{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/140024"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/140024","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Lienhard, Benjamin"],"institution":"Massachusetts Institute of Technology","degree_name":"Doctoral","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. 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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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Ph.D."]},{"key":"dc:title","label":"Title","values":["Machine Learning Assisted Superconducting Qubit Readout"]}]}],"canonical_facts":{"dc:contributor.advisor":["Oliver, William D."],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science"],"dc:creator":["Lienhard, Benjamin"],"dc:date.accessioned":["2022-02-07T15:19:36Z"],"dc:date.available":["2022-02-07T15:19:36Z"],"dc:date.issued":["2021-09"],"dc:description.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."],"dc:description.degree":["Ph.D."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/140024"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["In Copyright - Educational Use Permitted","Copyright MIT"],"dc:rights.uri":["http://rightsstatements.org/page/InC-EDU/1.0/"],"dc:title":["Machine Learning Assisted Superconducting Qubit Readout"],"dc:type":["Thesis"],"thesis:degree_name":["Doctoral","Doctor of Philosophy"]},"updated_at":"2026-07-22T22:21:44Z"}