Zapata raises $38 million for quantum machine learning Posted on : Nov 19 - 2020

Zapata Computing has raised $38 million for its quantum computing enterprise software platform. The figure, which brings its total funding to over $64 million, will be put toward Zapata’s core mission: “Delivering quantum advantage for customers through real business use cases.”

Quantum computing leverages qubits (unlike bits that can only be in a state of 0 or 1, qubits can also be in a superposition of the two) to perform computations that would be much more difficult, or simply not feasible, for a classical computer. Unlike most quantum computing startups that build the hardware, Zapata is focused on the algorithms and software that sit on top. Based in Boston, Zapata has one product: its hardware-agnostic Orquestra quantum computing platform. Enterprises can use Orquestra to figure out where quantum computing makes sense for them, without worrying about the nuts and bolts underneath.

Earlier this year, Zapata CEO Christopher Savoie told VentureBeat that the quantum computing and machine learning business use case is “a when, not an if.” Indeed, while the 58-person company plans to continue its work on optimization and simulation, the team believes “the nearest-term quantum use cases are in machine learning.”

Quantum Machine Learning

Zapata uses quasi quantum systems — which emulate quantum behavior on classical computers called Noisy Intermediate Scale Quantum (NISQ) devices — to woo potential customers. Orquestra requires changing only a couple of lines of code to swap out the backend from NISQ for an actual quantum system.

“In the near-term, Quantum Machine Learning (QML) appears to be the application most compatible with the NISQ devices in use today,” Savoie told VentureBeat. “Recent advances in promising quantum machine learning applications include natural language processing and generative adversarial networks (GANs), which are used to generate data indistinguishable from real-world data (see these photorealistic images as an example). One of the most valuable outcomes of quantum-powered GANs is the ability to fill gaps in data used to train machine learning models by creating synthetic data that falls within the probability range of existing data. Augmenting training data in this way could one day improve the ability of machine learning models to detect rare cancers or model rare events such as pandemics.” View More