D-Wave: Quantum computing and machine learning are ‘extremely well matched’
Following D-Wave’s announcement of Leap 2, a new version of its quantum cloud service for building and deploying quantum computing applications, VentureBeat had the opportunity to sit down with Murray Thom, D-Wave’s VP of software and cloud services. We naturally talked about Leap 2, including the improvements the company hopes it will bring for businesses and developers. But we also discussed the business applications D-Wave has already seen to date.
Quantum computing leverages qubits to perform computations that would be much more difficult, or simply not feasible, for a classical computer. Based in Burnaby, Canada, D-Wave was the first company to sell commercial quantum computers, which are built to use quantum annealing. Applications include everything from cryptography and optimization to machine learning and materials science. In fact, D-Wave has a webpage dedicated to quantum computing applications including airline scheduling, election modeling, quantum chemistry simulation, automotive design, preventative health care, logistics, and more.
Thom explained that D-Wave has seen success particularly with optimization and machine learning use cases. And he has the data to back it up: D-Wave’s customer applications are about 50% optimization, 20% AI and ML, 10% materials science, and 20% other.
Optimization vs. machine learning
Optimization applications leading the pack makes sense because they’re currently largely solved using brute force and raw computing power. If quantum computers can quickly see all the possible solutions, an optimal solution can become apparent more quickly. “Optimization stands out because it’s much more intuitive and easier to grasp,” Thom added.
Another reason optimization is ahead comes down to the interested parties.
“The community of people who can incorporate optimization and robust optimization is much, much larger,” Thom explained. “The machine learning community — the congruence between the technology and the needs are very technical; they’re only applicable to statisticians. And there’s a much smaller community of statisticians in the world than there are of programmers.”
In particular, the complexity of incorporating quantum computing into the machine learning workflow presents an obstacle. “For machine learning practitioners and researchers, it’s very easy to figure out how to program the system. Fitting that into a machine learning workflow is more challenging because machine learning programs are becoming quite multifaceted,” he said. “But our teams in the past have published a lot of research on how to incorporate it in a training workflow that makes sense.”
Indeed, Thom noted that ML practitioners currently want someone else to handle the quantum computing part: “When I’ve gone out and talked to the machine learners, they’re looking for somebody else to do the legwork of building the frameworks up to the extensions and showing that it can fit.”
Adoption will take time
Nonetheless, Thom believes quantum computing and machine learning are “extremely well matched. The features the technology has and the needs of the field are very close.”
“It’s something I think is going to be a very productive use of the technology in the future because there’s so many aspects of what the quantum computers can do in terms of the probabilistic sampling,” Thom continued. “For optimization, the probabilistic sampling is like ‘oh, I can do robust optimization with that.’ But for machine learning it’s essential for what you need to do. It’s very hard to reproduce that with a classical computer and you get it natively from the quantum computer. So those features can’t be accidental. It’s just that it’s going to take time for the community to find the right methods for incorporating it and then for the technology to insert into that space productively.”
We’ve seen AI and quantum computing collide before, but this is a much bigger nod of approval. Still, D-Wave is just one quantum computing company. We reached out to a few others to find out if they had seen similar numbers.
Rigetti doesn’t have a breakdown of use cases its customers fall into. IonQ explained that while it is helping companies most notably across energy, pharma, and manufacturing, it also does not have an exact breakdown of customers by use case. IBM did not respond in time for publishing. D-Wave says that quantum computing and machine learning are a good fit. If you think otherwise, let us know.
Source: Read Full Article