NVIDIA's GB200 NVL72 systems, equipped with fifth-generation multinode NVIDIA NVLink interconnect capabilities, are significantly accelerating progress in quantum computing. These systems are designed to handle computationally intensive tasks and are proving instrumental in developing better quantum algorithms, designing low-noise qubits, generating quantum training data, exploring hybrid applications, and unlocking quantum error correction. Developing Better Quantum Algorithms One of the primary challenges in quantum computing is creating efficient algorithms that can run on quantum processors. Simulating these algorithms helps researchers understand their performance before deployment. Ansys, for instance, is using the Gefion supercomputer to simulate algorithms for computational fluid dynamics, a task that is highly resource-intensive. The GB200 NVL72 system, leveraging NVIDIA cuQuantum libraries, achieves an 800x speedup compared to traditional CPU methods, making such simulations much more feasible. Designing Low-Noise Qubits Qubits, the fundamental units of quantum information, are highly susceptible to noise, which can degrade their performance. To design qubits that mitigate this issue, detailed physics simulations are essential. These simulations involve complex quantum mechanical calculations. With its high-bandwidth interconnect and cuQuantum's dynamics library, the GB200 NVL72 system offers a 1,200x speedup, thereby accelerating the design process for companies like Alice & Bob, which are pioneering low-noise qubit technology. Generating Quantum Training Data Quantum computing also benefits from AI, especially in controlling qubits and managing quantum states. However, training AI models for these tasks requires massive amounts of data, which can be costly or even impossible to obtain from existing quantum hardware. Simulated data from quantum processors serves as a viable alternative. The GB200 NVL72 can produce this data 4,000x faster than CPU-based methods, facilitating the integration of advanced AI techniques into quantum computing research. Exploring Hybrid Applications The future of quantum computing lies in hybrid systems that utilize both quantum and classical hardware to optimize processing efficiency. Researchers need a platform that can simulate quantum hardware while also offering powerful AI supercomputing capabilities. NVIDIA's CUDA-Q platform, which integrates GB200 NVL72, enables this by providing a seamless environment for testing hybrid algorithms. The system offers a 1,300x speedup, making it much easier to develop and test these hybrid applications. Unlocking Quantum Error Correction Quantum error correction (QEC) is critical for maintaining the integrity of qubit data. QEC algorithms run on classical hardware and must process vast amounts of data to correct errors continuously. The GB200 NVL72 system, with its high-performance capabilities, provides a 500x speedup in running these decoding algorithms, making QEC a practical reality for the future of quantum computing. Industry Impact and Future Vision These advancements in quantum computing are not isolated; they are part of a broader industry effort to create hybrid quantum-GPU supercomputers that can tackle complex commercial problems. For example, qubit builder Diraq is using the NVIDIA DGX Quantum reference architecture to connect silicon-based spins to NVIDIA GPUs, paving the way for scalable and reliable quantum hardware. Additionally, the NVIDIA CUDA-Q Academic program is bringing together researchers and institutions to push the boundaries of quantum-classical integration. NVIDIA founder and CEO Jensen Huang emphasized the importance of these efforts during his keynote at NVIDIA GTC Paris, held at VivaTech. He highlighted the company's commitment to integrating quantum hardware into all supercomputers, a vision driven by the potential of NVIDIA GB200 NVL72 systems. The GTC Paris sessions further explored how these systems are fostering innovation and collaboration within the quantum computing community. Industry Evaluation Industry experts laud NVIDIA's GB200 NVL72 systems for their transformative impact on quantum computing research. The significant speedups in algorithm simulation, qubit design, data generation, and error correction are breaking down barriers and accelerating the development of practical quantum applications. NVIDIA's role in this field is seen as pivotal, given its expertise in accelerated computing and the widespread adoption of its technologies by leading quantum hardware providers and academic researchers. The company's strategic partnerships and initiatives, such as the CUDA-Q program, are fostering a robust ecosystem for quantum innovation, ensuring that the journey to useful quantum computing remains on track.