mlsanityData to 7 Oct 2026

Verifiable cloud-based variational quantum algorithms

arXiv 2408.13713 ↗First posted 25 Aug 2024quant-ph cs.LGcited by 1

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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

  1. 2020Variational Quantum Algorithmscited by 687
  2. 2016Quantum Machine Learningcited by 325
  3. 2021Federated Quantum Machine Learningcited by 82
  4. 2021Quantum federated learning through blind quantum computingcited by 44
  5. 2013Quantum support vector machine for big data classificationcited by 35
  6. 2022Multi-agent blind quantum computation without universal cluster statescited by 1

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Citing papers per year: 2025: 1.

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