Forest 1.3: Upgraded developer tools, improved stability, and faster execution

Posted by Will Zeng

In December, the team at Rigetti became the first to solve an unsupervised machine learning problem on a gate model quantum computer. We 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.

Starting today, researchers using 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.

Upgraded tools for debugging and optimizing quantum programs

In the ten weeks since we announced availability of Acorn, our 19-qubit chip, we’ve received a lot of feedback from you: our wider community of researchers. The ultimate success of quantum computing depends not only on companies like Rigetti, but on the researchers using our platform to push the field forward. So we took the feedback we heard to heart, and we got to work. Today’s upgrade is in direct response to your input.

Here are a few of the improvements we think will help you in doing your work:

  • You can now access our compiler through an 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.
  • Our 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.

Be sure to check out the entire list of feature upgrades in pyQuil.


“Rigetti has a sophisticated compiler that optimizes textbook gates for the physical architecture, and they tell you exactly what physical gates were implemented. This is nice for casual users who just want to state their algorithms in the textbook language, but also is great for researchers who want to know exactly what gates were performed.
A cool new feature is their program fidelity calculator, which tells the user the expected fidelity for their algorithm, accounting for the known fidelities of the individual gates. This gives you a benchmark for how good your algorithm is expected to perform, and it’s encouraging when this correlates with the actual performance.
Rigetti’s model of single-user QPU access is also ideal from a research standpoint: You get sole access to their quantum computer and there’s essentially no delay between submitting your job and receiving the result of the computation.
Rigetti has accounted for our early feedback about a difference in their “run” commands between their simulator and the QPU, and they’ve now made it such that the same commands can be used on both. This is nice, since it allows you go back-and-forth between the simulator and QPU with ease.”
-Patrick Coles, Los Alamos National Laboratory

Stability improvements to our Acorn 19Q QPU

In measuring performance of QPUs, the number of qubits and gate fidelity are the most commonly used metrics. As a community, we don’t always highlight variations in qubit and processor performance, and we often fail to place enough emphasis on the effect of stability in QPU performance. Stability is particularly important in a hybrid classical/quantum computation, where a single algorithm may generate many different quantum programs that run over longer time periods. In the last two months we have significantly stabilized our CZ gates.

Faster execution

In any given quantum program, researchers make multiple calls to our API. In these circumstances, network latency can become an issue and slow down overall execution time. In Forest 1.3 we provide a 2x speedup in cloud job execution on our QPUs. This makes running hybrid classical/quantum algorithms with many API calls much faster.

We’ve also made a new two-qubit gate instruction available on our 19Q-Acorn quantum processor: CPHASE(theta). This introduces our first parameterized two-qubit gate, offering more powerful and flexible entanglement between qubits.

Forest is the easiest and most powerful way to build quantum applications today. We believe the combination of one of the most powerful gate-model quantum computers, cutting-edge classical hardware, and our unique hybrid classical/quantum architecture creates the clearest and shortest path toward the demonstration of unequivocal quantum advantage. Ultimately, it will be you — the broader Forest community — who clears that path.