Zapata Computing, Inc. (Zapata AI) has published research in Nature Communications demonstrating how quantum and classical techniques can work together to enhance generative AI. The paper, titled “Synergistic pretraining of parametrised quantum circuits via tensor networks,” shows how quantum circuits can extend the capabilities of classical generative AI. The research, led by CEO Christopher Savoie and Quantum Research Scientist Jacob Miller, suggests that quantum techniques can significantly benefit enterprise generative AI applications. Zapata AI is a Boston-based company specialising in industrial generative AI, and is set to become a publicly listed company on the New York Stock Exchange.
Zapata AI’s Quantum-Enhanced Generative AI Research Published in Nature Communications
Zapata Computing, Inc., also known as Zapata AI, a company specializing in Industrial Generative AI, has announced the publication of its research on quantum-enhanced Generative AI in the esteemed Nature Communications journal. The paper, titled “Synergistic pretraining of parametrized quantum circuits via tensor networks,” explores how quantum circuits can augment and supplement the capabilities of classical generative AI. The research was published online on December 15th.
Zapata AI’s Approach to Quantum and Classical Techniques
Christopher Savoie, CEO and co-founder of Zapata AI, expressed pride in the researchers who contributed to this pioneering work. He emphasized that quantum techniques could bring significant benefits to enterprise generative AI applications. The research demonstrates how to optimally utilize current resources to realize these benefits. Savoie stressed that the focus should not be on quantum versus classical, but on how the two can be used synergistically to achieve better results more quickly. He expressed anticipation about applying this research in their work with enterprise customers.
Quantum Techniques for Generative AI
The research builds on Zapata AI’s expanding portfolio of quantum techniques for generative AI. These quantum techniques offer several advantages for enterprise problems, including compressing large, computationally expensive models; accelerating time-consuming and costly calculations; and producing 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.
The Synergistic Approach of Quantum and Classical Computers
Jacob Miller, Quantum Research Scientist at Zapata AI, stated that their work combines the complementary strengths of quantum and classical computers to achieve better results than either type of hardware on its own. He noted that people often perceive quantum and classical technologies as competitors, but their research shows that classical methods can help overcome a significant limitation in the optimization of quantum devices. Miller expressed hope that their “synergistic” approach could begin to unlock the true potential of present-day quantum technologies for solving intractable computational problems.
The Role of Tensor Networks in Quantum Algorithms
Jing Chen, a Senior Quantum Scientist at Zapata AI who co-authored the paper, explained how tensor networks, traditionally used in classical algorithms, form a critical bridge to quantum algorithms, offering a unique synergy. This integration not only enhances both fields but also notably alleviates the challenges of barren plateaus in quantum computing. Chen emphasized that their approach encourages collaboration, leveraging the strengths of classical and quantum methods to address complex problems more effectively.
About Zapata AI
Zapata AI is revolutionizing how enterprises solve their most challenging problems with its robust suite of Generative AI software. By combining numerical and text-based solutions, Zapata AI enables industrial-scale commercial, government, and military/defense enterprises to leverage large language models and numerical generative models more effectively. The company, founded in 2017 and headquartered in Boston, Massachusetts, entered into a definitive business combination agreement with Andretti Acquisition Corp. (NYSE: WNNR) on September 6, 2023. Subject to customary closing conditions, this will result in Zapata AI becoming a publicly listed company on the New York Stock Exchange.
“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.”
“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.”
Quick Summary
Research published in Nature Communications demonstrates how quantum and classical techniques can work together in generative AI to deliver benefits unachievable with either method alone. The study shows how quantum circuits can enhance classical generative AI, offering potential advantages such as compressing large models, accelerating calculations, and producing more diverse, high-quality outputs.
- Zapata Computing, Inc., also known as Zapata AI, has had its research on quantum-enhanced Generative AI published in the journal Nature Communications.
- The research, titled “Synergistic pretraining of parametrised quantum circuits via tensor networks,” demonstrates how quantum circuits can enhance the capabilities of classical generative AI.
- The research suggests that quantum and classical techniques can work together to deliver benefits not possible with either approach alone.
- The work is part of Zapata AI’s growing portfolio of quantum techniques for generative AI, which offer several advantages for enterprise problems.
- The research was led by Christopher Savoie, CEO and co-founder of Zapata AI, and Jacob Miller, Quantum Research Scientist at Zapata AI. Other contributors include Jing Chen, a Senior Quantum Scientist at Zapata AI, Manuel Rudolph, Daniel Motlagh, Atithi Acharya, and Alejandro Perdomo-Ortiz.
- The research shows that tensor networks, traditionally used in classical algorithms, can form a critical bridge to quantum algorithms, offering a unique synergy.
- Zapata AI was founded in 2017 and is based in Boston, Massachusetts. It is set to become a publicly listed company on the New York Stock Exchange following a business combination agreement with Andretti Acquisition Corp.