Document Type
Pre-Print
Publication Date
2019
Subjects
Quantum theory, Quantum computers
Abstract
We use machine learning techniques to design a 50 ns three-qubit flux-tunable controlled-controlled-phase gate with fidelity of >99.99% for nearest-neighbor coupled transmons in circuit quantum electrodynamics architectures. We explain our gate design procedure where we enforce realistic constraints, and analyze the new gate’s robustness under decoherence, distortion, and random noise. Our controlled-controlled phase gate in combination with two single-qubit gates realizes a Toffoli gate which is widely used in quantum circuits, logic synthesis, quantum error correction, and quantum games.
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Persistent Identifier
https://archives.pdx.edu/ds/psu/30130
Citation Details
Daraeizadeh, Sahar; Premaratne, Shavindra; Song, Xiaoyu; Perkowski, Marek A.; and Matsuura, Anne Y., "Machine-learning Based Three-Qubit Gate for Realization of a Toffoli Gate with cQED-based Transmon Systems" (2019). Electrical and Computer Engineering Faculty Publications and Presentations. 510.
https://archives.pdx.edu/ds/psu/30130
Description
This is the author’s version of a work. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document.