Verifiable cloud-based variational quantum algorithms
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Abstract (arXiv)
Variational quantum algorithms (VQAs) have shown potential for quantum advantage with noisy intermediate-scale quantum (NISQ) devices for quantum machine learning (QML). However, given the high cost and limited availability of quantum resources, delegating VQAs via cloud networks is a more practical solution for clients with limited quantum capabilities. Recently, Shingu et al.[Physical Review A, 105, 022603 (2022)] proposed a variational secure cloud quantum computing protocol, utilizing ancilla-driven quantum computation (ADQC) for cloud-based VQAs with minimal quantum resource consumption. However, their protocol lacks verifiability, which exposes it to potential malicious behaviors by the server. Additionally, channel loss requires frequent re-delegation as the size of the delegated variational circuit grows, complicating verification due to increased circuit complexity. This paper introduces a new protocol to address these challenges and enhance both verifiability and tolerance to channel loss in cloud-based VQAs.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 6 of its 63 references that are in the map. First posted on arXiv 25 Aug 2024.
Builds on
6 of 63 references.
References · 6
- 2020Variational Quantum Algorithmscited by 687
- 2016Quantum Machine Learningcited by 325
- 2021Federated Quantum Machine Learningcited by 82
- 2021Quantum federated learning through blind quantum computingcited by 44
- 2013Quantum support vector machine for big data classificationcited by 35
- 2022Multi-agent blind quantum computation without universal cluster statescited by 1
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Citing papers per year: 2025: 1.
1 paper.