  Demonstrating the quantum computational advantage will require high-fidelity
control and readout of multi-qubit systems. As system size increases,
multiplexed qubit readout becomes a practical necessity to limit the growth of
resource overhead. Many contemporary qubit-state discriminators presume
single-qubit operating conditions or require considerable computational effort,
limiting their potential extensibility. Here, we present multi-qubit readout
using neural networks as state discriminators. We compare our approach to
contemporary methods employed on a quantum device with five superconducting
qubits and frequency-multiplexed readout. We find that fully-connected
feedforward neural networks increase the qubit-state-assignment fidelity for
our system. Relative to contemporary discriminators, the assignment error rate
is reduced by up to 25 % due to the compensation of system-dependent
nonidealities such as readout crosstalk which is reduced by up to one order of
magnitude. Our work demonstrates a potentially extensible building block for
high-fidelity readout relevant to both near-term devices and future
fault-tolerant systems.
