Quantum computing (QC) has the potential to rev-olutionize fields like machine learning, security, and healthcare. Quantum machine learning (QML) has emerged as a promising area, enhancing learning algorithms using quantum computers. However, QML models are lucrative targets due to their high training costs and extensive training times. The scarcity of quantum resources and long wait times further exacerbate the challenge. Additionally, QML providers may rely on a third-party quantum cloud for hosting the model, exposing the models and training data. As QML-as-a-Service (QMLaaS) becomes more prevalent, reliance on third party quantum clouds can pose a significant threat. This paper shows that adversaries in quantum clouds can use white-box access of the QML model during training to extract the state preparation circuit (containing training data) along with the labels. The extracted training data can be reused for training a clone model or sold for profit. We propose a suite of techniques to prune and fix the incorrect labels. Results show that ≈90% labels can be extracted correctly. The same model trained on the adversarially extracted data achieves approximately ≈90% accuracy, closely matching the accuracy achieved when trained on the original data. To mitigate this threat, we propose masking labels/classes and modifying the cost function for label obfuscation, reducing adversarial label prediction accuracy by ≈70%.
Quantum computing (QC) has the potential to rev-olutionize fields like machine learning, security, and healthcare. Quantum machine learning (QML) has emerged as a promising area, enhancing learning algorithms using quantum computers. However, QML models are lucrative targets due to their high training costs and extensive training times. The scarcity of quantum resources and long wait times further exacerbate the challenge. Additionally, QML providers may rely on a third-party quantum cloud for hosting the model, exposing the models and training data. As QML-as-a-Service (QMLaaS) becomes more prevalent, reliance on third party quantum clouds can pose a significant threat. This paper shows that adversaries in quantum clouds can use white-box access of the QML model during training to extract the state preparation circuit (containing training data) along with the labels. The extracted training data can be reused for training a clone model or sold for profit. We propose a suite of techniques to prune and fix the incorrect labels. Results show that ≈90% labels can be extracted correctly. The same model trained on the adversarially extracted data achieves approximately ≈90% accuracy, closely matching the accuracy achieved when trained on the original data. To mitigate this threat, we propose masking labels/classes and modifying the cost function for label obfuscation, reducing adversarial label prediction accuracy by ≈70%.
Quantum computing (QC) has the potential to revolutionize fields like machine learning, security, and healthcare. Quantum machine learning (QML) has emerged as a promising area, enhancing learning algorithms using quantum computers. However, QML models are lucrative targets due to their high training costs and extensive training times. The scarcity of quantum resources and long wait times further exacerbate the challenge. Additionally, QML providers may rely on third-party quantum clouds for hosting models, exposing them and their training data to potential threats. As QML-as-a-Service (QMLaaS) becomes more prevalent, reliance on third-party quantum clouds poses a significant security risk. This work demonstrates that adversaries in quantum cloud environments can exploit white-box access to QML models to infer the user's encoding scheme by analyzing circuit transpilation artifacts. The extracted data can be reused for training clone models or sold for profit. We validate the proposed attack through simulations, achieving high accuracy in distinguishing between encoding schemes. We report that ≈95% of the time, the encoding can be predicted correctly. To mitigate this threat, we propose a transient obfuscation layer that masks encoding fingerprints using randomized rotations and entanglement, reducing adversarial detection to near-random chance ≈42%, with a depth overhead of ≈8.5% for a 5-layer QNN design.
Quantum machine learning (QML) is a category of algorithms that employ variational quantum circuits (VQCs) to tackle machine learning tasks. Recent discoveries have shown that QML models can effectively generalize from limited training data samples. This capability has sparked increased interest in deploying these models to address practical, real-world challenges, resulting in the emergence of Quantum Machine Learning as a Service (QMLaaS). QMLaaS represents a hybrid model that utilizes both classical and quantum computing resources. Classical computers play a crucial role in this setup, handling initial pre-processing and subsequent post-processing of data to compensate for the current limitations of quantum hardware. Since this is a new area, very little work exists to paint the whole picture of QMLaaS in the context of known security threats in the domain of classical and quantum machine learning. This SoK paper is aimed to bridge this gap by outlining the complete QMLaaS workflow, which encompasses both the training and inference phases and highlighting significant security concerns involving untrusted classical or quantum providers. QML models contain several sensitive assets, such as the model architecture, training/testing data, encoding techniques, and trained parameters. Unauthorized access to these components could compromise the model's integrity and lead to intellectual property (IP) theft. We pinpoint the critical security issues that must be considered to pave the way for a secure QMLaaS deployment.
