Boulder Opal workflows for research

Understand how and when to integrate Boulder Opal into your research: for theorists or experimentalists, new hardware, or established systems

Boulder Opal gives you everything that you need to automate and optimize quantum research and hardware—for quantum computing and quantum sensing. Our technical team has been using Boulder Opal in collaboration with partners around the world to deliver major gains in hardware performance. How can you do the same?

The key functional areas are captured in our Boulder Opal overview topic, but here you'll learn some very efficient workflows to take advantage of Boulder Opal for hardware projects at various levels of technical maturity. We know that what works best for a new research team might not be appropriate for a mature system, so we're happy to share insights with you to help you, no matter where you are on your journey (and don't forget to check out our tutorials to help you get started with Boulder Opal).

Accelerating theoretical research

Boulder Opal delivers huge value to hardware teams and to theorists as well! You can take advantage of the most flexible and highest performance numerical package available in the field, delivering solutions orders of magnitude faster than alternative packages.

1. Speed up simulations of complex quantum dynamics by >10×

2. Discover the impact of optimal control on multidimensional quantum systems

Developing new hardware systems

The best time to start integrating advanced quantum control, automation, and characterization tools into your research is before you've wasted time with unstable hardware or heavily manual tasks. Here are some examples of how Boulder Opal can be deployed to preemptively solve hardware problems:

1. Accelerate system builds by automating the tuneup and calibration of classical supporting systems

  • Identify systems requiring manual calibration such as RF modulators, PID controllers, or amplifiers.
  • Establish appropriate measurement setups such as photodetectors and RF power meters.
  • Manually tune them to understand approximate performance bounds for control signals—this can be automated with simple Python scripts.
  • Build a closed-loop optimization routine.
  • Execute automated calibration or optimization of multiparameter systems.

2. Prevent research slowdowns by building in drift robustness from day one

Upgrading commissioned hardware systems

Once hardware is functional a range of new tasks might emerge—like trying to pinpoint the dominant sources of noise in your system. Or it might be that you've tried all of the "standard" tricks and now need to move on to fully autonomous control optimization. Here are some example workflows to get you improving your hardware quickly.

1. Identify sources of noise and performance degradation

2. Improve hardware performance via error-robust quantum control

3. Perform closed-loop hardware optimization to maximize performance against unknown sources of noise and error

4. Automate manual calibration and tuneup across full systems

  • Identify manual tasks like RF IQ channel calibration.
  • Build a closed-loop optimization routine covering multiple multidimensional optimizations in parallel.
  • Execute fully autonomous system-wide calibration as a simple script that can be scheduled for your hardware.

If you'd like more inspiration on complete workflows that can help, please have a look at our application notes which capture end-to-end research tasks, or contact our team to discuss achieving your performance gains.

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