Massachusetts Institute of Technology
Experiments towards mitigation of motional heating in trapped ion quantum information processing
Abstract
dc:description.abstractTrapped ions are a promising candidate for the implementation of quantum information processing. Techniques have already been developed for working with small systems of trapped-ion qubits; scalability is the biggest remaining challenge. One of the major scalability obstacles faced by trapped ions is an anomalous motional heating which limits the fidelity of two-qubit gates. It has been demonstrated that cleaning a gold trap chip via ion milling reduces the heating rate by two orders of magnitude [1]. However, it remains unclear why ion milling causes a much more dramatic improvement than similar cleaning techniques such as plasma cleaning, which only reduces the heating rate by a factor of 4 [2]. Understanding this difference will provide insight into the source of the anomalous heating noise. In this work, we investigate the mechanism by which ion milling reduces the heating rate by cleaning niobium traps with a ex-situ ion milling followed by plasma cleaning. We find that the resulting reduction in the heating rate is consistent with that obtained from plasma cleaning alone. This, combined with a recent result from the ex-situ milling of gold traps [3], suggests that some component of the improvement mechanism is material-based. Additionally, we present our work on the design and testing of a small resonator board used to deliver a high RF voltage to the trap chip. This board, made with off-the-shelf components, represents a more scalable alternative to the helical resonators which are commonly used for this purpose.
Degree
thesis:*- Department dc:contributor.department
- Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
- Grantor dc:publisher
- Massachusetts Institute of Technology
- Year dc:date.issued
- 2016
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Greene, Amy (Machine learning scientist) (Amy L.)
- Advisor dc:contributor.advisor
-
- Isaac Chuang, Jeremy Sage, and John Chiaverini.
Subjects
dc:subject × 1Rights
dc:rights- Statement dc:rights
-
- M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
- Licence dc:rights.uri
- Language dc:language.iso
- eng
Identifiers
dc:identifier.*- Handle dc:identifier.uri
- http://hdl.handle.net/1721.1/106017
- OAI identifier oai:identifier
- oai:dspace.mit.edu:1721.1/106017