Abstract:
Memory for quantum computers is important to several applications like machine learning but faces many challenges when it comes to hardware implementations.
There are a variety of theoretical proposals for how to implement memory, but each approach has tradeoffs.
In this talk, I will highlight a new library for Q# which implements a variety of qRAM proposals and allows for characterization of the resources needed for each approach.
I’ll show how this project was developed in the open with members of the Q# community, as well as how we adapted our workflow and tools to best support remote work.
I will also demo how we use software development best practices to test, package, and distribute the qRAM library, so that it’s easy for researchers to make use of.

Bio:
Dr. Sarah Kaiser has spent much of her career developing new quantum hardware in the lab, from satellites to hacking quantum cryptography hardware. Communicating what is so exciting about quantum is her passion, and she loves finding new demos and tools to help enable the quantum open source community to grow. When not at the keyboard she loves kayaking, laser cutting everything (safe), and writing books about engineering for kids.