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

arXiv:2404.13475v1 Announce Type: cross
Abstract: 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 …

abstract arxiv as-a-service cloud cloud computing cloud services computers computing cs.ai cs.cr cs.et cs.lg data data leakage data security design framework however machine machine learning qml quant-ph quantum quantum computers risk security service services stage type via