Deep Neural Network Discrimination of Multiplexed Superconducting Qubit States

• URL: http://arxiv.org/abs/2102.12481v2
• Date: Fri, 18 Jun 2021 12:43:42 GMT
• Title: Deep Neural Network Discrimination of Multiplexed Superconducting Qubit States
• Authors: Benjamin Lienhard, Antti Veps\"al\"ainen, Luke C. G. Govia, Cole R. Hoffer, Jack Y. Qiu, Diego Rist\`e, Matthew Ware, David Kim, Roni Winik, Alexander Melville, Bethany Niedzielski, Jonilyn Yoder, Guilhem J. Ribeill, Thomas A. Ohki, Hari K. Krovi, Terry P. Orlando, Simon Gustavsson, and William D. Oliver
• Abstract summary: We present multi-qubit readout using neural networks as state discriminators. We find that fully-connected feed neural networks increase the qubit-state-assignment fidelity for our system.
• Score: 39.26291658500249
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Demonstrating a 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.

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