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261 results on '"Paolo Rech"'

1. Evaluating the risk of data loss due to particle radiation damage in a DNA data storage system

Author

Christopher N. Takahashi, David P. Ward, Carlo Cazzaniga, Christopher Frost, Paolo Rech, Kumkum Ganguly, Sean Blanchard, Steve Wender, Bichlien H. Nguyen, and Jake A. Smith

Subjects
Science
Abstract

Abstract DNA data storage is a potential alternative to magnetic tape for archival storage purposes, promising substantial gains in information density. Critical to the success of DNA as a storage media is an understanding of the role of environmental factors on the longevity of the stored information. In this paper, we evaluate the effect of exposure to ionizing particle radiation, a cause of data loss in traditional magnetic media, on the longevity of data in DNA data storage pools. We develop a mass action kinetics model to estimate the rate of damage accumulation in DNA strands due to neutron interactions with both nucleotides and residual water molecules, then utilize the model to evaluate the effect several design parameters of a typical DNA data storage scheme have on expected data longevity. Finally, we experimentally validate our model by exposing dried DNA samples to different levels of neutron irradiation and analyzing the resulting error profile. Our results show that particle radiation is not a significant contributor to data loss in DNA data storage pools under typical storage conditions.

Published
2024
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2. Understanding Logical-Shift Error Propagation in Quanvolutional Neural Networks

Author

Marzio Vallero, Emanuele Dri, Edoardo Giusto, Bartolomeo Montrucchio, and Paolo Rech

Subjects
Fault injection, fault tolerance, quantum computing (QC), quantum machine learning (QML), reliability evaluation, Atomic physics. Constitution and properties of matter, QC170-197, Materials of engineering and construction. Mechanics of materials, TA401-492
Abstract

Quanvolutional neural networks (QNNs) have been successful in image classification, exploiting inherent quantum capabilities to improve performance of 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 article, 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 logical-shift 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.

Published
2024
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5. Reliability and Security of AI Hardware.

Author

Dennis Gnad, Martin Gotthard, Jonas Krautter, Angeliki Kritikakou, Vincent Meyers, Paolo Rech, Josie E. Rodriguez Condia, Annachiara Ruospo, Ernesto Sánchez 0001, Fernando Fernandes dos Santos, Olivier Sentieys, Mehdi B. Tahoori, Russell Tessier, and Marcello Traiola

Published
2024
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