Date of Award
Fall 2023
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Physics
First Advisor
Schoelkopf, Robert
Abstract
Any useful quantum computer must be robust to errors on the individual qubits. This means a quantum algorithm can still reliably give the correct answer despite the qubits suffering from decoherence. Quantum error correction provides a means for achieving this, by encoding logical qubits redundantly in many additional physical qubits and/or additional energy levels. In the presence of errors the logical information is still preserved, but this remains a monumentally difficult task to achieve in practice. But what can we do with quantum error detection? Rather than trying to recover from a quantum error we instead reset qubits that are known to have suffered errors. If we know exactly when and which qubit suffered an error, this becomes an erasure, which is significantly easier to correct than Pauli errors. For erasure qubits to be viable, we must be able to detect errors that happen not just while idling as for a quantum memory, but during every operation needed to run a quantum computer. In this thesis we describe a way to realize the necessary error-detected operations in the qubit platform of bosonic circuit quantum electrodynamics. We choose to encode our qubits in 3D superconducting microwave cavity modes, and introduce the dual-rail cavity qubit, in which the dominant hardware errors can all be detected as erasure errors. With this new approach, practical quantum error correction is expected to be within reach for currently available experimental hardware and coherence times. Finally, we also investigate how to use error detection to circumvent loss errors in links that connect qubit modules together, experimentally realizing high-fidelity state transfer between modules.
Recommended Citation
Teoh, James David, "Error Detection in Bosonic Circuit Quantum Electrodynamics" (2023). Yale Graduate School of Arts and Sciences Dissertations. 1125.
https://elischolar.library.yale.edu/gsas_dissertations/1125
