Krishnendu Guha

Samuel Punch, Krishnendu Guha, Utz Roedig

Quantum circuit cutting enables near-term quantum devices to execute workloads exceeding their qubit capacity by decomposing circuits into independently runnable fragments. While this extends computational reach, it creates a previously unexplored confidentiality surface: the fragment-level execution transcript observable by a semi-honest cloud provider. We formalise this surface and demonstrate that post-cut transcripts constitute a practical metadata side channel. Operating solely on provider-visible compiled circuit metadata (fragment width, depth, and two-qubit gate count), we evaluate a structured inference attack across six classification objectives spanning algorithm identity, cut mechanism, and coarse Hamiltonian structure. Our corpus comprises 1,200 circuit fragments across eight algorithm families transpiled against three hardware topologies, validated on a 156-qubit production quantum computer confirming that QPU execution time remains invariant across a 25x variation in compiled depth. Under strict instance-disjoint generalisation, our attack recovers algorithm family with 0.960 accuracy (AUC 0.999), cut mechanism with 0.847 accuracy (AUC 0.924), and Hamiltonian k-locality with 0.960 accuracy (AUC 0.998). Connectivity and geometry inference achieve AUC of 0.986 and 0.942 with strong stability under size-holdout. Topology inference remains above chance (AUC 0.666). A matched-footprint control and ablation study confirm leakage is structure-dominated and not explained by scale artefacts. These results demonstrate that circuit cutting is not confidentiality-neutral and that metadata leakage should be treated as a first-class security concern in quantum cloud systems.

Ninglin Ou, Mohammad A. Razzaque, Iftekher Islam Shovon et al.

The rapid growth of Electric Vehicle (EV) adoption challenges power distribution networks through peak load spikes, voltage instability, and transformer overloads from uncoordinated charging. While Model Predictive Control (MPC) and standard Reinforcement Learning (RL) methods have addressed these issues, existing approaches rarely treat real-time carbon intensity or fluctuating renewable energy (RE) availability as primary scheduling objectives, leaving substantial decarbonisation potential unrealised. This paper proposes an emission-aware RL strategy based on the Soft Actor Critic (SAC) algorithm, with a multi-objective reward that penalises carbon emissions, curtailed on-site renewables, and unmet user demand. The agent is trained within a unified benchmarking framework on the EV2Gym platform, incorporating behind-the-meter solar and wind profiles, time-varying EirGrid carbon intensity data, and realistic workplace EV behaviour across 25 Electric Vehicle Supply Equipment (EVSE) units. Nine control strategies, including heuristics, emission-aware MPC variants, and the proposed RL agent, are compared under five renewable penetration scenarios (0%-50%) over ten independent runs each. The RL agent achieves a carbon intensity as low as 23.96 grams of carbon dioxide per kilowatt-hour under 50% wind penetration, representing up to 87% emission reduction versus the uncontrolled baseline, and outperforms the external graph-based Power Distribution Network (PDN) benchmark. Transformer overload remains below 7 kWh across scenarios, against up to 1093 kWh for the As Fast As Possible (AFAP) heuristic, and renewable self-consumption reaches 52% under combined wind and solar supply. Embedding carbon intensity forecasts into the RL state and reward aligns charging with low-emission periods while preserving grid compliance and user satisfaction.

Rourab Paul, Krishnendu Guha, Amlan Chakrabarti

Number Theoretic Transform (NTT) is the most essential component for polynomial multiplications used in lattice-based Post-Quantum Cryptography (PQC) algorithms such as Kyber, Dilithium, NTRU etc. However, side-channel attacks (SCA) and hardware vulnerabilities in the form of hardware Trojans may alter control signals to disrupt the circuit's control flow and introduce unconventional delays in the critical hardware of PQC. Hardware Trojans, especially on control signals, are more low cost and impactful than data signals because a single corrupted control signal can disrupt or bypass entire computation sequences, whereas data faults usually cause only localized errors. On the other hand, adversaries can perform Soft Analytical Side Channel Attacks (SASCA) on the design using the inserted hardware Trojan. In this paper, we present a secure NTT architecture capable of detecting unconventional delays, control-flow disruptions, and SASCA, while providing an adaptive fault-correction methodology for their mitigation. Extensive simulations and implementations of our Secure NTT on Artix-7 FPGA with different Kyber variants show that our fault detection and correction modules can efficiently detect and correct faults whether caused unintentionally or intentionally by hardware Trojans with a high success rate, while introducing only modest area and time overheads.

