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 ## Learning
 
 - [An Interactive Introduction to Quantum Computing](https://davidbkemp.github.io/QuantumComputingArticle/) - Interactive learning for quantum gate computing by David Kemp.
+- [Black Opal](https://q-ctrl.com/black-opal) - An interactive platform for learning the fundamentals of quantum computing.
 - [CNOT](https://cnot.io/) - Easy to understand, step by step introduction to quantum computing concepts.
 - [Chris Ferrie](https://csferrie.medium.com/) - Univeristy Professor in Sydney, Australia, author of Quantum Computing for babies (and many more) as well as excellent Quantum Computing lectures on Medium.
 - [Documentation for Forest and pyQuil](http://pyquil.readthedocs.io/en/latest/) - Tutorials for Rigetti Computing's SDK.
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 - [Amazon Braket](https://aws.amazon.com/braket/) - Fully managed service providing a development environment to run quantum circuits on quantum simulators and computers.
 - [Blueqat](https://github.com/Blueqat/Blueqat) - Software development kit in Python for quantum gate computing.
+- [Boulder Opal](https://q-ctrl.com/boulder-opal) - Python toolset for automating and optimizing quantum hardware performance.
 - [Cirq](https://github.com/quantumlib/Cirq) - Python library for writing, manipulating, and optimizing NISQ circuits to run on quantum computers.
 - [Covalent](https://github.com/AgnostiqHQ/covalent) - Framework for distributed computing on heterogeneous infrastructure from CPUs to GPUs to quantum computers.
+- [Fire Opal](https://q-ctrl.com/fire-opal) - Python package for improving the quality of quantum algorithm results.
 - [IBM Quantum Experience](https://quantum-computing.ibm.com) - Online quantum composer to run experiments on real quantum computing hardware.
 - [Mitiq](https://mitiq.readthedocs.io/) - Python toolkit for implementing error mitigation techniques on quantum computers.
 - [NISQAI](https://github.com/quantumai-lib/nisqai) - Library for performing quantum artificial intelligence on near-term quantum computers.