Private quantum computation: An introduction to blind quantum computing and related protocols
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Abstract (arXiv)
Quantum technologies hold the promise of not only faster algorithmic processing of data, via quantum computation, but also of more secure communications, in the form of quantum cryptography. In recent years, a number of protocols have emerged which seek to marry these concepts for the purpose of securing computation rather than communication. These protocols address the task of securely delegating quantum computation to an untrusted device while maintaining the privacy, and in some instances the integrity, of the computation. We present a review of the progress to date in this emerging area.
Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 Nov 2016.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 5; 2024: 1; 2025: 0; 2026: 2.
10 papers.
- 2021Quantum federated learning through blind quantum computingcited by 44
- 2023Privacy-preserving quantum federated learning via gradient hidingcited by 15
- 2023Blind quantum machine learning with quantum bipartite correlatorcited by 8
- 2023QuMoS: A Framework for Preserving Security of Quantum Machine Learning Modelcited by 6
- 2023Classical Verification of Quantum Learningcited by 6
- 2024PristiQ: A Co-Design Framework for Preserving Data Security of Quantum Learning in the Cloudcited by 5
- 2022Data centers with quantum random access memory and quantum networkscited by 4
- 2023Enigma: Application-Layer Privacy for Quantum Optimization on Untrusted Computerscited by 2
- 2026Equivariant Quantum Clustering with Differential Privacy: Parameter-Efficient Privacy-Preserving Analysis Across Heterogeneous Sensitive Datasetscited by 0
- 2026Encryptability As a Coordinate Choice: Depth-One Homomorphic Federated Learning of Quantum Neural Networkscited by 0
- 2026Encryptability As a Coordinate Choice: Depth-One Homomorphic Federated Learning of Quantum Neural Networks24 Sep 2026
- 2026Equivariant Quantum Clustering with Differential Privacy: Parameter-Efficient Privacy-Preserving Analysis Across Heterogeneous Sensitive Datasets9 Jul 2026
- 2024PristiQ: A Co-Design Framework for Preserving Data Security of Quantum Learning in the Cloud20 Apr 2024
- 2023Privacy-preserving quantum federated learning via gradient hiding7 Dec 2023
- 2023Enigma: Application-Layer Privacy for Quantum Optimization on Untrusted Computers22 Nov 2023
- 2023Blind quantum machine learning with quantum bipartite correlator19 Oct 2023
- 2023Classical Verification of Quantum Learning8 Jun 2023
- 2023QuMoS: A Framework for Preserving Security of Quantum Machine Learning Model23 Apr 2023
- 2022Data centers with quantum random access memory and quantum networks28 Jul 2022
- 2021Quantum federated learning through blind quantum computing15 Mar 2021