By Purnam Sheth, VP Software
The quantum computing industry may be in its infancy, but today there are working quantum computers that anyone can build programs for. Over the past two years, Rigetti has been hard at work refining and improving our software development kit for programming quantum computers. We were the first company to outline an architecture and development approach for quantum computing in 2016, and ever since have been soliciting feedback from our community of users to ensure the entire suite of development tools included in our Forest SDK are the best available.
This week, we’re excited to share a few key updates to Forest.
- Open source Forest gets upgraded! We’ve long-embraced an open source approach to our software, and tools like pyQuil have been open source since the very get-go. PyQuil just got its own upgrade (more on that in a second!) and now, our compiler quilc, and our QVM are also open source and available on GitHub. Their respective README documents explain how to get started with optimizing and executing Quil code on your very own QVM.
- Optimize your algorithms for NISQ devices using pyQuil 2. Our Aspen series of QPUs, and all QPUs available to users today, are what is known as noisy, intermediate-scale quantum computers. These devices can be exploited more efficiently than ever because of key innovations now available in pyQuil 2. The most notable change in pyQuil 2 is parametric programming, which requires users to declare a variable that is compiled symbolically into an executable binary and then provided by the user at run-time — vastly speeding up the wall-clock time of running a program.
- Compile to any quantum hardware with Quil. Although our compiler is optimized for Rigetti hardware, it is not explicitly tied to this architecture. In fact, it’s the only compiler available today that can produce highly optimized code that translates easily to whatever chip architecture you specify.
- Test your code in a super-fast simulation environment. Our QVM is unique among simulators on the market in enabling just-in-time compilation of Quil to fast machine code. It also includes noise models that simulate actual behavior of real QPUs, rather than theoretically-perfect QPUs that don’t exist. This means you can prototype your quantum algorithms on a classical computer, and expect they will run as if they were running on the real QPU.
Please give these tools a whirl and don’t hesitate to ask questions, give feedback, or just hang — you can find us in the #dev channel on rigetti.slack.com. Happy programming!