Quantum Machine Learning (QML) is an amalgamation of quantum computing paradigms with machine learning models, providing significant prospects for solving complex problems. However, with the expansion of numerous third-party vendors in the Noisy Intermediate-Scale Quantum (NISQ) era of quantum computing, the security of QML models is of prime importance, particularly against reverse engineering, which could expose sensitive parameters and proprietary algorithms embedded within the models. We assume the untrusted third-party quantum cloud provider is an adversary having white-box access to the transpiled version of the user-designed trained QML model during inference. Although the adversary can steal and use the model without any modification, reverse engineering (RE) to extract the pre-transpiled copy of the QML circuit will enable re-transpilation and usage of the model for various hardware with completely different native gate sets and even different qubit technology. The information about the parameters (e.g., number of parameters, their placements, and optimized values) can allow further training of the QML model if the adversary plans to alter the QML model to tamper with the watermark and/or embed their own watermark or refine the model for other purposes. In this first effort to investigate the RE of QML circuits, we examine quantum classifiers by comparing the training accuracy of original and reverse-engineered models across various sizes (i.e., number of qubits and number of parametric layers) of Quantum Neural Networks (QNNs). We note that multi-qubit classifiers can be reverse-engineered under specific conditions with a mean error of order 10^-2 in a reasonable time. We also propose adding dummy rotation gates in the QML model with fixed parameters to increase the RE overhead for defense. For instance, an addition of 2 dummy qubits and 2 layers increases the overhead by ~1.76 times for a classifier with 2 qubits and 3 layers with a performance overhead of less than 9%. We note that RE is a very powerful attack model which warrants further efforts on defenses.
Security has always been a critical issue in machine learning (ML) applications. Due to the high cost of model training – such as collecting relevant samples, labeling data, and consuming computing power – model-stealing attack is one of the most fundamental but vitally important issues. When it comes to quantum computing, such a quantum machine learning (QML) model-stealing attack also exists and is even more severe because the traditional encryption method, such as homomorphic encryption can hardly be directly applied to quantum computation. On the other hand, due to the limited quantum computing resources, the monetary cost of training QML model can be even higher than classical ones in the near term. Therefore, a well-tuned QML model developed by a third-party company can be delegated to a quantum cloud provider as a service to be used by ordinary users. In this case, the QML model is likely to be leaked if the cloud provider is under attack. To address such a problem, we propose a novel framework, namely QuMoS, to preserve model security. Instead of applying encryption algorithms, we propose to divide the complete QML model into multiple parts and distribute them to multiple physically isolated quantum cloud providers for execution. As such, even if the adversary in a single provider can obtain a partial model, it does not have sufficient information to retrieve the complete model. Although promising, we observed that an arbitrary model design under distributed settings cannot provide model security. We further developed a reinforcement learning-based security engine, which can automatically optimize the model design under the distributed setting, such that a good trade-off between model performance and security can be made. Experimental results on four datasets show that the model design proposed by QuMoS can achieve a close accuracy to the model designed with neural architecture search under centralized settings while providing the highest security than the baselines.
Quantum machine learning (QML) is a rapidly emerging area of research, driven by the capabilities of Noisy Intermediate-Scale Quantum (NISQ) devices. With the progress in the research of QML models, there is a rise in third-party quantum cloud services to cater to the increasing demand for resources. New security concerns surface, specifically regarding the protection of intellectual property (IP) from untrustworthy service providers. One of the most pressing risks is the potential for reverse engineering (RE) by malicious actors who may steal proprietary quantum IPs such as trained parameters and QML architecture, modify them to remove additional watermarks or signatures, and re-transpile them for other quantum hardware. Prior work presents a brute force approach to RE the QML parameters which takes exponential time overhead. In this paper, we introduce an autoencoder-based approach to extract the parameters from transpiled QML models deployed on untrusted third-party vendors. We experiment on multi-qubit classifiers and note that they can be reverse-engineered under restricted conditions with a mean error of order 102−1. The amount of time taken to prepare the dataset and train the model to reverse engineer the QML circuit being of the order 103seconds (which is 102× better than the previously reported value for 4-layered 4-qubit classifiers) makes the threat of RE highly potent, underscoring the need for continued development of effective defenses.
The high costs of existing quantum cloud services, combined with the growing demand for quantum resources, could drive the development of more affordable but potentially untrusted quantum cloud providers. Deploying or hosting quantum models, such as quantum neural networks (QNNs), on these untrusted platforms introduces numerous security concerns, with the most critical being model theft. This vulnerability arises from the cloud provider's full access to the circuits during training and/or inference. In this work, we introduce STIQ, a novel ensemble-based strategy designed to safeguard QNNs against such cloud-based adversaries. Our method innovatively trains two distinct QNNs concurrently, hosting them on the same or different platforms, in such a way that each network yields obfuscated outputs, rendering the individual QNNs ineffective for adversaries operating within cloud environments. However, when these outputs are combined locally (using an aggregate function), they reveal the correct result. Through extensive experiments across various QNNs and datasets, our technique has proven to effectively mask the accuracy and losses of the individually hosted models by up to 76 %, albeit at the expense of a≤2×increase in total computational overhead. This trade-off, however, is a small price to pay for the enhanced security and integrity of QNNs in cloud-based environments prone to untrusted adversaries. We also demonstrated STIQ's practical application by evaluating it on multiple real quantum hardwares, showing that STIQ achieves up to≈70%obfuscation, with combined performance comparable to an unobfuscated model.