QuFI: a Quantum Fault Injector to Measure the Reliability of Qubits and Quantum Circuits

Oliveira, Daniel; Giusto, Edoardo; Dri, Emanuele; Casciola, Nadir; Baheri, Betis; Guan, Qiang; Montrucchio, Bartolomeo; Rech, Paolo

doi:10.1109/dsn53405.2022.00025

Cited by 9 publications

(7 citation statements)

References 34 publications

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“…The sources of noise can vary, from environmental factors such as temperature and electromagnetic radiation to decoherence and imperfections in the hardware itself. As a result, researchers have been actively investigating ways to characterize and mitigate the impact of noise on qubits [ 29 , 30 , 31 , 32 ].…”

Section: Discussionmentioning

confidence: 99%

A More General Quantum Credit Risk Analysis Framework

Dri

Aita

Giusto

et al. 2023

Entropy

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Credit risk analysis (CRA) quantum algorithms aim at providing a quadratic speedup over classical analogous methods. Despite this, experts in the business domain have identified significant limitations in the existing approaches. Thus, we proposed a new variant of the CRA quantum algorithm to address these limitations. In particular, we improved the risk model for each asset in a portfolio by enabling it to consider multiple systemic risk factors, resulting in a more realistic and complex model for each asset’s default probability. Additionally, we increased the flexibility of the loss-given-default input by removing the constraint of using only integer values, enabling the use of real data from the financial sector to establish fair benchmarking protocols. Furthermore, all proposed enhancements were tested both through classical simulation of quantum hardware and, for this new version of our work, also using QPUs from IBM Quantum Experience in order to provide a baseline for future research. Our proposed variant of the CRA quantum algorithm addresses the significant limitations of the current approach and highlights an increased cost in terms of circuit depth and width. In addition, it provides a path to a substantially more realistic software solution. Indeed, as quantum technology progresses, the proposed improvements will enable meaningful scales and useful results for the financial sector.

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Section: Discussionmentioning

confidence: 99%

A More General Quantum Credit Risk Analysis Framework

Dri

Aita

Giusto

et al. 2023

Entropy

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“…To inject logical-shift errors into the quantum convolution circuit during the QNN inference (we do not inject during training), we apply a tuned stimulus to modify the qubit state. To model the injected fault that, as discussed in Section II-B, can have parametrized rotations of different magnitudes, we use the QuFI fault injector, which inserts an extra U3 gate to model the fault [16]. The U3 gate can modify the ϕ and/or θ angles used to define the qubit's actual state (refer to Fig.…”

Section: B Logical-shift Injection and Simulationmentioning

confidence: 99%

“…Ideally, the correct state will have the highest probability so it can be selected as the output. We use the Quantum Vulnerability Factor (QVF) [50] metric to measure the impact of a transient fault in the output probability distribution. The QVF, corresponding to the Architecture Vulnerability Factor (AVF) [51] and the Program Vulnerability Factor (PVF) [52] in traditional computing systems, ranges from [0, 1], and indicates the probability of a fault to propagate affecting the output.…”

Section: Fault Effect Evaluationmentioning

confidence: 99%

“…As a consequence, retention and relaxation errors shorten significantly the computationally useful lifetime of a qubit, inducing a logical-shift error in the qubit state. On top of this, it has been proven, through both experiments and simulations, that quantum devices are extremely sensitive to natural ionizing radiation [8]- [16]. Rather than flipping a bit, as would happen in CMOS technology [17], intrinsic noise and external radiation both trigger the qubit(s), modifying the resulting qubit logic state.…”

Section: Introductionmentioning

confidence: 99%

“…By promptly addressing the issue imposed by both intrinsic and extrinsic radiationinduced faults, we can immediately start to develop new reliability solutions, rather than patching it up only after its impact will become evident in the field. In this paper we inject more than 13 billion logical-shift faults in the quantum layer, adapting to QNNs an open-source fault-injector for quantum circuits (QuFI) [16]. We aim at filling the gap in the reliability evaluation of QNNs by investigating and understanding how faults in the quantum layer propagate in the network during inference and why they cause misclassifications.…”

Section: Introductionmentioning

confidence: 99%

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Understanding Logical-Shift Error Propagation in Quanvolutional Neural Networks

Vallero,

Dri,

Giusto

et al. 2024

IEEE Trans. Quantum Eng.

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Quanvolutional Neural Networks (QNNs) have been successful in image classification, exploiting inherent quantum capabilities to improve performance of the traditional convolution. Unfortunately, the qubit's reliability can be a significant issue for QNNs inference, since its logical state can be altered by both intrinsic noise and by the interaction with natural radiation. In this paper we aim at investigating the propagation of logical-shift errors (i.e. the unexpected modification of the qubit state) in QNNs. We propose a bottom-up evaluation reporting data from 13, 322, 547, 200 logical-shift injections. We characterize the error propagation in the quantum circuit implementing a single convolution and then in various designs of the same QNN, varying the dataset and the network depth. We track the logicalshift error propagation through the qubits, channels, and subgrids identifying the faults that are more likely to cause misclassifications. We found that up to 10% of the injections in the quanvolutional layer cause misclassification and even logical-shifts of small magnitude can be sufficient to disturb the network functionality. Our detailed analysis shows that corruptions in the qubits' state that alter their probability amplitude are more critical than the ones altering their phase, that some object classes are more likely than others to be corrupted, that the criticality of subgrids depends on the dataset, and that the control qubits, once corrupted, are more likely to modify the QNN output than the target qubits.

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An overview of quantum software engineering in Latin America

Aparicio-Morales,

Moguel,

Bibbo

et al. 2024

Quantum Inf Process

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Quantum computing represents a revolutionary computational paradigm with the potential to address challenges beyond classical computers’ capabilities. The development of robust quantum software is indispensable to unlock the full potential of quantum computing. Like classical software, quantum software is expected to be complex and extensive, needing the establishment of a specialized field known as Quantum Software Engineering. Recognizing the regional focus on Latin America within this special issue, we have boarded on an in-depth inquiry encompassing a systematic mapping study of existing literature and a comprehensive survey of experts in the field. This rigorous research effort aims to illuminate the current landscape of Quantum Software Engineering initiatives undertaken by universities, research institutes, and companies across Latin America. This exhaustive study aims to provide information on the progress, challenges, and opportunities in Quantum Software Engineering in the Latin American context. By promoting a more in-depth understanding of cutting-edge developments in this burgeoning field, our research aims to serve as a potential stimulus to initiate pioneering initiatives and encourage collaborative efforts among Latin American researchers.

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QuFI: a Quantum Fault Injector to Measure the Reliability of Qubits and Quantum Circuits

Cited by 9 publications

References 34 publications

A More General Quantum Credit Risk Analysis Framework

A More General Quantum Credit Risk Analysis Framework

Understanding Logical-Shift Error Propagation in Quanvolutional Neural Networks

An overview of quantum software engineering in Latin America

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