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Quantum Machine Learning

Toward Privacy in Quantum Program Execution On Untrusted Quantum Cloud Computing Machines for Business-sensitive Quantum Needs

arXiv
Authors: Tirthak Patel, Daniel Silver, Aditya Ranjan, Harshitta Gandhi, William Cutler, Devesh Tiwari

Year

2023

Paper ID

56253

Status

Preprint

Abstract Read

~2 min

Abstract Words

100

Citations

N/A

Abstract

Quantum computing is an emerging paradigm that has shown great promise in accelerating large-scale scientific, optimization, and machine-learning workloads. With most quantum computing solutions being offered over the cloud, it has become imperative to protect confidential and proprietary quantum code from being accessed by untrusted and/or adversarial agents. In response to this challenge, we propose SPYCE, which is the first known solution to obfuscate quantum code and output to prevent the leaking of any confidential information over the cloud. SPYCE implements a lightweight, scalable, and effective solution based on the unique principles of quantum computing to achieve this task.

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.
  • Quantum computing is an emerging paradigm that has shown great promise in accelerating large-scale scientific, optimization, and machine-learning workloads.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2307.16799 [2] arXiv https://arxiv.org/abs/2307.16799 [3] Publisher https://arxiv.org/abs/2307.16799

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