Parameterized Two‐Qubit Gates for Enhanced Variational Quantum Eigensolver

Rasmussen, S. E.; Zinner, N. T.

doi:10.1002/andp.202200338

Cited by 4 publications

(5 citation statements)

References 58 publications

(80 reference statements)

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“…In QML models, the quantum part aims to provide trial states for the algorithm. The PQC, or ansatz circuit, generates these states according to a set of control parameters that are managed by the classical part of the algorithm [37].…”

Section: Related Workmentioning

confidence: 99%

“…Faced with the different ways of schematizing PQCs, there are algorithms that combine different types of gates, such as the Variational Quantum Eigensolver (VQE), that can combine the single-qubit and two-qubit, as well as the parameterized and non-parameterized gates [37], [40]. For example, Rasmussen and Zinner [37] used VQE with both single (rotation gates) and two-qubit (CNOT, CZ, and iSWAP) gates with angles to be parameterized during training. Meanwhile, Sim et al [41] performed several combinations with different rotation gates, in x, y, and z, and entanglement gates (CNOT and CZ), forming 19 different circuit types for testing.…”

Section: Related Workmentioning

confidence: 99%

“…Entanglement is another important quantum mechanics property that is leveraged in QC for constructing dependencies between qubits [27]. Among the entanglement gates, we can cite CNOT, CZ, and iSWAP as examples [37].…”

Section: B Controlled Gatesmentioning

confidence: 99%

“…If the controlled gate is 1, a Z gate is applied to the qubit on the target gate. A symmetric gate, the iSWAP switches twoqubit states and define the amplitudes of |𝟎𝟏⟩ and |𝟏𝟎⟩ by i [37], [51]. Equations ( 4)-( 6) present the matrices corresponding to these gates.…”

Section: B Controlled Gatesmentioning

confidence: 99%

“…In addition, we considered the circuit configuration used in the VQE quantum algorithm based on the study by Rasmussen and Zinner [37]. First, an Euler rotation is performed on each qubit, followed by a nearest-neighbor coupling using the given entangling gate.…”

Section: Figure 5 Pqc Defined By Y X and Z Rotation Gatesmentioning

confidence: 99%

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Prognostics and Health Management of Rotating Machinery via Quantum Machine Learning

et al. 2023

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Prognostics and Health Management (PHM) concerns predicting machines' behavior to support maintenance decisions through failure modes diagnosis and prognosis. Diagnosis is broadly applied in the context of rotating machines' state classification using several traditional Machine Learning (ML) and Deep Learning (DL) methods. Recently, Quantum Computing (QC), a new and expanding research field, has contributed to different purposes and contexts, such as optimization, artificial intelligence, simulation, cybersecurity, pharmaceutics, and the energy sector. Despite the current limitations in terms of hardware, QC has been studied as an alternative for improving models' speed and computational efficiency. Specifically, this paper proposes a Quantum Machine Learning (QML) approach to diagnose rolling bearings, which are essential components in rotating machinery, based on vibration signals. We apply hybrid models involving the encoding and construction of parameterized quantum circuits (PQC) connected to a classical neural network, the Multi-Layer Perceptron (MLP). We consider combinations of the Variational Quantum Eigensolver (VQE) framework with rotation gates and different entanglement (two-qubits) gates (CNOT, CZ and iSWAP). For each PQC configuration, we assess the impact of the number of layers (1, 5 and 10). We use two databases of different complexity levels not previously explored with QML, namely CWRU and JNU, with 10 and 12 failure modes, respectivel. For CWRU, all QML models presented higher accuracy than the classical MLP. For JNU, all QML models were superior to classical MLP as well. These results suggest that, despite the current limitations of quantum environments, QML models are promising tools to be further investigated in PHM.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: B Controlled Gatesmentioning

confidence: 99%

Section: B Controlled Gatesmentioning

confidence: 99%

Section: Figure 5 Pqc Defined By Y X and Z Rotation Gatesmentioning

confidence: 99%

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Prognostics and Health Management of Rotating Machinery via Quantum Machine Learning

et al. 2023

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Quantum Metrology Assisted by Machine Learning

Huang,

Zhuang,

Zhou

et al. 2024

Adv Quantum Tech

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Quantum metrology aims to measure physical quantities based on fundamental quantum principles, enhancing measurement precision through resources like quantum entanglement and quantum correlations. This field holds promise for advancing quantum‐enhanced sensors, including atomic clocks and magnetometers. However, practical constraints exist in the four fundamental steps of quantum metrology, including initialization, sensing, readout, and estimation. Valuable resources, such as coherence time, impose limitations on the performance of quantum sensors. Machine learning, enabling learning and prediction without explicit knowledge, provides a powerful tool in optimizing quantum metrology with limited resources. This article reviews the fundamental principles, potential applications, and recent advancements in quantum metrology assisted by machine learning.

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