Resource-Efficient Multi-Qubit Readout on FPGAs

Simultaneous state readout of multi-qubit systems is a key task in quantum computing. Frequency multiplexing offers a resource-efficient way to read out multiple superconducting qubits. However, this approach can also introduce challenges like system crosstalk.

Qubit readout effectiveness depends on the interaction between hardware, gateware for state determination, and software for error correction. Traditionally, simple algorithms like thresholding or mode-matched filtering, implemented on FPGAs, are used to connect these components but struggle to handle crosstalk.

Neural networks can significantly enhance readout accuracy, but their models are often too large for practical FPGA implementation.

We present a hybrid approach that integrates mode-matched filtering with neural networks on FPGAs. This solution achieves crosstalk resistance comparable to pure neural networks while significantly reducing resource demands, making practical deployment on quantum computers more feasible.