Practical and versatile reservoir-filter for optimizing multi-state qubit readout

Qubit state readout has relied on matched filters to extract information from measurement data, in spite of their applicability only for white noise and binary classification. We demonstrate a reservoir-computing inspired learning scheme [1] for optimal temporal processing of quantum measurement data dominated by noise of a quantum-mechanical origin, such as quantum jumps or added noise of quantum amplifiers, for classification of an arbitrary number of states. Through demonstrations on real qubits, we show that reservoir classification can outperform standard approaches in complex readout regimes at high readout powers with multi-level transitions. Via a heuristic interpretation of reservoir learning as optimal filtering, we show that reservoir-learned filters account for correlations in data, including those due to quantum noise. For white noise, our approach provides a generalization of binary matched filtering to an arbitrary number of states. More importantly, across both experiments and theoretical simulations with quantum noise sources such as amplifier added noise and quantum jumps, we show the reservoir can uncover optimal filters that outperform matched filtering. The reservoir framework requires only linear weights and is thus ideal for real-time processing via FPGAs.

[1] Git repo: https://github.com/skhanCC/multistateRC-py