Multifractality Analysis of Single‐Qubit Quantum Circuit Outcomes for a Superconducting Quantum Computer

We present a multifractal analysis of time series data obtained by repeatedly running a single‐qubit quantum circuit on IBM superconducting quantum computers in which the measurement outcomes are recorded as the number of zeros. We analyse empirical time series of repeated quantum circuit measurement outcomes from a superconducting NISQ processor, rather than eigenstates, wavefunctions or simulated quantum maps. We study shot‐aggregated measurement statistics (number of zeros per run over time) as a stochastic process influenced by hardware drift and measurement noise. By applying advanced signal processing techniques, including the wavelet leader method, we uncover strong multifractal behaviour in the output data. This finding indicates that the temporal fluctuations inherent to quantum circuit outputs are not purely random but exhibit complex scaling properties across multiple time scales. The multifractal nature of the signal suggests the possibility of tailoring filtering strategies that specifically target these scaling features to effectively mitigate noise in quantum computations. Our results not only contribute to a deeper understanding of the dynamical properties of quantum systems under repeated measurement but also provide a promising avenue for improving noise reduction techniques in near‐term quantum devices.