The paper demonstrates how quantum and
classical techniques for generative AI can work synergistically to
deliver advantages not possible with either approach in
isolation.
Zapata Computing, Inc. (“Zapata AI”), the Industrial Generative
AI company, today announced that its research in quantum-enhanced
Generative AI has been published in the prestigious Nature
Communications journal. The article, titled “Synergistic
pretraining of parametrized quantum circuits via tensor networks,”
demonstrates how quantum circuits can extend and complement the
capabilities of classical generative AI.
The research was published online on December 15th and can be
accessed here.
“We are extremely proud of the talented researchers who
contributed to this groundbreaking work,” said Christopher Savoie,
CEO and co-founder of Zapata AI. “Quantum techniques can bring
tremendous advantages to enterprise generative AI applications, and
this research shows how we can make the most of the resources we
have today to realize those advantages. It is no longer a question
of quantum vs. classical, but rather how the two can be used
synergistically together to get better results, faster. We are
looking forward to applying this research in our work with
enterprise customers.”
The work builds on Zapata AI’s growing portfolio of quantum
techniques for generative AI. These quantum techniques offer
several advantages for enterprise problems, including compressing
large, computationally expensive models; speeding up time-consuming
and costly calculations; and more diverse, higher quality outputs
for generative AI. More details on how quantum science can enhance
generative AI can be found in a recent Zapata AI blog post.
“Our work combines the complementary strengths of quantum and
classical computers to reach better results than either type of
hardware on its own,” said Jacob Miller, Quantum Research Scientist
at Zapata AI. “People often think that quantum and classical
technologies are in competition with each other, but we show that
classical methods can actually help overcome a major limitation in
the optimization of quantum devices. We hope our “synergistic”
approach can start to unlock the true potential of present-day
quantum technologies for solving intractable computational
problems.”
“In our Nature Communications article, we showcase how tensor
networks, traditionally used in classical algorithms, form a
critical bridge to quantum algorithms, offering a unique synergy,”
said Jing Chen, a Senior Quantum Scientist at Zapata AI who
authored the paper along with Manuel Rudolph, Jacob Miller, Daniel
Motlagh, Atithi Acharya, and Alejandro Perdomo-Ortiz. “This
integration not only enhances both fields but also notably
alleviates the challenges of barren plateaus in quantum computing.
Our approach fosters collaboration, leveraging the strengths of
classical and quantum methods to address complex problems more
effectively.”
About Zapata AI:
Zapata AI is the Industrial Generative AI company,
revolutionizing how enterprises solve their hardest problems with
its powerful suite of Generative AI software. By combining
numerical and text-based solutions, Zapata AI empowers
industrial-scale commercial, government and military/defense
enterprises to leverage large language models and numerical
generative models better, faster, and more efficiently—delivering
solutions to drive growth, savings and unprecedented insight. With
proprietary science and engineering techniques and the Orquestra®
platform, Zapata AI is accelerating Generative AI’s impact in
Industry. The Company was founded in 2017 and is headquartered in
Boston, Massachusetts. On September 6, 2023, Zapata AI entered into
a definitive business combination agreement with Andretti
Acquisition Corp. (NYSE: WNNR), the consummation of which, subject
to customary closing conditions, will result in Zapata AI becoming
a publicly listed company on the New York Stock Exchange. To learn
more, visit: https://www.zapata.ai
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