insider brief
- Zapata AI Researcher shows how quantum circuits can extend and complement the capabilities of classical generative AI.
- The researchers published their findings in the journal Nature Communications.
- This effort builds on Zapata AI’s growing portfolio of quantum technologies for generative AI.
press release – Zapata Computing, Inc., an industrial generative AI company, announced that its quantum-enhanced generative AI research has been published in a prestigious journal. nature communications journal. This article entitled “Synergistic pre-training of parameterized quantum circuits via tensor networks” shows how quantum circuits can extend and complement the capabilities of classical generative AI .
The study was published online on December 15th.th and can be accessed here.
“We are extremely proud of the talented researchers who contributed to this groundbreaking research,” said Christopher Savoie, CEO and co-founder of Zapata AI. “Quantum technology has the potential to bring tremendous benefits to enterprise-generated AI applications, and this research shows how we can best leverage the resources we currently have to realize those benefits. The question is not quantum or classical, but how the two can be used together to get better results faster. We are applying this research to our work with enterprise customers. I look forward to applying it.”
This effort builds on Zapata AI’s growing portfolio of quantum technologies for generative AI. These quantum techniques offer several advantages for enterprise problems, such as compression of large and computationally expensive models. Speed up time-consuming and costly calculations. More diverse and high-quality output for generative AI. A recent paper details how quantum science can power generative AI. Zapata AI blog post.
said Jacob Miller, Quantum Research Scientist at Zapata AI. “Our research combines the complementary strengths of quantum and classical computers to achieve better results than using either type of hardware alone.” “Quantum and classical techniques are often considered to be in competition with each other, we show that classical techniques can indeed help overcome major limitations in quantum device optimization. We hope that our ‘synergistic’ approach can unlock the true potential of current quantum technologies for solving difficult computational problems. ”
“This article in Nature Communications shows how tensor networks, traditionally used in classical algorithms, provide an important bridge to quantum algorithms and offer unique synergies,” said Zapata AI, author of the paper. said senior quantum scientist Jing Chen. Manuel Rudolph, Jacob Miller, Daniel Motlag, Atiti Acharya, Alejandro Perdomo-Ortiz. “This integration not only strengthens both fields, but also significantly alleviates the challenges of quantum computing’s barren plateau. Our approach fosters collaboration and leverages the strengths of classical and quantum methods. and effectively address complex problems.”
