Zapata Chief Says Quantum Machine Learning Is a When, Not an If

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Quantum machine learning is a matter of when, not if, according to quantum startup leader. (Image: Flickr Image via www.vpnsrus.com)

Zapata CEO Christopher Savoie told Venture Beat that it’s an only matter of time before businesses begin to officially use quantum machine learning, something that likely will change the trajectories of both quantum computing and artificial intelligence (AI).

“AI itself, but more appropriately machine learning, already has a very horizontal applicability,” Savoie told the magazine. “But the places where quantum is going to really help, I think, initially, one of the main places is in generative modeling. The GANs, time course data, and this kind of thing.”

Healthcare may be one area where this quantum computing-machine learning hook-ups happen.

“Say you have 100 patients with a very rare form of lung cancer. You will be able to deepfake 1,000 of those MRI results,” he said. “With the distributions that you’re able to model with a quantum computer that you can’t do classically, you’ll be able to not only detect but reproduce features in datasets and create artificial data sets that will help you train machine learning models a lot better and a lot more accurately with fewer samples.”

The CEO said that he expects quantum-backed machine learning to be faster, more powerful and more accurate.

“So this is going to impact all of machine learning, pretty much,” Savoie said. “The ability to sample from probability distributions that would take you 10,000 years on a classical computer, even a powerful supercomputer classical computer is going to really change the world of how accurate our models are going to be and how long it takes to train them and how many samples it takes to train them to the same level of accuracy.”

Savoie may be so confident about the coming of quantum ML-AI because Zapata customers are already experimenting with it. He told Venture Beat that an unnamed client is currently developing a system for optimization work using machine learning and quantum-inspired algorithms.

He expects these algorithms to be into production “by the end of this year or early next year.”

Even though quantum computers aren’t quite powerful enough to replace classical AI-ML, that hasn’t stopped developers from preparing for the eventuality. Zapata’s preparing classical algorithms that easily converts to a quantum backend once “the qubits are there.”

Savoie said, “So you can be forward compatible and backward compatible with all your data analytics, all the data preparation and that stuff. You don’t have to repeat, you don’t have to rip it up and start over again. It’s literally changing a couple lines of code to flip out the back end.”

According to Savoie, no customer is currently running quantum machine learning algorithms, but gave the magazine an idea of the timeline.

“Within the next year, likely, these will be quantum-inspired, classical backends,” Savoie said. “Within the next, I would say between two to five years, I won’t give you an exact timeframe — the power of these quantum computers, if they keep going on this trajectory — we will be swapping out that backend. Developing the algorithms, which are a bit different for those backends, is ongoing now. Companies are investing in creating those algorithms, because it’s imminent. Nobody will put a timeframe on it. I wish I could for you. I can’t. But in some ways, it doesn’t matter, right? Is it two years, three years, five years — it’s in the mid-term business plan that that disruption is going to happen.”