QuantrolOx will test its tools on Berkeley’s qubit platform

A five-year collaboration between UC Berkeley’s Department of Physics and QuantrolOx aims to move quantum computing beyond laboratory experimentation and toward industrial-scale manufacturing. The agreement unites Berkeley’s superconducting quantum research with QuantrolOx’s machine-learning-based software for automated qubit control, focusing on standardized workflows and a common data architecture. Professor Irfan Siddiqi of UC Berkeley highlights that “the white-box superconducting qubit platform we have here at Berkeley is an ideal test environment for developing QuantrolOx’s commercial tools,” indicating a crucial proving ground for the technology’s viability.

UC Berkeley & QuantrolOx Advance Quantum Industrialization

The pursuit of practical quantum computers received a significant boost as UC Berkeley and QuantrolOx formalized a five-year collaboration focused on scaling quantum technology beyond academic labs. This partnership establishes a framework designed to address the challenges of quantum industrialization, encompassing materials science, processor development, and automated testing procedures.

The agreement unites Berkeley’s strengths in superconducting qubit research with QuantrolOx’s expertise in machine-learning-driven qubit control software, demonstrating a commitment to building standardized, reproducible quantum systems. Central to this effort is the Roger Herst Quantum Nexus at UC Berkeley, which will serve as a physical collaboration space to accelerate progress and facilitate workforce training.

Professor Irfan Siddiqi, Department of Physics at the University of California, Berkeley, emphasized the importance of bridging the gap between academia and industry, stating, “Bringing the strengths of academia together with industry in an open collaboration is by far the most efficient path to a quantum computer relevant to real-world applications.” Siddiqi specifically highlighted the value of Berkeley’s “white-box superconducting qubit platform” as a proving ground for QuantrolOx’s tools, designed for the characterization, calibration, and control of quantum devices. This platform is not simply a research tool, but a critical element in validating the commercial viability of QuantrolOx’s software.

QuantrolOx’s approach focuses on hardware-agnostic, machine-learning-based solutions, a departure from the bespoke calibration methods currently dominating quantum research. The company intends to develop automated workflows and a shared data architecture to streamline the entire quantum lifecycle, from materials research to operational runtime.

This emphasis on standardization is crucial for scaling quantum computing, as manual, laboratory-based processes are inherently limited in their ability to produce consistent, reliable results. Vishal Chatrath, CEO of QuantrolOx, articulated this need for a broader approach, saying, “Quantum computing will not scale on laboratory heroics alone. Industrialization requires common tools, automated workflows, shared data architectures, and a skilled workforce.” The collaboration will also explore the application of artificial intelligence to automate quantum control and calibration, aiming for both speed and accuracy.

Researchers will investigate AI designed to provide physics-aware assistance to quantum engineers, and methods for training a workforce capable of operating these complex systems at scale. Beyond the technical aspects, the Memorandum of Understanding (MOU) recognizes the importance of knowledge sharing, with plans for seminars, workshops, and community events hosted at the Roger Herst Quantum Nexus.

This commitment to open scientific exchange underscores the collaborative spirit driving this initiative and the shared goal of overcoming scalability bottlenecks in quantum computing. The MOU is non-binding, with specific research and commercial engagements to be defined through separate agreements, but it lays a solid foundation for a sustained effort to bring quantum technology closer to practical realization.