Q-CTRL digest

Calling all quantum researchers in academia: Solve your toughest R&D challenges with free access to Boulder Opal

June 13, 2023
Written by
Alex Shih

Our mission is to make quantum technology useful; as a quantum company we do this through product innovation that’s underpinned by cutting-edge research.

We want to combine the best you’ve come to expect from conventional Python packages with commercial support, quality, and computational-resource provisioning to provide you with more than just raw numeric packages. And we want to help as many in the community as possible accelerate their research through these capabilities.

So now we’re delighted to announce that we’re making basic access to Boulder Opal, our flagship research tool in quantum control, available for free to the academic research community!

Helping all researchers do more

From Northwestern University and Chalmers University of Technology to Atom Computing and Pasqal, national laboratories, top universities, and private sector quantum companies use Boulder Opal to accelerate research and maximize quantum hardware performance.

Boulder Opal delivers best-in-class numerical techniques for control design (optimization), simulation and quantum digital twinning, performance validation, and AI-driven hardware automation. As a versatile Python-based package, it’s the ideal software in the market for deploying cutting-edge capabilities in quantum control.

Figure 1: Boulder Opal provides cutting-edge techniques and computational resources to help academics solve tough research and development challenges

Now you can combine the technical capabilities with production-level quality, professionally maintained documentation, and cloud-provisioned computational resources so you aren’t constrained by your laptop - for free!

Bringing convenience and flexibility to experimentalists

We want you to experience the power and customization flexibility of our tools for your research needs early on in your experience with Boulder Opal. So we’ve worked hard to build convenience on top of core technical capabilities so you can find that “aha!” moment as quickly as possible.

For experimentalists using Boulder Opal, that moment may come by shaving hours off of your calibration tasks with hardware automation. To build upon that, we recently launched several new capabilities that bring the power of AI to your systems.

Chief among these is the automated experiment scheduler that allows you to automate the manual processes of hardware calibration and system bring-up, shaving hours off of your day. In a recent demonstration we showed how you could configure the scheduler to allow a superconducting quantum computer to be brought up from a cold-start to maximum performance in under 90 minutes.

We offer not only the framework, but a range of convenience functions so you can perform the core experimental tasks with minimal coding. And of course you have the full ability to customize the scheduler to handle any automation tasks for your unique system.

Figure 2: The automated graph-based scheduler allows you to create your own custom calibration flows which are executed without human intervention, even when calibrations fail or require iteration between measurements

Supporting the theoretical research community

There are many exceptionally tough numerical challenges in quantum technology where the theory-focused community needs tools truly fit for purpose and documentation at a standard where you don’t spend weeks tracking down former grad students for help.

For instance, as our community builds ever more complex quantum devices, numerical optimization over complex high-dimensional spaces becomes critical. Add-in constraints such as limits on how fast a signal can change and most conventional packages break. Or encounter a problem with a challenging landscape and home-built solutions get stuck.

So we’ve introduced an all-new gradient-free optimization package that allows total convenience to add constraints and craft your own cost functions, but in regimes where traditional gradient-based approaches fail, enabling you to push the size of the system you’d like to optimize. This package can help you by identifying initial values to speed up the optimization process, and it uses less memory as it doesn’t need to compute the gradient, delivering the results you're after quickly!

Figure 3: Boulder Opal’s optimization package can be used to solve numerical challenges in a wide range of systems, including the design of entangling gates on large arrays of trapped ions.

Start using Boulder Opal to accelerate academic R&D

We are proud to support the entire academic research community by offering a free, self-service tier of Boulder Opal.

With our Basic plan, you can try the product at your own pace and use it continuously to integrate Boulder Opal into your workflows. It’s all Python-based and seamless to connect with other software packages like qutip and even hardware like Quantum Machines.

Early users have shared how free access via the Basic plan has allowed them to further their research.

“I'm using Boulder Opal’s pulse-control feature to optimize the fidelity of a two-qubit gate on quantum devices. My goal is to generate noise-robust pulses to solve a nuclear physics topic. After I graduate, I'll be working as an Applications Engineer at the National Quantum Computing Center in the UK where I hope to continue using Boulder Opal for industry applications.”
Manqoba Hlatshwayo, PhD candidate at Western Michigan University/Research collaborator at Lawrence Livermore National Laboratory

👉 Try out Boulder Opal for free to accelerate your research!

Already enjoying Boulder Opal and need more performance? You can chat to our expert team to get access to more compute resources, as well as premium features including an ultra-low-latency hybrid solution which allows you to combine cloud compute with local execution to reduce latencies to milliseconds for time-critical closed-loop optimizations.