Omkar2118/datasets huggingface dataset:
quantumNoise

The dataset consists in many runs of the same quantum circuit on different IBM quantum machines. We used 9 different machines and for each one of them, we run 2000 executions of the circuit. The circuit has 9 differents measurement steps along it. To obtain the 9 outcome distributions, for each execution, parts of the circuit are appended 9 times (in the same call to the IBM API, thus, in the shortest possible time) measuring a new step each time. The calls to the IBM API followed two different strategies. One was adopted to maximize the number of calls to the interface, parallelizing the code with as many possible runs and even running 8000 shots per run but considering for 8 times 1000 out of the memory to get the probabilities. The other strategy was slower, without parallelization and with a minimum waiting time between subsequent executions. The latter was adopted to get more uniformly distributed executions in time.

Introducing paper: Learning the noise fingerprint of quantum devices