Boulder Opal for quantum computing

An introduction to the application of Boulder Opal for key tasks in quantum computing

Realizing useful quantum computers requires a recognition that performance is predominantly limited by hardware imperfections and failures, not just system size. Susceptibility to noise and error remains the Achilles heel of quantum computers, and ultimately limits the range of achievable algorithms run on quantum coherent hardware. And the manual processes for hardware tuneup, calibration, and recalibration serve as a major block on progress.

Boulder Opal provides you tools to tackle the quantum computing challenges of decoherence, errors, and hardware instability, so you can accelerate R&D progress and achieve better performance from today's quantum hardware. As outlined in our Boulder Opal overview topic, we provide everything you need to improve and automate quantum computing hardware.

In quantum computing the three key activities of System Identification, Control Design, and Performance Verification take more specific forms tailored to the needs of contemporary hardware systems. The application of quantum control in a broader quantum computing software stack leads to the development of a quantum firmware abstraction layer. Here, quantum control is used to stabilize and virtualize faulty hardware before application in algorithms.


Quantum firmware is charged with encompassing the following functionality, which can be implemented in practice using Boulder Opal:

The core functionality of quantum firmware is augmented in Boulder Opal by tools that allow direct performance validation of the protocols deployed to quantum computing hardware. This includes validating the performance of a selected gateset to confirm error robustness, determine sensitivity to different noise frequencies, and understand the impact of coherent and incoherent error sources on quantum logic. It extends to detailed end-to-end simulation of complete quantum circuits using arbitrary quantum logic gates in realistic laboratory noise environments.

Boulder Opal provides both generic tools that can be tailored by the user to very complex optimization and simulation tasks, but also convenience functions and examples helping with application for superconducting circuits, trapped-ions, Rydberg atoms, and solid-state devices. Throughout our documentation we also provide convenience functions to ensure seamless data integration with lab-based control-hardware systems and cloud quantum computers.

The quantum control techniques described above are validated to deliver up to 10X performance improvements in real quantum computing hardware across multiple key metrics:

  • Qubit error rates: Quantum control protocols and efficient numeric optimization tools enable quantum logic with dramatically reduced susceptibility to noise and error.
  • Hardware stability and uptime: Building quantum logic operations robust against drifts has been shown to extend the window of useful calibration on IBM Q hardware from ~12 hours to >5 days.
  • Device performance homogeneity: Ensuring all devices on a quantum computer perform at their peak helps improve algorithmic success and simplifies compilation.

To begin taking advantage of Boulder Opal for your quantum computing research, check out our Get started guide for installation and initialization instructions and you'll be on your way!

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