Quantum computing holds tremendous potential for various applications, but its security remains a crucial concern. Quantum circuits need high-quality compilers to optimize the depth and gate count to boost the success probability on current noisy quantum computers. There is a rise of efficient but unreliable/untrusted compilers; however, they present a risk of tampering, such as malicious insertion of Trojans, which can degrade circuit performance and reliability. This work focuses on analyzing the impact of Trojans in Quantum Approximate Optimization Algorithm (QAOA) circuits, which are widely used for solving combinatorial optimization problems. We propose a methodology to reveal vulnerable locations and adversarial gate types for Trojan insertion that maximizes the negative impact on QAOA’s approximation ratio in solving Max-Cut problem. By disrupting critical paths and altering qubit states, the strategic insertion of additional gates degrade the approximation ratio by up to 50% based on evaluations on benchmark graphs. These insights on plausible attack mechanisms advance the understanding of optimization-oriented Trojan vulnerabilities specific to quantum computing. Additionally, a Convolutional Neural Network (CNN) model, referred to as QTrojanNet, is presented to detect the presence of Trojans in compiled QAOA circuits by learning inherent features that indicate malicious modifications. Experimental results showcase an average accuracy of 98.80% and an average F1-score of 98.53% in effectively detecting and classifying Trojan-inserted QAOA circuits.Quantum computing holds tremendous potential for various applications, but its security remains a crucial concern. Quantum circuits need high-quality compilers to optimize the depth and gate count to boost the success probability on current noisy quantum computers. There is a rise of efficient but unreliable/untrusted compilers; however, they present a risk of tampering, such as malicious insertion of Trojans, which can degrade circuit performance and reliability. This work focuses on analyzing the impact of Trojans in Quantum Approximate Optimization Algorithm (QAOA) circuits, which are widely used for solving combinatorial optimization problems. We propose a methodology to reveal vulnerable locations and adversarial gate types for Trojan insertion that maximizes the negative impact on QAOA’s approximation ratio in solving Max-Cut problem. By disrupting critical paths and altering qubit states, the strategic insertion of additional gates degrade the approximation ratio by up to 50% based on evaluations on benchmark graphs. These insights on plausible attack mechanisms advance the understanding of optimization-oriented Trojan vulnerabilities specific to quantum computing. Additionally, a Convolutional Neural Network (CNN) model, referred to as QTrojanNet, is presented to detect the presence of Trojans in compiled QAOA circuits by learning inherent features that indicate malicious modifications. Experimental results showcase an average accuracy of 98.80% and an average F1-score of 98.53% in effectively detecting and classifying Trojan-inserted QAOA circuits.

International Symposium on Quality Electronic Design (ISQED)Apr 3, 20248 citations

Hardware Trojans in Quantum Circuits, Their Impacts, and Defense

Rupshali Roy, Subrata K. Das, Swaroop Ghosh

The reliability of the outcome of a quantum circuit in near-term noisy quantum computers depends on the gate count and depth for a given problem. Circuits with a short depth and lower gate count can yield the correct solution more often than the variant with a higher gate count and depth. To work successfully for Noisy Intermediate Scale Quantum (NISQ) computers, quantum circuits need to be optimized efficiently using a compiler that decomposes high-level gates to native gates of the hardware. Many 3rd party compilers are being developed for lower compilation time, reduced circuit depth, and lower gate count for large quantum circuits. Such compilers, or even a specific release version of a compiler that is otherwise trustworthy, may be unreliable and give rise to security risks such as insertion of a quantum trojan during compilation that evades detection due to the lack of a golden/Oracle model in quantum computing. Trojans may corrupt the functionality to give flipped probabilities of basis states, or result in a lower probability of correct basis states in the output. In this paper, we investigate and discuss the impact of a single qubit Trojan (we have chosen a Hadamard gate and a NOT gate) inserted one at a time at various locations in benchmark quantum circuits without changing the the depth of the circuit. Results indicate an average of 16.18% degradation for the Hadamard Trojan without noise, and 7.78% with noise. For the NOT Trojan (with noise) there is 14.6% degradation over all possible inputs. We then discuss the detection of such Trojans in a quantum circuit using CNN-based classifier achieving an accuracy of 90%.The reliability of the outcome of a quantum circuit in near-term noisy quantum computers depends on the gate count and depth for a given problem. Circuits with a short depth and lower gate count can yield the correct solution more often than the variant with a higher gate count and depth. To work successfully for Noisy Intermediate Scale Quantum (NISQ) computers, quantum circuits need to be optimized efficiently using a compiler that decomposes high-level gates to native gates of the hardware. Many 3rd party compilers are being developed for lower compilation time, reduced circuit depth, and lower gate count for large quantum circuits. Such compilers, or even a specific release version of a compiler that is otherwise trustworthy, may be unreliable and give rise to security risks such as insertion of a quantum trojan during compilation that evades detection due to the lack of a golden/Oracle model in quantum computing. Trojans may corrupt the functionality to give flipped probabilities of basis states, or result in a lower probability of correct basis states in the output. In this paper, we investigate and discuss the impact of a single qubit Trojan (we have chosen a Hadamard gate and a NOT gate) inserted one at a time at various locations in benchmark quantum circuits without changing the the depth of the circuit. Results indicate an average of 16.18% degradation for the Hadamard Trojan without noise, and 7.78% with noise. For the NOT Trojan (with noise) there is 14.6% degradation over all possible inputs. We then discuss the detection of such Trojans in a quantum circuit using CNN-based classifier achieving an accuracy of 90%.

