Using learning control to create high fidelity cross resonance gates on superconducting qubits with Boulder Opal

Oct 18, 2021, 4:15 PM


Michael Hush (Q-CTRL, Australia)


Quantum computers promise to open new opportunities in the simulation of quantum systems and machine learning. Superconducting qubit quantum computers are currently one o the leading platforms to perform, but their performance is significantly limited by the quality of the two qubit quantum gates. In this talk we use learning control to create high fidelity cross resonance gates using the software package Boulder Opal. We present the entire learning control development cycle. We start by using Boulder Opal to create a realistic pulse level simulation of a quantum compute with cross resonance gates. The underlying simulations are implemented in Tensorflow and are accelerated with GPUs. A series of reinforcement learning algorithms are then tested and compared using this simulation tool. The best reinforcement learning algorithm is then picked and implemented on IBM-Q. IBM-Q provides access to superconducting quantum computers that can be programmed at the individual pulse level using the OpenPulse standard. We demonstrate that reinforcement learning is able to produce cross resonance gates with a 40% less errors than the IBM-Q defaults.

Presentation Materials

There are no materials yet.