{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/150299"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/150299","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Calibration and Utilization of High-Fidelity Two-Qubit Operations","abstract":"Over the past two decades, impressive strides have been made in the field of quantum computing. Quantum advantage has been reported, and there is now an ecosystem of cloud-based quantum processors and companies interested in using them. However, high error rates continue to limit circuit depth, such that solving real-world problems with today’s quantum computers remains a challenge. For quantum computing with superconducting qubits, two-qubit gates are a major source of those errors. In this thesis, we calibrate high-fidelity CZ and CPhase gates for flux-tunable transmon qubits. We develop a new technique for mitigating coherent errors in twoqubit gates called quantum measurement emulation (QME). We use this technique to implement a novel operation called density matrix exponentiation (DME), which has applications in quantum machine learning and universal simulation. These protocols contribute to the understanding and mitigation of errors in two-qubit gates. They are a step towards fault-tolerant universal quantum computing with superconducting circuits.","abstract_html":"Over the past two decades, impressive strides have been made in the field of quantum computing. Quantum advantage has been reported, and there is now an ecosystem of cloud-based quantum processors and companies interested in using them. However, high error rates continue to limit circuit depth, such that solving real-world problems with today’s quantum computers remains a challenge. For quantum computing with superconducting qubits, two-qubit gates are a major source of those errors. In this thesis, we calibrate high-fidelity CZ and CPhase gates for flux-tunable transmon qubits. We develop a new technique for mitigating coherent errors in twoqubit gates called quantum measurement emulation (QME). We use this technique to implement a novel operation called density matrix exponentiation (DME), which has applications in quantum machine learning and universal simulation. These protocols contribute to the understanding and mitigation of errors in two-qubit gates. They are a step towards fault-tolerant universal quantum computing with superconducting circuits.","abstract_has_math":false,"creators":["Greene, Amy"],"institution":"Massachusetts Institute of Technology","degree_name":"Doctoral","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. 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Quantum advantage has been reported, and there is now an ecosystem of cloud-based quantum processors and companies interested in using them. However, high error rates continue to limit circuit depth, such that solving real-world problems with today’s quantum computers remains a challenge. For quantum computing with superconducting qubits, two-qubit gates are a major source of those errors. In this thesis, we calibrate high-fidelity CZ and CPhase gates for flux-tunable transmon qubits. We develop a new technique for mitigating coherent errors in twoqubit gates called quantum measurement emulation (QME). We use this technique to implement a novel operation called density matrix exponentiation (DME), which has applications in quantum machine learning and universal simulation. These protocols contribute to the understanding and mitigation of errors in two-qubit gates. 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However, high error rates continue to limit circuit depth, such that solving real-world problems with today’s quantum computers remains a challenge. For quantum computing with superconducting qubits, two-qubit gates are a major source of those errors. In this thesis, we calibrate high-fidelity CZ and CPhase gates for flux-tunable transmon qubits. We develop a new technique for mitigating coherent errors in twoqubit gates called quantum measurement emulation (QME). We use this technique to implement a novel operation called density matrix exponentiation (DME), which has applications in quantum machine learning and universal simulation. These protocols contribute to the understanding and mitigation of errors in two-qubit gates. 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