Machine Learning Assisted Superconducting Qubit Readout
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, inf...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2022
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| Online Access: | https://hdl.handle.net/1721.1/140024 |
| _version_ | 1811084825786843136 |
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| author | Lienhard, Benjamin |
| author2 | Oliver, William D. |
| author_facet | Oliver, William D. Lienhard, Benjamin |
| author_sort | Lienhard, Benjamin |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T12:58:10Z |
| format | Thesis |
| id | mit-1721.1/140024 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T12:58:10Z |
| publishDate | 2022 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1400242022-02-08T03:33:38Z Machine Learning Assisted Superconducting Qubit Readout Lienhard, Benjamin Oliver, William D. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 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. Ph.D. 2022-02-07T15:19:36Z 2022-02-07T15:19:36Z 2021-09 2021-09-21T19:31:00.835Z Thesis https://hdl.handle.net/1721.1/140024 In Copyright - Educational Use Permitted Copyright MIT http://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Lienhard, Benjamin Machine Learning Assisted Superconducting Qubit Readout |
| title | Machine Learning Assisted Superconducting Qubit Readout |
| title_full | Machine Learning Assisted Superconducting Qubit Readout |
| title_fullStr | Machine Learning Assisted Superconducting Qubit Readout |
| title_full_unstemmed | Machine Learning Assisted Superconducting Qubit Readout |
| title_short | Machine Learning Assisted Superconducting Qubit Readout |
| title_sort | machine learning assisted superconducting qubit readout |
| url | https://hdl.handle.net/1721.1/140024 |
| work_keys_str_mv | AT lienhardbenjamin machinelearningassistedsuperconductingqubitreadout |