Imagine no longer needing to stand next to a giant supercomputer to dive into quantum research. Thanks to [Quantum Brilliance’s](https://quantumbrilliance.com/) virtual Quantum Processing Unit (vQPU), you can now explore quantum computing applications from wherever you are — whether that’s a standard workstation, a remote HPC cluster, or the cloud. This advancement emulates the experience of real quantum hardware, meaning you can develop, test, and refine advanced algorithms without being tied to specialized facilities or cryogenic machines.
Quantum Brilliance, a diamond-based, room-temperature quantum computing company, and the Pawsey Supercomputing Research Centre have announced a milestone in quantum-classical integration. Its new hybrid workflow fuses CPU, GPU, and quantum resources into a single job-scheduling environment, enabling scientific and industrial communities to explore practical quantum-enhanced applications without overhauling existing HPC infrastructure.
Unlike many quantum computing approaches reliant on cryogenic environments, Quantum Brilliance’s technology is based on small-form-factor, room-temperature quantum processors made of diamond. This unique capability allows their hardware to be placed directly within data centers, more efficiently bridging quantum and classical systems.
> “What we’ve developed is essentially a conductor for a technological orchestra, where quantum and classical computers can work in harmony to solve complex problems. Previous approaches focused on quantum algorithms in isolation, but real-world problems require seamless integration of multiple computing technologies.” — Dr. Pascal Elahi, Quantum Team Lead at Pawsey
A highlight of the collaboration is Quantum Brilliance’s virtual Quantum Processing Unit (vQPU). Designed for flexibility, it emulates the user experience of physical quantum processors—complete with realistic noise modeling and shot representation — while operating at scale on [NVIDIA GH200 Grace Hopper Superchips](https://www.nvidia.com/en-us/data-center/grace-hopper-superchip/). This design lowers barriers for researchers testing new quantum algorithms: They can prototype on vQPUs and seamlessly transition to physical quantum hardware when ready.
“This novel hybrid workflow demonstrates that accelerated computing is critical for advancing quantum computing,” said Sam Stanwyck, Group Product Manager for Quantum Computing at NVIDIA. “Our collaboration with Quantum Brilliance and Pawsey brings the industry one step closer to running genuinely useful quantum applications in an environment that makes sense for HPC users worldwide.”
Researchers can deploy quantum-classical hybrid jobs with minimal reconfiguration thanks to the integration with standard HPC tools — such as the SLURM job scheduler. Pawsey’s approach is a universal adapter, enabling HPC clusters to connect multiple processor types under a single, flexible framework.
“By successfully integrating our virtual QPU into Pawsey’s workflow, we’re demonstrating that quantum computing is not just theoretical — it is set to become a practical tool for solving real-world problems,” said Andrea Tabacchini, VP of Quantum Solutions at Quantum Brilliance.
“This dynamic virtual-physical hybrid capability positions Australia at the forefront of quantum and supercomputing convergence, strengthening national infrastructure and quantum technology leadership.”
Initially targeted at radio astronomy data processing, AI workflows, and bioinformatics, the hybrid solution benefits any field dealing with large data sets and complex models. With the success of this first phase, the next step involves deploying the new workflow on Pawsey’s Setonix supercomputer. Using the same scheduling system, researchers will access the vQPU and a physical quantum computer.
This collaboration marks an important stride toward bringing quantum computation into day-to-day research environments. It offers an accessible, scalable, and practical route to the real-world benefits of quantum technology by granting seamless, on-demand access to CPUs, GPUs, and QPUs.