Menten.ai to Work with NVIDIA on Quantum-Powered Drug Discovery
May 30, 2022, NVIDIA.
It’s time to start building tomorrow’s hybrid quantum computers.
The motivation is compelling, the path is clear and key components for the job are available today.
Quantum computing has the potential to bust through some of today’s toughest challenges, advancing everything from drug discovery to weather forecasting. In short, quantum computing will play a huge role in HPC’s future.
Today’s Quantum Simulations
Creating that future won’t be easy, but the tools to get started are here.
Taking the first steps forward, today’s supercomputers are simulating quantum computing jobs at scale and performance levels beyond the reach of today’s relatively small, error-prone quantum systems.
Dozens of quantum organizations are already using the NVIDIA cuQuantum software development kit to accelerate their quantum circuit simulations on GPUs.
Most recently, AWS announced the availability of cuQuantum in its Braket service. It also demonstrated on Braket how cuQuantum can provide up to a 900x speedup on quantum machine learning workloads.
And cuQuantum now enables accelerated computing on the major quantum software frameworks, including Google’s qsim, IBM’s Qiskit Aer, Xanadu’s PennyLane and Classiq’s Quantum Algorithm Design platform. That means users of those frameworks can access GPU acceleration without any additional coding.
Quantum-Powered Drug Discovery
Today, Menten AI joins companies using cuQuantum to support its quantum work.
The Bay Area drug-discovery startup will use cuQuantum’s tensor network library to simulate protein interactions and optimize new drug molecules. It aims to harness the potential of quantum computing to speed up drug design, a field that, like chemistry itself, is thought to be among the first to benefit from quantum acceleration.
Specifically, Menten AI is developing a suite of quantum computing algorithms including quantum machine learning to break through computationally demanding problems in therapeutic design.
“While quantum computing hardware capable of running these algorithms is still being developed, classical computing tools like NVIDIA cuQuantum are crucial for advancing quantum algorithm development,” said Alexey Galda, a principal scientist at Menten AI.
Forging a Quantum Link
As quantum systems evolve, the next big leap is a move to hybrid systems: quantum and classical computers that work together. Researchers share a vision of systems-level quantum processors, or QPUs, that act as a new and powerful class of accelerators.
So, one of the biggest jobs ahead is bridging classical and quantum systems into hybrid quantum computers. This work has two major components.
First, we need a fast, low-latency connection between GPUs and QPUs. That will let hybrid systems use GPUs for classical jobs where they excel, like circuit optimization, calibration and error correction.
GPUs can speed the execution time of these steps and slash communication latency between classical and quantum computers, the main bottlenecks for today’s hybrid quantum jobs.
Second, the industry needs a unified programming model with tools that are efficient and easy to use. Our experience in HPC and AI has taught us and our users the value of a solid software stack.
Right Tools for the Job
To program QPUs today, researchers are forced to use the quantum equivalent of low-level assembly code, something outside of the reach of scientists who aren’t experts in quantum computing. In addition, developers lack a unified programming model and compiler toolchain that would let them run their work on any QPU.
This needs to change, and it will. In a March blog, we discussed some of our initial work toward a better programming model.
To efficiently find ways quantum computers can accelerate their work, scientists need to easily port parts of their HPC apps first to a simulated QPU, then to a real one. That requires a compiler enabling them to work at high performance levels and in familiar ways.
With the combination of GPU-accelerated simulation tools and a programming model and compiler toolchain to tie it all together, HPC researchers will be empowered to start building tomorrow’s hybrid quantum data centers.
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