Understanding the Impact of Cutting in Quantum Circuits Reliability to Transient Faults

Casciola, Nadir; Giusto, Edoardo; Dri, Emanuele; Oliveira, Daniel; Rech, Paolo; Montrucchio, Bartolomeo

doi:10.1109/iolts56730.2022.9897308

“…Hardware/software co-design has been demonstrated to be critical for quantum computers [26], [56]- [61]. Our analysis adds the logical-shift fault issue to the reliability assessment of these devices and architectures.…”

Section: Discussion and Projectionsmentioning

confidence: 99%

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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“…Circuit cutting can help with gate errors and short decoherence times since smaller circuits are less susceptible to these noise sources; in contrast, readout errors can be detrimental to the circuit cutting procedure since it requires additional measurements [32]. In addition, it was investigated how errors occurring in different subcircuits affect the final recombined result, and thereby, different error propagation characteristics and sensitivities were found for different subcircuits [33].…”

Section: Related Workmentioning

confidence: 99%

Investigating the effect of circuit cutting in QAOA for the MaxCut problem on NISQ devices

Bechtold¹,

Barzen²,

Leymann³

et al. 2023

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Noisy Intermediate-Scale Quantum (NISQ) devices are restricted by their limited number of qubits and their short decoherence times. An approach addressing these problems is quantum circuit cutting. It decomposes the execution of a large quantum circuit into the execution of multiple smaller quantum circuits with additional classical postprocessing. Since these smaller quantum circuits require fewer qubits and gates, they are more suitable for NISQ devices. To investigate the effect of quantum circuit cutting in a quantum algorithm targeting NISQ devices, we design two experiments using the Quantum Approximate Optimization Algorithm (QAOA) for the Maximum Cut (MaxCut) problem and conduct them on state-of-the-art superconducting devices. Our first experiment studies the influence of circuit cutting on the objective function of QAOA, and the second evaluates the quality of results obtained by the whole algorithm with circuit cutting. The results show that circuit cutting can reduce the effects of noise in QAOA, and therefore, the algorithm yields better solutions on NISQ devices.

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“…Circuit cutting can help with gate errors and short decoherence times since smaller circuits are less susceptible to these noise sources; in contrast, readout errors can be detrimental to the circuit cutting procedure since it requires additional measurements [34]. In addition, it was investigated how errors occurring in different subcircuits affect the final recombined result, and thereby, different error propagation characteristics and sensitivities were found for different subcircuits [35].…”

Section: Related Workmentioning

confidence: 99%

Investigating the effect of circuit cutting in QAOA for the MaxCut problem on NISQ devices

Bechtold,

Barzen,

Leymann

et al. 2023

Quantum Sci. Technol.

12

0

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Noisy Intermediate-Scale Quantum (NISQ) devices are restricted by their limited number of qubits and their short decoherence times. An approach addressing these problems is quantum circuit cutting. It decomposes the execution of a large quantum circuit into the execution of multiple smaller quantum circuits with additional classical postprocessing. Since these smaller quantum circuits require fewer qubits and gates, they are more suitable for NISQ devices. To investigate the effect of quantum circuit cutting in a quantum algorithm targeting NISQ devices, we design two experiments using the Quantum Approximate Optimization Algorithm (QAOA) for the Maximum Cut (MaxCut) problem and conduct them on state-of-the-art superconducting devices. Our first experiment studies the influence of circuit cutting on the objective function of QAOA, and the second evaluates the quality of results obtained by the whole algorithm with circuit cutting. The results show that circuit cutting can reduce the effects of noise in QAOA, and therefore, the algorithm yields better solutions on NISQ devices.

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Understanding the Impact of Cutting in Quantum Circuits Reliability to Transient Faults

Cited by 4 publications

References 24 publications

Understanding Logical-Shift Error Propagation in Quanvolutional Neural Networks

Understanding Logical-Shift Error Propagation in Quanvolutional Neural Networks

Investigating the effect of circuit cutting in QAOA for the MaxCut problem on NISQ devices

Investigating the effect of circuit cutting in QAOA for the MaxCut problem on NISQ devices

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