arXiv:2402.01552International Symposium on Quality Electronic Design (ISQED)Apr 3, 202410 citations

Quantum Trojan Insertion: Controlled Activation for Covert Circuit Manipulation

John, Jayden, Lakshman Golla, Qian Wang

Quantum computing has demonstrated superior efficiency compared to classical computing. Quantum circuits are essential for implementing functions and achieving correct computational outcomes. Quantum circuit compilers, which translate high-level quantum operations into hardware-specific gates while optimizing performance, serve as the interface between the quantum software stack and physical quantum machines. However, untrusted compilers can introduce malicious hardware Trojans into quantum circuits, altering their functionality and leading to incorrect results. In the world of classical computing, effective hardware Trojans are a critical threat to integrated circuits. This process often involves stealthily inserting conditional logic gates that activate under specific input conditions. In this paper, we propose a novel advanced quantum Trojan that is controllable, allowing it to be activated or deactivated under different circumstances. These Trojans remain dormant until triggered by predefined input conditions, making detection challenging. Through a series of benchmark experiments, we demonstrate the feasibility of this method by evaluating the effectiveness of embedding controlled trojans in quantum circuits and measuring their impact on circuit performance and security.Quantum computing has demonstrated superior efficiency compared to classical computing. Quantum circuits are essential for implementing functions and achieving correct computational outcomes. Quantum circuit compilers, which translate high-level quantum operations into hardware-specific gates while optimizing performance, serve as the interface between the quantum software stack and physical quantum machines. However, untrusted compilers can introduce malicious hardware Trojans into quantum circuits, altering their functionality and leading to incorrect results. In the world of classical computing, effective hardware Trojans are a critical threat to integrated circuits. This process often involves stealthily inserting conditional logic gates that activate under specific input conditions. In this paper, we propose a novel advanced quantum Trojan that is controllable, allowing it to be activated or deactivated under different circumstances. These Trojans remain dormant until triggered by predefined input conditions, making detection challenging. Through a series of benchmark experiments, we demonstrate the feasibility of this method by evaluating the effectiveness of embedding controlled trojans in quantum circuits and measuring their impact on circuit performance and security.

arXiv:2502.08880Feb 13, 20250 citations

Stealthy Conditional Trojans in Quantum Circuits

Jayden John, Lakshman Golla, Qian Wang

International Symposium on Quality Electronic Design (ISQED)Apr 23, 20254 citations

