makes you want to set them up on a blind date and look forward to attending their wedding a year later.
D-Wave Quantum CEO Dr. Alan Baratz is certainly excited by the prospect of getting more involved in AI. The company’s recent spike in bookings was fueled by the order of a quantum computer that’s going to be hooked up to a supercomputer in Germany “to explore new workflows in a few different areas, but especially AI,” Baratz said.
He added something that made our eyebrows go up, though, by saying that recent experiments between quantum computing and blockchain technology are a key pit stop on the path to greater integration of quantum computing and AI.
When we asked him why these seeming soulmates hadn’t already gotten hitched, he told us:
“There’s a first step along the way: blockchain. We also posted a paper on the archive last week where we showed how you could take the computation we did to demonstrate supremacy and use it to create a hashing function. Everybody knows that blockchain and cryptocurrency are based on hashing functions. And then we showed how you could use this quantum hashing function to actually build a blockchain...
Now, what’s so important about quantum proof of work is a) it’s kind of by construction quantum-safe. But b) it’s much more energy efficient. This will consume far less energy to do the hashing and the mining than what happens today, for example, with bitcoin, which is a massive energy consumer.
So we’re very excited about this because if magnetic materials aren’t approachable enough, blockchain and cryptocurrency should be, and the fact that we’re now talking about and demonstrating a quantum blockchain based on our quantum computers — that’s pretty exciting. And that’s the first step toward low energy computation.
Now we are also working on how to apply these technologies in AI and machine learning. And we started working on how to integrate our quantum systems with GPUs to do more energy efficient model training. We’ve got some good early results, but on small datasets. We’re just now starting to work on larger datasets and we’re going to move that forward, and especially once we get the Julich system integrated with the GPU system there, we should be able to push that ball even further.”
It’s true that one thing mining bitcoin and AI data centers have in common is that they require a boatload of energy, and if quantum can solve for the notoriously energy-sucking mining process, it’s not impossible to imagine it might do the same for AI training and inference, though they are different problems to solve.
