A thermal quantum classifier

Korkmaz, Ufuk; Türkpençe, Deniz; Akıncı, Tahir Çetin; Akinci, Cetin; Şeker, Serhat

doi:10.26421/qic20.11-12-4

Cited by 2 publications

(2 citation statements)

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“…The physics we use in this model is based on the complete positivity, additivity [19] and divisibility [20,21] of quantum dynamical maps. As has been done in previous studies [7], [22], superconducting circuits can be shown as a physical model to apply the theoretical model with possible defects. We leave the activation and training tasks out of the scope of this study for our proposed classifier.…”

Section: Introductionmentioning

confidence: 97%

Quantum Reservoir Parameter Estimation via Fisher Information

Korkmaz¹,

TÜRKPENÇE²

2022

Sakarya University Journal of Science

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In this study, we show that as a result of weak interaction of different information environments structured with a single probe qubit, these environments can perform binary classification of the information they contain. In this way, we refer to these environments as quantum information baths because they consist of sequences of identical qubits in certain pure quantum states. A micro-maser like master equation has been developed to clearly describe the system dynamics analytically and the quantum states of different information reservoirs. The model can also be treated as a quantum neuron, due to the single-qubit probe that makes a binary decision depending on the reservoir parameters in its steady state. The numerical results of the repeated interaction process based on the divisibility and additivity of the quantum dynamic maps are compared with the analytical results. Besides being a single quantum classifier, the model we present can also serve as a basic unit of a quantum neural network within the framework of the dissipative model of quantum computing.

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Section: Introductionmentioning

confidence: 97%

Quantum Reservoir Parameter Estimation via Fisher Information

Korkmaz¹,

TÜRKPENÇE²

2022

Sakarya University Journal of Science

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“…One example is Korkmaz et al, who propose a scheme where the system is coupled to thermal reservoirs. The input data is encoded in these reservoirs and it is possible to show that this system works as a natural classifier that resembles the perceptron from Rosenblatt [12,13]. As done by Korkmaz et al, investigating options of open quantum algorithms for machine learning is one alternative to work around the limitations of the difference between the expected behavior versus the actual behavior of quantum computing.…”

Section: Introductionmentioning

confidence: 99%

Quantum classifier based on open quantum systems with amplitude information loading

Brito,

Neto,

Bernardes

2024

Preprint

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Although the studies on quantum algorithms have been progressing, it is still necessary to broaden the investigation of open quantum systems. In this study, we present the use of an open quantum system to implement a quantum classifier algorithm. Zhang et al. propose a one QuBit system interacting with the environment through a unitary operator that comes from the Hamiltonian. In our proposal, the input data is loaded into the amplitude of the environment instead of being in the unitary operator. This change positively impacts the performance of different databases tested and causes a difference in the system entanglement behavior. For evaluation, both the Zhang et. al model and the proposed model were tested in four real-world datasets and seven other toy problems. The results are evaluated according to accuracy and F1-Score. A deeper analysis of the Iris dataset is also done, checking the creation of entanglement and an extensive random search for better parameters on the proposed model. The results show that for most real-world datasets configurations, the proposed model, although having a simpler decision area, performed better than the one inspired by the Zhang et. al model, and that there is no pattern for the system entanglement in the Iris Dataset.

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A thermal quantum classifier

Cited by 2 publications

References 0 publications

Quantum Reservoir Parameter Estimation via Fisher Information

Quantum Reservoir Parameter Estimation via Fisher Information

Quantum classifier based on open quantum systems with amplitude information loading

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