Katharine Hyatt

Automatic differentiation (AD) is gaining ground as a technique for optimization of tensor networks (TN), which are widely used simulation tools in quantum computing, condensed matter, and high energy physics.

In this panel we'll sum up where the wider JuliaGPU ecosystem is today and invite feedback from developers and users on where we should focus efforts in the year to come.

In this lightning talk I will step through some of the annoyances and papercuts I encountered while writing multithreaded Julia code for my scientific applications.

In this talk we'll summarize and demonstrate some of the improvements made to the CUDA.jl package over the past year; including new features in the compiler, memory management, and device programming stack; as well as updates about the support for various CUDA libraries.

In this chalk talk, dive into CUDA-Q, the quantum programming platform from NVIDIA, and its integration with Amazon Braket. This talk guides you through the process of developing and running quantum algorithms using CUDA-Q on Braket.

Hybrid algorithms, combining quantum and classical compute resources, are widely believed to be promising candidates for the first demonstrations of useful applications of quantum computers.

Amazon Braket offers a range of programming models from different quantum computing hardware providers, each with its own unique attributes for performing computational work. In this chalk talk, builders can learn from examples how to best use real quantum computing hardware in an integrated environment within Amazon Braket to tackle difficult computational problems.

The state of quantum computing today resembles the early days of machine learning (ML). Many current quantum algorithms are based on the idea that they can be “trained” in a similar way as a neural network in ML.

Julia is a new open-source programming language developed for scientific and technical computing. In this chalk talk, learn the basics of quantum computing using Amazon Braket and Julia.