Understand Boulder Opal fundamentals for quantum experimentalists
Get started with hardware characterization and closed-loop optimization for experimentalists in Boulder Opal
Boulder Opal provides experimentalists with a comprehensive toolkit for characterizing quantum hardware, identifying noise sources, and automating the optimization of real waveform pulses through closed-loop routines. This article focuses on practical workflows, from system identification to closed-loop optimization, using Boulder Opal's functional graph framework and automation tools.
1. Characterize your hardware and noise
System identification is the first step in understanding and improving your quantum device. This involves probing the system to quantify key system parameters. Boulder Opal enables you to:
- Estimate Hamiltonian parameters: Use probe measurements and data fusion routines to build accurate models of your system. For example, you can estimate parameters of your system's Hamiltonian or characterize transmission lines in your experiment.
- Perform noise spectroscopy: Reconstruct noise spectra on arbitrary channels using shaped control pulses, helping you pinpoint dominant sources of decoherence or control errors.
For more details, see:
- Explore system identification techniques for quantum hardware characterization
- How to perform noise spectroscopy on arbitrary noise channels
- How to perform parameter estimation with a small amount of data
- For additional resources, view our documentation on characterizing hardware
2. Integrate Boulder Opal with your experimental setup
Boulder Opal outputs control solutions as NumPy arrays, which can be easily converted to the waveform formats required by your electronic controllers or cloud quantum computing platforms. Ensure that the time step and segmentation of your piecewise-constant (PWC) signals match your hardware requirements by inspecting the durations attribute of the PWC object.
For more details, see:
- How to format and export control solutions for hardware implementation
- Easily integrate Boulder Opal outputs with common electronic controllers
3. Run closed loop optimization on real hardware
Boulder Opal's closed-loop optimization tools allow you to automate the calibration and optimization of control pulses directly on your hardware, even if you do not have a complete system model.
Example workflow:
- Set up the closed loop: The optimizer communicates with your experimental apparatus, sending test pulses and receiving measurement results. Based on these results, it recommends improved pulses, iterating until optimal performance is achieved.
- Run calibration: For example, calibrate RF or microwave channels to maximize pulse fidelity, accounting for hardware imperfections and translating ideal pulses into hardware-ready signals. Optimize against unknown noise: Closed-loop routines can learn and compensate for hardware imperfections and unknown noise sources, maximizing performance in real-world conditions.
For more details, see:
- How to automate calibration of control hardware
- How to automate closed-loop hardware optimization
- Automate quantum experiments with AI
- For additional resources, view our documentation on automating closed-loop optimization
Example experimentalist workflow
Experimentalists can bring these capabilities together to accelerate their investigative work while evaluating quantum hardware. Use an iterative approach with the below steps to facilitate your quantum research.
- Characterize hardware: Use system identification and noise spectroscopy routines to build a model of your device and identify noise sources.
- Create starting pulses: Use the information you've obtained about your system from characterization to improve your system model. Then run model-based optimizations to create initial waveform pulses. (link to Boulder 101 for Theorists)
- Apply starting pulses: Parameterize your waveform pulses to your controller's format and apply them to your hardware.
- Run closed-loop optimization: Set up a closed-loop routine where Boulder Opal iteratively refines control pulses based on experimental feedback.
- Validate performance: Use Boulder Opal's simulation tools to verify improvements in fidelity, robustness, and hardware stability.
Additional resources for experimentalists
Review qubit modality specifics for more information related to your specific system:
By following these steps, experimentalists can leverage Boulder Opal to accelerate hardware tune-up, achieve robust calibration, and maximize quantum device performance through automated, data-driven optimization routines.
