On May 30, local time, Nvidia said it would begin building classical-quantum hybrid computers.
A classical-quantum hybrid computer refers to a hybrid system in which quantum and classical computers can work together.
Quantum computing has the potential to solve complex problems ranging from drug discovery to weather forecasting in the future, and play an important role in the development of high-performance computing.
Today, supercomputers can simulate the work of quantum computing at scale and performance beyond that of relatively small, error-prone quantum systems. Therefore, classical high-performance computing systems can help researchers in quantum computing research.
NVIDIA announced at GTC 2022 that the NVIDIA cuQuantum software development kit for accelerating quantum computing has been fully released and showcases the results cuQuantum has achieved over a year. The software development kit was officially released at the 2021 GTC conference.
Amazon Cloud Technologies (AWS) recently announced the availability of cuQuantum in its Amazon Braket quantum computing service, and demonstrated at Braket how cuQuantum can provide acceleration on quantum machine learning. Quantum machine learning is an emerging subfield within the field of quantum information that combines the speed of quantum computing with the learning and adaptability provided by machine learning.
On May 30, drug discovery startup Menten AI began conducting related research using cuQuantum. The company will utilize cuQuantum's library of tensor network algorithms to model protein interactions and optimize new drug molecules. Menten AI aims to use quantum computing to accelerate drug design and is working on developing quantum algorithms, including quantum machine learning, to break through complex computational problems in treatment design.
"While quantum computing hardware capable of running these algorithms is still in development, classical computing tools like NVIDIA's cuQuantum are critical to advancing the development of quantum algorithms," said Alexey Galda, chief scientist at Menten AI.
First, Nvidia needs a fast, low-latency connection between the GPU and the QPU (quantum processing unit) so that hybrid classical-quantum systems can use the GPU for circuit optimization, calibration, and error correction. GPUs can speed up the execution time of these steps and reduce communication delays between classical and quantum computers.
Second, the industry needs a unified programming model with tools that are efficient and easy to use.
Based on the aforementioned GPU-accelerated simulation software cuQuantum, programming model and compiler tools, etc., NVIDIA will begin to build a future classical-quantum high-performance computing data center.
A classical-quantum hybrid computer refers to a hybrid system in which quantum and classical computers can work together.
Quantum computing has the potential to solve complex problems ranging from drug discovery to weather forecasting in the future, and play an important role in the development of high-performance computing.
Today, supercomputers can simulate the work of quantum computing at scale and performance beyond that of relatively small, error-prone quantum systems. Therefore, classical high-performance computing systems can help researchers in quantum computing research.
NVIDIA announced at GTC 2022 that the NVIDIA cuQuantum software development kit for accelerating quantum computing has been fully released and showcases the results cuQuantum has achieved over a year. The software development kit was officially released at the 2021 GTC conference.
Image via Nvidia
Currently, dozens of quantum companies and departments have used the aforementioned software development kits to accelerate quantum circuit simulations on GPUs (graphics processing units, also known as display chips).Amazon Cloud Technologies (AWS) recently announced the availability of cuQuantum in its Amazon Braket quantum computing service, and demonstrated at Braket how cuQuantum can provide acceleration on quantum machine learning. Quantum machine learning is an emerging subfield within the field of quantum information that combines the speed of quantum computing with the learning and adaptability provided by machine learning.
On May 30, drug discovery startup Menten AI began conducting related research using cuQuantum. The company will utilize cuQuantum's library of tensor network algorithms to model protein interactions and optimize new drug molecules. Menten AI aims to use quantum computing to accelerate drug design and is working on developing quantum algorithms, including quantum machine learning, to break through complex computational problems in treatment design.
"While quantum computing hardware capable of running these algorithms is still in development, classical computing tools like NVIDIA's cuQuantum are critical to advancing the development of quantum algorithms," said Alexey Galda, chief scientist at Menten AI.
Image via Nvidia
Therefore, one of the most important future work of NVIDIA is to connect classical and quantum systems to hybrid quantum computers. This work is divided into two parts.First, Nvidia needs a fast, low-latency connection between the GPU and the QPU (quantum processing unit) so that hybrid classical-quantum systems can use the GPU for circuit optimization, calibration, and error correction. GPUs can speed up the execution time of these steps and reduce communication delays between classical and quantum computers.
Second, the industry needs a unified programming model with tools that are efficient and easy to use.
Based on the aforementioned GPU-accelerated simulation software cuQuantum, programming model and compiler tools, etc., NVIDIA will begin to build a future classical-quantum high-performance computing data center.
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