Accelerated Computing Key to Yale’s Quantum Research

A recently released joint research paper by Yale, Moderna and NVIDIA reviews how techniques from quantum machine learning (QML) may enhance drug discovery methods by better predicting molecular properties.

Ultimately, this could lead to the more efficient generation of new pharmaceutical therapies.

The review also emphasizes that the key tool for exploring these methods is GPU-accelerated simulation of quantum algorithms.

The study focuses on how future quantum neural networks can use quantum computing to enhance existing AI techniques.

Applied to the pharmaceutical industry, these advances offer researchers the ability to streamline complex tasks in drug discovery.

Researching how such quantum neural networks impact real-world use cases like drug discovery requires intensive, large-scale simulations of future noiseless quantum processing units (QPUs).

This is just one example of how, as quantum computing scales up, an increasing number of challenges are only approachable with GPU-accelerated supercomputing.

The review article explores how NVIDIA’s CUDA-Q quantum development platform provides a unique tool for running such multi-GPU accelerated simulations of QML workloads.

The study also highlights CUDA-Q’s ability to simulate multiple QPUs in parallel. This is a key ability for studying realistic large-scale devices, which, in this particular study, also allowed for the exploration of quantum machine learning tasks that batch training data.

Many of the QML techniques covered by the review — such as hybrid quantum convolution neural networks — also require CUDA-Q’s ability to write programs interweaving classical and quantum resources.

The increased reliance on GPU supercomputing demonstrated in this work is the latest example of NVIDIA’s growing involvement in developing useful quantum computers.

NVIDIA plans to further highlight its role in the future of quantum computing at the SC24 conference, Nov. 17-22 in Atlanta.

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