mlsanityData to 7 Oct 2026

PristiQ: A Co-Design Framework for Preserving Data Security of Quantum Learning in the Cloud

arXiv 2404.13475 ↗First posted 20 Apr 2024quant-ph cs.AI cs.CRcited by 5

Not on the map. Why?

Abstract (arXiv)

Benefiting from cloud computing, today's early-stage quantum computers can be remotely accessed via the cloud services, known as Quantum-as-a-Service (QaaS). However, it poses a high risk of data leakage in quantum machine learning (QML). To run a QML model with QaaS, users need to locally compile their quantum circuits including the subcircuit of data encoding first and then send the compiled circuit to the QaaS provider for execution. If the QaaS provider is untrustworthy, the subcircuit to encode the raw data can be easily stolen. Therefore, we propose a co-design framework for preserving the data security of QML with the QaaS paradigm, namely PristiQ. By introducing an encryption subcircuit with extra secure qubits associated with a user-defined security key, the security of data can be greatly enhanced. And an automatic search algorithm is proposed to optimize the model to maintain its performance on the encrypted quantum data. Experimental results on simulation and the actual IBM quantum computer both prove the ability of PristiQ to provide high security for the quantum data while maintaining the model performance in QML.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 27 of its 47 references that are in the map. First posted on arXiv 20 Apr 2024.

Builds on

27 of 47 references.

References · 27

  1. 2016Deep Learning with Differential Privacycited by 2,682
  2. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 528
  3. 2020The effect of data encoding on the expressive power of variational quantum machine learning modelscited by 328
  4. 2018Gazelle: A Low Latency Framework for Secure Neural Network Inferencecited by 193
  5. 2018Quantum generative adversarial networkscited by 128
  6. 2018Slalom: Fast, Verifiable and Private Execution of Neural Networks in Trusted Hardwarecited by 101
  7. 2021QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuitscited by 97
  8. 2020Recurrent Quantum Neural Networkscited by 79
  9. 2020Local Differential Privacy based Federated Learning for Internet of Thingscited by 74
  10. 2018Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applicationscited by 67
  11. 2019Local Differential Privacy for Deep Learningcited by 46
  12. 2017Quantum Neuron: an elementary building block for machine learning on quantum computerscited by 34
15 more
  1. 2023AutoReP: Automatic ReLU Replacement for Fast Private Network Inferencecited by 29
  2. 2021QuantumNAT: Quantum Noise-Aware Training with Noise Injection, Quantization and Normalizationcited by 27
  3. 2023LinGCN: Structural Linearized Graph Convolutional Network for Homomorphically Encrypted Inferencecited by 26
  4. 2020A Co-Design Framework of Neural Networks and Quantum Circuits Towards Quantum Advantagecited by 23
  5. 2022QOC: Quantum On-Chip Training with Parameter Shift and Gradient Pruningcited by 23
  6. 2021Can Noise on Qubits Be Learned in Quantum Neural Network? A Case Study on QuantumFlowcited by 13
  7. 2022Quantum Neural Network Compressioncited by 12
  8. 2023RRNet: Towards ReLU-Reduced Neural Network for Two-party Computation Based Private Inferencecited by 12
  9. 2021Exploration of Quantum Neural Architecture by Mixing Quantum Neuron Designscited by 11
  10. 2016Private quantum computation: An introduction to blind quantum computing and related protocolscited by 10
  11. 2017Classical Homomorphic Encryption for Quantum Circuitscited by 6
  12. 2023QuMoS: A Framework for Preserving Security of Quantum Machine Learning Modelcited by 5
  13. 2023A Novel Spatial-Temporal Variational Quantum Circuit to Enable Deep Learning on NISQ Devicescited by 5
  14. 2020DarKnight: A Data Privacy Scheme for Training and Inference of Deep Neural Networkscited by 4
  15. 2023VENUS: A Geometrical Representation for Quantum State Visualizationcited by 4

Built on by

Cited by year

Citing papers per year: 2024: 5.

5 papers.

  1. 2024Deep Learning-based Anomaly Detection and Log Analysis for Computer Networkscited by 23
  2. 2024Image anomaly detection and prediction scheme based on SSA optimized ResNet50-BiGRU modelcited by 18
  3. 2024Advanced AI Framework for Enhanced Detection and Assessment of Abdominal Trauma: Integrating 3D Segmentation with 2D CNN and RNN Modelscited by 9
  4. 2024The Quantum Imitation Game: Reverse Engineering of Quantum Machine Learning Modelscited by 2
  5. 2024Security Concerns in Quantum Machine Learning as a Servicecited by 1