Cloud-based accessing of Quantum-as-a-Service (QaaS) platforms such as IBM Quantum, IonQ Cloud, and Amazon Braket is becoming popular day by day. Hybrid quantum-classical algorithms (VQE, QAOA, QML) transfer data via a long layered pipeline of orchestration, compilation, and execution. Recent works have demonstrated various critical attacks at individual stages: Calibration tampering, SWAP attacks, QubitHammer, and so on. However, these attacks remain separated because of their own terminology, and existing STRIDE-based threat modeling in the context of quantum lacks a structured view towards the QaaS stack itself. We address this concern by decomposing the workflow into six-stage model with STRIDE threat modeling. Our matrix demonstrated attack vectors in quantum-specific, inherited classical, and plausible tiers for each of the stages. We further investigate the underexplored sections (repudiation and elevation-of-privilege) and distinguish three different cross-stage attack chains with higher impacts.
Badhon Rahman, Majid Haghparast, T. Mikkonen· 1 citation
Value-at-risk (VaR) is a broadly used measure for evaluating financial tail risk, but its estimation depends on assessing threshold exceeding probabilities over complex loss distributions. Probability estimation tasks using quantum algorithms works based on amplitude estimation that has a potential to offer quadratic speedup; however, their implementation on noisy intermediate-scale quantum devices is obstructed by the qubit and circuit depth requirements of arithmetic-heavy comparator oracles. This work proposes a qubit-efficient Boolean quantum oracle for threshold-based risk screening motivated by VaR analysis that has the ability to directly flag loss scenarios that violate a predefined threshold condition without utilizing high depth quantum adders or quantum subtractors. The proposed oracle employs a risk ancilla together with a reusable work qubit and utilizes a shallow circuit depth, making it a feasible candidate for near-term quantum hardware. In this work we analyze the resource scaling of the proposed oracle and benchmark it against arithmetic-based approaches under noisy simulations. This work further shows how the proposed oracle can be integrated within a hybrid quantum–classical variational optimization framework to restrain high-risk configurations.
Gayathri S S, K. R, Majid Haghparast· Physica Scripta· 0 citations
Resgru, a lightweight recurrent neural decoder that maps time-ordered detection events to a binary logical-class prediction for each shot, is proposed, a lightweight recurrent neural decoder that maps time-ordered detection events to a binary logical-class prediction for each shot.
Ashutosh Kumar, Majid Haghparast, Lauri Kettunen· Physica Scripta· 0 citations
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