Noise Detection with Spectator Qubits and Quantum Feature Engineering

Abstract

Designing optimal control pulses that drive a noisy qubit to a target state is a challenging and crucial task for quantum engineering. In a situation where the properties of the quantum noise affecting the system are dynamic, a periodic characterization procedure is essential to ensure the models are updated. As a result, the operation of the qubit is disrupted frequently. In this paper, we propose a protocol that addresses this challenge by making use of a spectator qubit to monitor the noise in real-time. We develop a machine-learning-based quantum feature engineering approach for designing the protocol. The complexity of the protocol is front-loaded in a characterization phase, which allow real-time execution during the quantum computations. We present the results of numerical simulations that showcase the favorable performance of the protocol.

The source code, datasets, and trained models that were used to generate the results in this paper are publicly available at https://github.com/akramyoussry/QFEND

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Cited by in corpus (6)

• On the Interpretability of Quantum Neural Networks
• Experimental graybox quantum system identification and control
• Recovery With Incomplete Knowledge: Fundamental Bounds on Real-Time Quantum Memories
• Coherent interaction-free detection of noise
• Quantum-network nodes with real-time noise mitigation using spectator qubits
• Imperfection analyses for random-telegraph-noise mitigation using spectator qubits