Cloud hosting of quantum machine learning (QML) models exposes them to a range of vulnerabilities, the most significant of which is the model stealing attack. In this study, we assess the efficacy of such attacks in the realm of quantum computing. Our findings revealed that model stealing attacks can produce clone models achieving up to 0.9 × and 0.99 × clone test accuracy when trained using Top-1 and Top-k labels, respectively (k: num_classes). To defend against these attacks, we propose: 1) hardware variation-induced perturbation (HVIP) and 2) hardware and architecture variation-induced perturbation (HAVIP). Despite limited success with our defense techniques, it has led to an important discovery: QML models trained on noisy hardwares are naturally resistant to perturbation or obfuscation-based defenses or attacks.

Recent assertions of a potential advantage of Quantum Neural Network (QNN) for specific Machine Learning (ML) tasks have sparked the curiosity of a sizable number of application researchers. The parameterized quantum circuit (PQC), a major building block of a QNN, consists of several layers of single-qubit rotations and multi-qubit entanglement operations. The optimum number of PQC layers for a particular ML task is generally unknown. A larger network often provides better performance in noiseless simulations. However, it may perform poorly on hardware compared to a shallower network. Because the amount of noise varies amongst quantum devices, the optimal depth of PQC can vary significantly. Additionally, the gates chosen for the PQC may be suitable for one type of hardware but not for another due to compilation overhead. This makes it difficult to generalize a QNN design to wide range of hardware and noise levels. An alternate approach is to build and train multiple QNN models targeted for each hardware which can be expensive. To circumvent these issues, we introduce the concept of knowledge distillation in QNN using approximate synthesis. The proposed approach will create a new QNN network with (i) a reduced number of layers or (ii) a different gate set without having to train it from scratch. Training the new network for a few epochs can compensate for the loss caused by approximation error. Through empirical analysis, we demonstrate ≈71.4% reduction in circuit layers, and still achieve ≈16.2% better accuracy under noise.

In the last few years, quantum computing has experienced a growth spurt. One exciting avenue of quantum computing is quantum machine learning (QML) which can exploit the high dimensional Hilbert space to learn richer representations from limited data and thus can efficiently solve complex learning tasks. Despite the increased interest in QML, there have not been many studies that discuss the security aspects of QML. In this work, we explored the possible future applications of QML in the hardware security domain. We also expose the security vulnerabilities of QML and emerging attack models, and corresponding countermeasures.

Using quantum computing, this paper addresses two scientifically-pressing and day to day-relevant problems, namely, chemical retrosynthesis which is an important step in drug/material discovery and security of semiconductor supply chain. We show that Quantum Long Short-Term Memory (QLSTM) is a viable tool for retrosynthesis. We achieve 65% training accuracy with QLSTM whereas classical LSTM can achieve 100%. However, in testing we achieve 80% accuracy with the QLSTM while classical LSTM peaks at only 70% accuracy! We also demonstrate an application of Quantum Neural Network (QNN) in the hardware security domain, specifically in Hardware Trojan (HT) detection using a set of power and area Trojan features. The QNN model achieves detection accuracy as high as 97.27%.

Neural Networks (NNs) are being extensively used in critical applications such as aerospace, healthcare, autonomous driving, and military, to name a few. Limited precision of the underlying hardware platforms, permanent and transient faults injected unintentionally as well as maliciously, and voltage/temperature fluctuations can potentially result in malfunctions in NNs with consequences ranging from substantial reduction in the network accuracy to jeopardizing the correct prediction of the network in worst cases. To alleviate such reliability concerns, this paper discusses the state-of-the-art reliability enhancement schemes that can be tailored for deep learning accelerators. We will discuss the errors associated with the hardware implementation of Deep-Learning (DL) algorithms along with their corresponding countermeasures. An in-field self-test methodology with a high test coverage is introduced, and an accurate high-level framework, so-called FIdelity, is proposed that enables the designers to evaluate DL accelerators in presence of such errors. Then, a state-of-the-art robustness-preserving training algorithm based on the Hessian Regularization is introduced. This algorithm alleviates the perturbations during inference time with negligible degradation in the accuracy of the network. Finally, Quantum Neural Networks (QNNs) and the methods to make them resilient against a variety of vulnerabilities such as fault injection, spatial and temporal variations in Qubits, and noise in QNNs are discussed.

Image classification is a major application domain for conventional deep learning (DL). Quantum machine learning (QML) has the potential to revolutionize image classification. In any typical DL-based image classification, we use convolutional neural network (CNN) to extract features from the image and multi-layer perceptron network (MLP) to create the actual decision boundaries. QML models can be useful in both of these tasks. On one hand, convolution with parameterized quantum circuits (Quanvolution) can extract rich features from the images. On the other hand, quantum neural network (QNN) models can create complex decision boundaries. Therefore, Quanvolution and QNN can be used to create an end-to-end QML model for image classification. Alternatively, we can extract image features separately using classical dimension reduction techniques such as, Principal Components Analysis (PCA) or Convolutional Autoen-coder (CAE) and use the extracted features to train a QNN. We review two proposals on quantum-classical hybrid ML models for image classification namely, Quanvolutional Neural Network and dimension reduction using a classical algorithm followed by QNN. Particularly, we make a case for trainable filters in Quanvolution and CAE-based feature extraction for image datasets (instead of dimension reduction using linear transformations such as, PCA). We discuss various design choices, potential opportunities, and drawbacks of these models. We also release a Python-based framework to create and explore these hybrid models with a variety of design choices.