Machine Learning Assisted Superconducting Qubit Readout

Abstract

Quantum computers hold the promise to solve specific problems significantly faster than classical computers. However, to realize a practical quantum computer, the quantum processor’s constituent components, their control, and their readout must be very well-calibrated. Over the last few decades, infrastructure and protocols have been developed to operate small-scale quantum processors efficiently. However, the operation of medium- to large-scale quantum processors presents new engineering challenges. Among those challenges are efficient and high-fidelity multi-qubit control and readout. In particular, qubit-state readout is a significant error source in contemporary superconducting quantum processors. The fidelity of dispersive qubit-state readout depends on the readout pulse shape and frequency as well as the resulting qubit-state discriminator. For a single qubit, fast and high-fidelity readout is achieved with minor changes to the rising and falling edge of a rectangular microwave pulse and a linear matched filter discriminator. However, in resource-efficient, frequency-multiplexed readout of multiple qubits, optimizing the readout pulse shape and discriminator becomes a computationally intensive task.

In this thesis, control and readout hardware and software tools for multiple superconducting qubits are developed. First, I discuss the principles to engineer microwave packages for multiple qubits. I designed and engineered a novel multiqubit package to enable efficient qubit control and readout and minimize errors due to interactions between the quantum processor and its immediate environment. Second, I demonstrate deep machine learning techniques to improve frequency-multiplexed superconducting qubit readout pulse shapes and discrimination for a five-qubit system. Compared with currently employed readout methods, these novel techniques reduce the required measurement time, the readout resonator reset, and the discrimination error rate by about 20% each. The developed readout techniques are a significant step towards efficient implementations of near-term quantum algorithms based on iterative optimization and quantum error correction protocols necessary for future universal quantum processors.