Real-world use cases
Preparing diverse cohorts with Black Opal for Digital Catapult’s hybrid learning Quantum Technology Programme
Helping Digital Catapult drive quantum computing adoption through education, industry partnerships, and hands-on learning with Black Opal.
4.8
out of 5 average participant rating for the program, including Black Opal, which was also credited with raising participants' confidence in their quantum computing abilities from 3.1 to 4.2 out of 5 by the end of the course.
The easy-to-use, intuitive Black Opal tool allowed our QTAP participants to quickly get up to speed with quantum computing, ready to trial their real-life business use cases. There was also some great material for participants who were curious to learn more about advanced topics.
Improving quantum education outcomes with Black Opal at the University of Hull, UK
Enhancing student engagement and real-world understanding in quantum computing through intuitive and interactive learning with Black Opal.
85%
of students reported that Black Opal improved their overall learning outcomes when used alongside their university syllabus, and would recommend its use in future courses.
Students who engaged with Black Opal as an active companion resource significantly boosted knowledge retention and ultimately understanding. Some of them even engaged further with quantum computing by choosing a final-year project in that field.
Northwestern looks to the heart of the universe with robust quantum sensors
With Boulder Opal, Northwestern suppressed 5 different noise sources simultaneously with a single optimized robust control pulse for atom interferometry.
5
different noise sources can be suppressed simultaneously with a single optimized robust control pulse for atom interferometry.
The breadth and flexibility of Boulder Opal allowed us to create our own optimization scenario and obtain pulses robust to the five most relevant experimental noise sources at the same time! This will be crucial in the development of atomic interferometers to detect dark matter and gravitational waves at currently unexplored frequencies.
Making sense of quantum noise with machine learning
Q-CTRL's quantum control engineers developed a new machine-learning tool allowing high-fidelity reconstruction of the spectral “fingerprint” of quantum noise.
<1PPM
Sensitivity error-source identification during quantum logic.
Collaboration between experimentalists at University of Sydney and quantum control engineers at Q-CTRL breakthrough result published in Physical Review Letters
