PristiQ: A Co-Design Framework for Preserving Data Security of Quantum
  Learning in the Cloud
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.
Zhepeng Wang Yi Sheng Nirajan Koirala Kanad Basu Taeho Jung Cheng-Chang Lu Weiwen Jiang