HPC meets quantum computing at ISC 2022

It’s time to start building the hybrid quantum computers of tomorrow.

The motivation is persuasive, the path is clear, and the main components of the job are available today.

Quantitative Statistics He has the potential to overcome some of today’s toughest challenges, by advancing everything from drug discovery to weather forecasting. In short, quantum computing will play a huge role in the future of HPC.

Today’s Quantum Simulation

Creating this future won’t be easy, but the tools to get started are right here.

Taking the first steps forward, today’s supercomputers simulate quantum computing functions at a scale and performance levels beyond the reach of relatively small and error-prone quantum systems.

Dozens of quantitative regulators are already in use NVIDIA cuQuantum A software development kit for accelerating quantum circuit simulations on GPUs.

Recently, AWS announced the availability of cuQuantum at its service Braket. like that proven Braket shows how cuQuantum can provide up to 900x acceleration on quantum machine learning workloads.

cuQuantum now enables accelerated computing on major quantum software frameworks, including Google’s qsim, IBM’s Qiskit Aer, Xanadu’s PennyLane and Classiq’s quantum algorithm design platform. This means that users of these frameworks can access GPU acceleration without any additional coding.

Quantitative drug discovery

Today, Menten AI is joining companies that are using cuQuantum to support their quantum work.

The emerging drug discovery company Bay Area will use the cuQuantum tensor network library to simulate protein interactions and optimize new drug molecules. It aims to harness the potential of quantum computing to accelerate 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 set of quantum computing algorithms including quantum machine learning to break through problems that require computational computation in therapeutic design.

“While quantum computing devices capable of running these algorithms are still under development, classical computing tools such as NVIDIA cuQuantum are essential for quantum algorithm development,” said Alexey Galda, principal scientist at Menten AI.

Quantum link falsification

With the development of quantum systems, the next big leap is the transition to hybrid systems: quantum and classical computers working together. Researchers share a vision of systems-level quantum processors, or QPUs, serving as a powerful new class of accelerators.

So, one of the biggest jobs ahead of us is to connect classical and quantum systems with hybrid quantum computers. This work has two main components.

First, we need a fast, low-latency connection between the GPUs and QPUs. This will allow hybrid systems to use GPUs for the classic functions where they excel, such as circuit optimization, calibration, and debugging.

GPUs can speed up the execution time of these steps and reduce communication latency between classical and quantum computers, which are the main bottlenecks for today’s hybrid quantum functions.

Second, the industry needs a unified programming model with efficient and easy-to-use tools. Our experience with HPC and AI has taught us and our users the value of a robust software suite.

The right tools for the job

To program today’s QPUs, researchers are forced to use the quantum equivalent of low-level assembly code, something beyond the reach of scientists who are not experts in quantum computing. In addition, developers lack a unified programming model and compiler toolchain that would allow them to run their work on any QPU.

This needs to change, and it will. in March BlogIn this article, we discussed some of our initial work towards a better programming paradigm.

To find ways quantum computers can speed up their work efficiently, scientists need to easily transfer parts of HPC applications first to a simulated QPU, and then to a real machine. This requires a compiler that enables it to operate at high performance levels and in familiar ways.

By combining GPU-accelerated simulation tools, a programming model, and a compiler toolchain to link them together, HPC researchers will be empowered to begin building hybrid quantum data centers of the future.

how to start

To some, quantum computing may seem like science fiction, decades to come. The fact is that every year researchers build larger and larger quantum systems.

NVIDIA is fully involved in this work and we invite you to join us in building tomorrow’s hybrid quantum systems today.

To learn more, you can view the file GTC جلسة session and attend ISC Education Program about the topic. To delve into what you can do with GPUs today, read about state vector And the tensor network libraries.