Boulder Opal at a glance and quickstart
Boulder Opal overview
Boulder Opal is a versatile toolkit that provides everything you need to improve and automate the performance of quantum hardware. The core capabilities of Boulder Opal fall under the framework of quantum control—the discipline that addresses the question, “How can systems that obey the laws of quantum mechanics be efficiently manipulated to create desired behaviors?”
The quantum control functionality underlying Boulder Opal comprises three core activities:
System identification: characterizing key system parameters by probing the quantum system and performing analysis on the resulting data. This information allows you to build a model of your system which you can use to inform how to design suitable controls for your system.
Control design: creating control solutions to manipulate quantum systems so they are resilient to noise, to cancel particular Hamiltonian terms, or to minimize control duration. This can be performed using either a Hamiltonian model of the system or via closed-loop experimental interrogation, and will generally involve critical calibration steps on the hardware.
Performance verification: analyzing the control solutions to gain insights into their functionality or effectiveness. This includes techniques to both probe the expected response and action of the control via simulation under realistic conditions and experimental probing of performance through specific validation routines known to highlight or amplify certain errors for easy tuneup.
If you want to know more about the capabilities of Boulder Opal, you can read our Boulder Opal overview topic or our introductions to the application of Boulder Opal to quantum computing, quantum sensing, or research.
The Q-CTRL Python package
To get started with Boulder Opal, you should first install the Q-CTRL Python package. It provides an intuitive and convenient interface to Q-CTRL’s quantum control solutions for customers of Q-CTRL. Here you will learn how to set up your system to use Boulder Opal, authenticate your credentials, and start a Boulder Opal session.
- To use the Q-CTRL Python package you will need a Q-CTRL account.
- The Q-CTRL Python package requires Python 3.7 or later.
1. Install Q-CTRL packages
To get started quickly and easily, we recommend Anaconda—a free and open-source distribution of the Python and R programming languages for scientific computing that aims to simplify package management and deployment.
Once you’re set up with a valid Python version, install the Q-CTRL Python package using pip on the command line.
pip install qctrl
pip install qctrl-open-controls
If you already have the
qctrl package installed and wish to update to the latest version, use the
upgrade flag in
pip install --upgrade qctrl
You can similarly update the
2. Authenticate your credentials
Call the authentication tool from your command-line interface with
An authentication link will open up automatically, or be provided for you to copy and open in your browser of choice. You will be asked to enter your credentials if you are not already authenticated on our web app.
It should look something like this:
Authentication URL: https://id.q-ctrl.com/auth/... The URL above should be automatically opened in your default web browser. (Please copy and paste if it doesn't open automatically) Authenticate your credentials in your browser... Finalizing authentication... Successful authentication!
3. Start a Q-CTRL session
Import the Q-CTRL Python package in your Python script or Jupyter notebook, and start a session by creating a
# Import Q-CTRL Python package. from qctrl import Qctrl # Start a Boulder Opal session. qctrl = Qctrl()
Authentication will happen automatically in the background whenever possible. Otherwise you will be redirected to a web browser for further action.
You are now ready to run your calculations. You can check out our tutorials, guiding you through Boulder Opal’s main features with hands-on exercises, and topics, discussing the core capabilities and concepts surrounding Boulder Opal.