Fast qubit experiments using Qiskit and Quantify - a control stack designed for Experimentalists
ORAL
Abstract
The coding of experiments has become increasingly complex. Different coding platforms/languages make up an indigestible forest of options to choose from. With increasing qubit numbers and experiment complexity, there is an ever growing need for faster execution of experiments. At the same time, control stack electronics have become more powerful and support more complex instruction sets. In order to both minimize the time required for coding and maximize the speed of execution it is crucial that the software stack is both highly modular and that it offers multiple abstraction levels.
We show how these challenges are overcome using Quantify, an open source platform for quantum experiments.
We will first introduce the full software stack for a quantum computer. We then show seamless mixing of Qiskit circuits and hybrid gate-pulse level Quantify schedules together with classical logic like fast parameter sweeps and active reset. This dramatically simplifies and speeds up characterization experiments. Finally, we show how backend optimizations improve hardware utilization with minimal overhead for the user. We show how subroutines increase the maximum length of a randomized benchmarking sequence by a factor of 3. Similarly, instruction selection allows to implement pulses much more efficiently, without user input.
Such optimizations allow for maximal efficiency when using control electronics, without burdening the user with the additional complexity that comes with manual implementation.
We show how these challenges are overcome using Quantify, an open source platform for quantum experiments.
We will first introduce the full software stack for a quantum computer. We then show seamless mixing of Qiskit circuits and hybrid gate-pulse level Quantify schedules together with classical logic like fast parameter sweeps and active reset. This dramatically simplifies and speeds up characterization experiments. Finally, we show how backend optimizations improve hardware utilization with minimal overhead for the user. We show how subroutines increase the maximum length of a randomized benchmarking sequence by a factor of 3. Similarly, instruction selection allows to implement pulses much more efficiently, without user input.
Such optimizations allow for maximal efficiency when using control electronics, without burdening the user with the additional complexity that comes with manual implementation.
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Presenters
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Daniel J Weigand
- Qblox bv
- Qblox BV