Quantum error-correcting output codes

Windridge, David; Mengoni, Riccardo; Nagarajan, Rajagopal

doi:10.1142/s0219749918400038

Cited by 6 publications

(3 citation statements)

References 20 publications

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“…Very recently, [52] has proposed a distributed framework for ensemble learning on a variety of NISQ quantum devices, although it requires many NISQ devices to be actually implemented. A quantum ECOC multiclass ensemble approach was proposed in [53]. In [54], the authors investigated the performance enhancement of a majority-voting-based ensemble system in the quantum regime.…”

Section: Related Workmentioning

confidence: 99%

Resource Saving via Ensemble Techniques for Quantum Neural Networks

Incudini¹,

Grossi²,

Ceschini³

et al. 2023

Preprint

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Quantum neural networks hold significant promise for numerous applications, particularly as they can be executed on the current generation of quantum hardware. However, due to limited qubits or hardware noise, conducting large-scale experiments often requires significant resources. Moreover, the output of the model is susceptible to corruption by quantum hardware noise. To address this issue, we propose the use of ensemble techniques, which involve constructing a single machine learning model based on multiple instances of quantum neural networks. In particular, we implement bagging and AdaBoost techniques, with different data loading configurations, and evaluate their performance on both synthetic and real-world classification and regression tasks. To assess the potential performance improvement under different environments, we conduct experiments on both simulated, noiseless software and IBM superconducting-based QPUs, suggesting these techniques can mitigate the quantum hardware noise. Additionally, we quantify the amount of resources saved using these ensemble techniques. Our findings indicate that these methods enable the

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Section: Related Workmentioning

confidence: 99%

Resource Saving via Ensemble Techniques for Quantum Neural Networks

Incudini¹,

Grossi²,

Ceschini³

et al. 2023

Preprint

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“…Appropriate choice of error-correcting codes further enables incorrect individual classification decisions to be effectively corrected in the composite output. In [182], the authors propose an appropriate quantisation of the ECOC process, based on the quantum support vector machine. They show that, in addition to the usual benefits of quantising machine learning, this technique leads to an exponential reduction in the number of logic gates required for effective correction of classification error.…”

Section: Quantum Formal Verification and Quantum Machine Learningmentioning

confidence: 99%

Foundations of Reversible Computation

Aman

Ciobanu

Glück

et al. 2020

Lecture Notes in Computer Science

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Reversible computation allows computation to proceed not only in the standard, forward direction, but also backward, recovering past states. While reversible computation has attracted interest for its multiple applications, covering areas as different as low-power computing, simulation, robotics and debugging, such applications need to be supported by a clear understanding of the foundations of reversible computation. We report below on many threads of research in the area of foundations of reversible computing, giving particular emphasis to the results obtained in the framework of the European COST Action IC1405, entitled "Reversible Computation-Extending Horizons of Computing", which took place in the years 2015-2019.

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“…An experimental implementation of the QSVM have been shown in Li et al (2015) and Patrick et al (2018). Also, in Windridge et al (2018), the authors propose a quantized version of Error Correction Output Codes (ECOC) which extends the QSVM algorithm to the multi-class case and enables it to perform an error correction on the label allocation.…”

Section: Quantum Svmmentioning

confidence: 99%