Insider Brief
- Qilimanjaro Quantum Tech integrated NVIDIA CUDA-Q into QiliSDK, enabling GPU-accelerated quantum workflow emulation across classical and quantum computing environments.
- The update allows researchers to run quantum algorithms on CPUs, GPUs, digital quantum processors, and Qilimanjaro’s analog quantum processors through a single software framework.
- QiliSDK now includes CUDA-Q backends, noise models, and OpenQASM3 and QIR connectors, supporting hybrid quantum-classical workflows and code portability.
PRESS RELEASE — Qilimanjaro Quantum Tech’s software development toolbox QiliSDK is now integrated with NVIDIA CUDA-Q – the platform for quantum-classical computing. This upgrade provides researchers with GPU acceleration for production-scale emulation of quantum workflows on classical hardware. Qilimanjaro’s unique multimodal approach now extends across classical and quantum backends, from CPUs and GPUs to analog and digital QPUs.
This upgrade to QiliSDK is especially relevant for HPC centers. Those users—including the team at the Barcelona Supercomputing Center where Qilimanjaro has installed three quantum computers—regularly work with NVIDIA GPUs and now can use this hardware to execute large CUDA-Q simulations directly from QiliSDK. QiliSDK allows researchers to use the same library to run algorithms on real quantum hardware (digital and analog) and execute quantum emulation on classical resources.
“The future of computing will be multimodal, combining supercomputers with quantum acceleration, both digital and analog” said Marta P. Estarellas, CEO of Qilimanjaro Quantum Tech. “This upgrade to QiliSDK helps users to integrate all these classical and quantum modalities under a single entry point, bringing this multimodal vision closer to reality.”
Most quantum software runs on a single kind of quantum hardware, either digital or analog, or on classical emulators. QiliSDK runs across multiple backends: CPU, GPU, digital QPUs (dQPU) and Qilimanjaro’s analog QPUs (aQPU). This drives Qilimanjaro’s vision of the next phase of compute: the future of supercomputing will be multimodal.
“Every future supercomputer will draw on quantum processors to expand what’s possible with computing,” said Sam Stanwyck, Director of Quantum Product at NVIDIA. “To start building for tomorrow’s quantum-GPU supercomputers today, researchers need tools like QiliSDK, which taps CUDA-Q for the GPU-accelerated performance required to understand truly hybrid quantum-classical systems.”
QiliSDK is Qilimanjaro’s Python framework for developing, running and emulating both digital and analog quantum algorithms. Its modular design makes it easy to prototype circuits, build Hamiltonians, design variational workflows and quantum-reservoir models, then deploy them on local or remote backends, classical or quantum.
QiliSDK now includes a complete list of CUDA-Q backends and noise models, as well as QIR and OpenQASM3 connectors. This allows users from other software libraries to port their code and benefit from the integration.
Classical emulation is a vital part of how quantum teams work. Before running anything on real hardware, teams use emulation to prototype circuits, study system behavior, characterize noise, and establish the benchmarks that quantum results are measured against. It also handles the classical side of hybrid workflows, the pre- and post-processing that wraps every quantum call.
Adding the computational power of NVIDIA accelerated computing to this workflow adds more powerful tools to the process.
Emulating a quantum state on a classical computer takes exponentially large resources as the qubit count increases. On a CPU, this can be easily achieved up to about 25 qubits before hitting the limits of memory and bandwidth. At that point, run times go from seconds to hours. GPUs are built for this kind of workload with wide memory buses, massive parallel arithmetic and multi-GPU NVLink topologies. Beyond the strength of the individual GPU, adding tensor-network or distributed-memory methods add even more capacity. NVIDIA CUDA-Q provides a platform for developing hybrid quantum-classical workflows, including the most performant way to draw upon GPU-acceleration to support quantum computing workloads.
Find all the technical details in this blog post.