Andrew Nash, Dirk Pesch, Krishnendu Guha

With the ever-increasing range of applications of Internet in Things (IoT) and sensor networks, challenges are emerging in various categories of classification tasks. Applications such as vehicular networking, UAV swarm coordination and cyber-physical systems require global classification over distributed sensors, with tight constraints on communication and computation resources. There has been much research in decentralized and distributed data-exchange for communication-efficient collective inference. Likewise, there has been considerable research involving the use of cloud and edge computing paradigms for efficient task allocation. To the best of our knowledge, there has been no research on the integration of these two concepts to create a hybrid cloud and distributed approach that makes dynamic runtime communication strategy decisions. In this paper, we focus on aspects of combining distributed and hierarchical communication and classification approaches for collective inference. We derive optimal policies for agents that implement this hybrid approach, and evaluate their performance under various scenarios of the distribution of underlying data. Our analysis shows that this approach can maintain a high level of classification accuracy (comparable to that of centralised joint inference over all data), at reduced theoretical communication cost. We expect there is potential for our approach to facilitate efficient collective inference for real-world applications, including instances that involves more complex underlying data distributions.

Luke Power, Krishnendu Guha

Many Machine Learning (ML) models are referred to as black box models, providing no real insights into why a prediction is made. Feature importance and explainability are important for increasing transparency and trust in ML models, particularly in settings such as healthcare and finance. With quantum computing's unique capabilities, such as leveraging quantum mechanical phenomena like superposition, which can be combined with ML techniques to create the field of Quantum Machine Learning (QML), and such techniques may be applied to QML models. This article explores feature importance and explainability insights in QML compared to Classical ML models. Utilizing the widely recognized Iris dataset, classical ML algorithms such as SVM and Random Forests, are compared against hybrid quantum counterparts, implemented via IBM's Qiskit platform: the Variational Quantum Classifier (VQC) and Quantum Support Vector Classifier (QSVC). This article aims to provide a comparison of the insights generated in ML by employing permutation and leave one out feature importance methods, alongside ALE (Accumulated Local Effects) and SHAP (SHapley Additive exPlanations) explainers.

Caylum Collier, Krishnendu Guha

With the increasing integration of smart meters in electrical grids worldwide, detecting energy theft has become a critical and ongoing challenge. Artificial intelligence (AI)-based models have demonstrated strong performance in identifying fraudulent consumption patterns; however, previous works exploring the use of machine learning solutions for this problem demand high computational and energy costs, limiting their practicality -- particularly in low-theft scenarios where continuous inference can result in unnecessary energy usage. This paper proposes a lightweight detection unit, or watchdog mechanism, designed to act as a pre-filter that determines when to activate a long short-term memory (LSTM) model. This mechanism reduces the volume of input fed to the LSTM model, limiting it to instances that are more likely to involve energy theft thereby preserving detection accuracy while substantially reducing energy consumption associated with continuous model execution. The proposed system was evaluated through simulations across six scenarios with varying theft severity and number of active thieves. Results indicate a power consumption reduction exceeding 64\%, with minimal loss in detection accuracy and consistently high recall. These findings support the feasibility of a more energy-efficient and scalable approach to energy theft detection in smart grids. In contrast to prior work that increases model complexity to achieve marginal accuracy gains, this study emphasizes practical deployment considerations such as inference efficiency and system scalability. The results highlight the potential for deploying sustainable, AI-assisted monitoring systems within modern smart grid infrastructures.

Barra White, Krishnendu Guha

Explainable ML algorithms are designed to provide transparency and insight into their decision-making process. Explaining how ML models come to their prediction is critical in fields such as healthcare and finance, as it provides insight into how models can help detect bias in predictions and help comply with GDPR compliance in these fields. QML leverages quantum phenomena such as entanglement and superposition, offering the potential for computational speedup and greater insights compared to classical ML. However, QML models also inherit the black-box nature of their classical counterparts, requiring the development of explainability techniques to be applied to these QML models to help understand why and how a particular output was generated. This paper will explore the idea of creating a modular, explainable QML framework that splits QML algorithms into their core components, such as feature maps, variational circuits (ansatz), optimizers, kernels, and quantum-classical loops. Each component will be analyzed using explainability techniques, such as ALE and SHAP, which have been adapted to analyse the different components of these QML algorithms. By combining insights from these parts, the paper aims to infer explainability to the overall QML model.