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Deep-Neural-Network Discrimination of Multiplexed Superconducting-Qubit States

2022; American Physical Society; Volume: 17; Issue: 1 Linguagem: Inglês

10.1103/physrevapplied.17.014024

2331-7043

Benjamin Lienhard, Antti Vepsäläinen, Luke C. G. Govia, Cole R. Hoffer, Jack Y. Qiu, Diego Ristè, Matthew Ware, David Kim, Roni Winik, Alexander Melville, Bethany M. Niedzielski, Jonilyn Yoder, Guilhem Ribeill, Thomas Ohki, Hari Krovi, Terry P. Orlando, Simon Gustavsson, William D. Oliver,

Neural Networks and Reservoir Computing

Demonstrating a quantum computational advantage will require high-fidelity control and readout of multiqubit 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 multiqubit 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.