PristiQ: A Co-Design Framework for Preserving Data Security of Quantum Learning in the Cloud
This addresses data security for users of cloud-based quantum machine learning, though it is incremental as it builds on existing QaaS paradigms.
The paper tackles the risk of data leakage in quantum machine learning (QaaS) by proposing PristiQ, a co-design framework that enhances data security through encryption subcircuits and maintains model performance via an automatic search algorithm, with experimental validation on simulations and IBM quantum computers.
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.