Quantum Properties Trojans (QuPTs) for Attacking Quantum Neural Networks

Sounak Bhowmik, Travis S. Humble, Himanshu Thapliyal

Quantum neural networks (QNN) hold immense potential for the future of quantum machine learning (QML). However, QNN security and robustness remain largely unexplored. In this work, we proposed novel Trojan attacks based on the quantum computing properties in a QNN-based binary classifier. Our proposed Quantum Properties Trojans (QuPTs) are based on the unitary property of quantum gates to insert noise and Hadamard gates to enable superposition to develop Trojans and attack QNNs. We showed that the proposed QuPTs are significantly stealthier and heavily impact the quantum circuits’ performance, specifically QNNs. The most impactful QuPT caused a deterioration of $23 \%$ accuracy of the compromised QNN under the experimental setup. To the best of our knowledge, this is the first work on the Trojan attack on a fully quantum neural network independent of any hybrid classical-quantum architecture.Quantum neural networks (QNN) hold immense potential for the future of quantum machine learning (QML). However, QNN security and robustness remain largely unexplored. In this work, we proposed novel Trojan attacks based on the quantum computing properties in a QNN-based binary classifier. Our proposed Quantum Properties Trojans (QuPTs) are based on the unitary property of quantum gates to insert noise and Hadamard gates to enable superposition to develop Trojans and attack QNNs. We showed that the proposed QuPTs are significantly stealthier and heavily impact the quantum circuits’ performance, specifically QNNs. The most impactful QuPT caused a deterioration of $23 \%$ accuracy of the compromised QNN under the experimental setup. To the best of our knowledge, this is the first work on the Trojan attack on a fully quantum neural network independent of any hybrid classical-quantum architecture.

IEEE Computer Society Annual Symposium on VLSIJul 6, 20254 citations

Quantum Computing Based Neural Networks for Anomaly Classification in燫eal-Time Surveillance Videos

MD. Yasar Arafath, Ajit Kumar

For intelligent surveillance videos, anomaly detection is extremely important. Deep learning algorithms have been popular for evaluating real-time surveillance recordings, like traffic accidents, and criminal or unlawful incidents such as suicide attempts. Nevertheless, Deep learning methods for classification, like convolutional neural networks, necessitate a lot of computing power. Quantum computing is a branch of technology that solves abnormal and complex problems using quantum mechanics. As a result, the focus of this research is on developing a hybrid quantum computing model which is based on deep learning. This research develops a Quantum Computing-based Convolutional Neural Network (QC-CNN) to extract features and classify anomalies from surveillance footage. A Quantum-based Circuit, such as the real amplitude circuit, is utilized to improve the performance of the model. As far as my research, this is the first work to employ quantum deep learning techniques to classify anomalous events in video surveillance applications. There are 13 anomalies classified from the UCF-crime dataset. Based on experimental results, the proposed model is capable of efficiently classifying data concerning confusion matrix, Receiver Operating Characteristic (ROC), accuracy, Area Under Curve (AUC), precision, recall as well as F1-score. The proposed QC-CNN has attained the best accuracy of 95.65 percent which is 5.37% greater when compared to other existing models. To measure the efficiency of the proposed work, QC-CNN is also evaluated with classical and quantum models.For intelligent surveillance videos, anomaly detection is extremely important. Deep learning algorithms have been popular for evaluating real-time surveillance recordings, like traffic accidents, and criminal or unlawful incidents such as suicide attempts. Nevertheless, Deep learning methods for classification, like convolutional neural networks, necessitate a lot of computing power. Quantum computing is a branch of technology that solves abnormal and complex problems using quantum mechanics. As a result, the focus of this research is on developing a hybrid quantum computing model which is based on deep learning. This research develops a Quantum Computing-based Convolutional Neural Network (QC-CNN) to extract features and classify anomalies from surveillance footage. A Quantum-based Circuit, such as the real amplitude circuit, is utilized to improve the performance of the model. As far as my research, this is the first work to employ quantum deep learning techniques to classify anomalous events in video surveillance applications. There are 13 anomalies classified from the UCF-crime dataset. Based on experimental results, the proposed model is capable of efficiently classifying data concerning confusion matrix, Receiver Operating Characteristic (ROC), accuracy, Area Under Curve (AUC), precision, recall as well as F1-score. The proposed QC-CNN has attained the best accuracy of 95.65 percent which is 5.37% greater when compared to other existing models. To measure the efficiency of the proposed work, QC-CNN is also evaluated with classical and quantum models.

Computer Systems Science and EngineeringJan 1, 20238 citations

23 \%

accuracy of the compromised QNN under the experimental setup. To the best of our knowledge, this is the first work on the Trojan attack on a fully quantum neural network independent of any hybrid classical-quantum architecture.