Quick Navigation
Topics
Quantum Machine Learning
Superconducting Qubits
Hardware Architecture for a Quantum Computer Trusted Execution Environment
arXiv
Authors: Theodoros Trochatos, Chuanqi Xu, Sanjay Deshpande, Yao Lu, Yongshan Ding, Jakub Szefer
Year
2023
Paper ID
55996
Status
Preprint
Abstract Read
~2 min
Abstract Words
256
Citations
N/A
Abstract
The cloud-based environments in which today's and future quantum computers will operate, raise concerns about the security and privacy of user's intellectual property. Quantum circuits submitted to cloud-based quantum computer providers represent sensitive or proprietary algorithms developed by users that need protection. Further, input data is hard-coded into the circuits, and leakage of the circuits can expose users' data. To help protect users' circuits and data from possibly malicious quantum computer cloud providers, this work presented the first hardware architecture for a trusted execution environment for quantum computers. To protect the user's circuits and data, the quantum computer control pulses are obfuscated with decoy control pulses. While digital data can be encrypted, analog control pulses cannot and this paper proposed the novel decoy pulse approach to obfuscate the analog control pulses. The proposed decoy pulses can easily be added to the software by users. Meanwhile, the hardware components of the architecture proposed in this paper take care of eliminating, i.e. attenuating, the decoy pulses inside the superconducting quantum computer's dilution refrigerator before they reach the qubits. The hardware architecture also contains tamper-resistant features to protect the trusted hardware and users' information. The work leverages a new metric of variational distance to analyze the impact and scalability of hardware protection. The variational distance of the circuits protected with our scheme, compared to unprotected circuits, is in the range of only 0.16 to 0.26. This work demonstrates that protection from possibly malicious cloud providers is feasible and all the hardware components needed for the proposed architecture are available today.
Why This Paper Matters
- This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
- It adds a 2023 reference point for readers tracking recent quantum research.
- The cloud-based environments in which today's and future quantum computers will operate, raise concerns about the security and privacy of user's intellectual property.
Paper Tools
Become a member to use research tools
Sign in to open papers, visit source links, share, cite, compare, copy DOI links, request category corrections, and build your reading list.
Show Paper arXiv Publisher Share
Cite This Paper
Copy URL
Compare
Copy DOI Add to Reading List
Category Correction Request
Category Correction Request
Help us improve classification quality by proposing a better category. Every request is reviewed by an admin.
Sign in to submit a category correction request for this paper.
Log In to SubmitReferences & Citation Signals
Community Reactions
Quick sentiment from readers on this paper.
Score:
0
Likes: 0
Dislikes: 0
Sign in to react to this paper.
Discussion & Reviews (Moderated)
Average Rating: 0.0 / 5 (0 ratings)
No written reviews yet.