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

TL;DR

PristiQ is a co-design framework that enhances data security in quantum machine learning over cloud services by encrypting quantum circuits with optimized security keys, ensuring data privacy without sacrificing model performance.

Contribution

It introduces an encryption method with an automatic optimization algorithm to secure quantum data in QML on untrusted cloud platforms, balancing security and performance.

Findings

PristiQ significantly improves data security in quantum machine learning.

Experimental results show maintained model performance with enhanced security.

Validated on both simulation and IBM quantum hardware.

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 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.