(EurekaAlert) A new AI-based toolset developed by scientists at Q-CTRL enables quantum computers to optimize their own performance autonomously without user intervention.
The fundamental building blocks of quantum algorithms are extremely susceptible to errors, posing the most substantial barrier to progress in quantum computing. Q-CTRL’s new tools use custom AI agents to enact algorithms with fewer errors and ultimately better performance for quantum computing end users.
Available as a new feature on the company’s flagship BOULDER OPAL software, the Automated Closed-Loop Hardware Optimization tool leverages AI to achieve unparalleled results from quantum computers without users needing a detailed understanding of the hardware.
The feature is a major step towards fully abstracted quantum computer hardware powered by AI, according to Michael J. Biercuk, founder and CEO of Q-CTRL, a startup that applies the principles of control engineering to accelerate the development of quantum technology.
The feature is a major step towards fully abstracted quantum computer hardware powered by AI, according to Michael J. Biercuk, founder and CEO of Q-CTRL, a startup that applies the principles of control engineering to accelerate the development of quantum technology.
BOULDER OPAL is an advanced Python-based toolkit used by research teams at national laboratories, top-tier universities, and private-sector quantum companies to integrate state-of-the-art quantum control into their research and product engineering.
“This new feature is both an exciting technical and strategic innovation,” said Ben Yu, managing partner of Sierra Ventures. “It’s the first step we’ve seen in the industry towards truly removing the need for expert human intervention at the hardware level. This powerful new feature validates our view that building hardware abstractions is key to the development of the quantum computing sector, as well as our belief in Q-CTRL’s continuing industry leadership in this area.”

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