Quantum Machines Announces Deep Quantum-Classical Integration to Power Quantum-Accelerated Supercomputers With NVIDIA
The first deployment of the combined solution is expected at the end of this year
Tel Aviv, Israel, Mar 21, 2023 — Quantum Machines (QM), the provider of breakthrough quantum control solutions that accelerate the realization of practical quantum computers, announced today it is collaborating with NVIDIA on a first-of-its-kind architecture for high-performance and low-latency quantum-classical computing. The NVIDIA DGX Quantum brings together the world’s most powerful accelerated computing platform with the world’s most advanced quantum control solution to create the first purpose-built infrastructure for industrial-scale quantum-accelerated computing.
“We are proud to work together with NVIDIA, a true market leader in its field, in order to accelerate the realization of useful quantum computers.”
Itamar Sivan, Co-founder and CEO of Quantum Machines
Industries that currently rely on the power of supercomputers will be the first to benefit from a quantum speedup for applications like drug discovery, finance and optimization. Efforts to achieve this are already underway — the developer of one of the world’s fastest supercomputers recently provided an update on its effort to develop hybrid quantum-classical algorithms, and another announced the intention for broad integration of quantum capabilities in the next couple of years. But there are significant technical challenges in integrating and embedding quantum computers into supercomputers, and it cannot be achieved at scale with existing hardware.
The DGX Quantum system tightly integrates QM’s OPX+ quantum controller with the NVIDIA Grace Hopper Superchip to create a quantum-accelerated supercomputing system. This is the first purpose-built hardware that can enable a quantum speedup at the scale and performance required for world-leading supercomputers. DGX Quantum also supports NVIDIA’s open-source CUDA Quantum programming model, which enables the integration and programming of quantum processing units (QPUs), GPUs and CPUs in one system. The Israel Quantum Computing Center, which is being built by a consortium of companies led by Quantum Machines, will be the site of the first deployment of DGX Quantum at the end of this year.
“The DGX Quantum system will serve to lower the threshold for integrated HPC-QC infrastructure and allow it to scale faster to meet growing demand.”
Itamar Sivan, Co-founder and CEO of Quantum Machines
This first-of-its-kind integration will enhance real-time error correction, advancing the development of quantum computers, and thus creating a virtuous cycle of improvements on both the quantum and classical sides. Additionally, DGX Quantum provides a unified quantum-classical architecture that can facilitate the co-scheduling of resources for optimal utilization.
“We are proud to work together with NVIDIA, a true market leader in its field, in order to accelerate the realization of useful quantum computers,” said Itamar Sivan, co-founder and CEO of Quantum Machines. “The DGX Quantum system will serve to lower the threshold for integrated HPC-QC infrastructure and allow it to scale faster to meet growing demand.”
“Our collaboration with Quantum Machines on the integration of quantum computing with GPU supercomputing and the first purpose-built hardware that can deliver real-time quantum error correction is bringing useful quantum computing one step closer to reality,” said Tim Costa, director of high performance computing and quantum at NVIDIA.
DGX Quantum benefits from the advanced capabilities of the two solutions it leverages, resulting in a system that is at the cutting edge on both the quantum and the HPC fronts. DGX Quantum is scalable to quantum processors with any number of qubits, supports qubits of all types and is industry-leading in timing, precision and latency. The classical compute of DGX Quantum accelerates applications with the strengths of both GPUs and CPUs while providing a simple and productive heterogeneous programming model.
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