In December, 2017 the team at Rigetti Computing became the first to solve an unsupervised machine learning problem on a gate model quantum computer. They did this by connecting one of our recent superconducting quantum processors, a 19-qubit system, to our software platform, Forest. In the ten weeks since then, researchers have already used Forest to train neural networks, program benchmarking games, and simulate nuclear physics.
Today, Forest will be upgraded to version 1.3, which provides better tools for optimizing and debugging quantum programs. The upgrade also provides greater stability in our quantum processor (QPU), which will let researchers run more powerful quantum programs.
Here are a few of the improvements we think will help you in doing your work:
* You can now access our compiler through a dedicated API, which empowers you to experiment with compiling your programs to different hardware architectures.
* You can now test your programs on a quantum virtual machine (QVM) that more accurately mimics actual quantum hardware, accelerating your development time. We’ve released preconfigured noise models based on the behavior of our QPU.
* Once you’ve tested a program on the QVM, you can now easily port it to the QPU, which now supports the .run pyQuil command.
* Their effective readout fidelity has been improved thanks to a toolset that compensates for readout errors in the QPU. This can dramatically improve the performance of your programs.
* You now have built-in tools for quantum state and process tomography, This makes it easier to debug and study the programs you are running.