Entanglement scaling in quantum advantage benchmarks

Biamonte, Jacob; Morales, Mauro E. S.; Koh, Dax Enshan

doi:10.1103/physreva.101.012349

Cited by 4 publications

(3 citation statements)

References 17 publications

(31 reference statements)

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“…The following section discusses the various methods and algorithms utilized in the prediction of heart-related irregularities [19] and the earlier techniques of assessing the heart disease diagnosis to assist in lowering the risk of progression. The suggested method [20] has constructed a hybrid technique based on DL for finding out whether a person has been affected by cardiac disease using CNN with Bi-LSTM.…”

Section: Literature Reviewmentioning

confidence: 99%

Heart Failure Detection Using Instance Quantum Circuit Approach and Traditional Predictive Analysis

et al. 2023

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The earlier prediction of heart diseases and appropriate treatment are important for preventing cardiac failure complications and reducing the mortality rate. The traditional prediction and classification approaches have resulted in a minimum rate of prediction accuracy and hence to overcome the pitfalls in existing systems, the present research is aimed to perform the prediction of heart diseases with quantum learning. When quantum learning is employed in ML (Machine Learning) and DL (Deep Learning) algorithms, complex data can be performed efficiently with less time and a higher accuracy rate. Moreover, the proposed ML and DL algorithms possess the ability to adapt to predictions with alterations in the dataset integrated with quantum computing that provides robustness in the earlier detection of chronic diseases. The Cleveland heart disease dataset is being pre-processed for the checking of missing values to avoid incorrect predictions and also for improvising the rate of accuracy. Further, SVM (Support Vector Machine), DT (Decision Tree) and RF (Random Forest) are used to perform classification. Finally, disease prediction is performed with the proposed instance-based quantum ML and DL method in which the number of qubits is computed with respect to features and optimized with instance-based learning. Additionally, a comparative assessment is provided for quantifying the differences between the standard classification algorithms with quantum-based learning in order to determine the significance of quantum-based detection in heart failure. From the results, the accuracy of the proposed system using instance-based quantum DL and instance-based quantum ML is found to be 98% and 83.6% respectively.

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Section: Literature Reviewmentioning

confidence: 99%

Heart Failure Detection Using Instance Quantum Circuit Approach and Traditional Predictive Analysis

et al. 2023

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“…In other words, the bond dimension (the dimension of the wires) in the tensor network acts to bound the maximal entanglement. Merging quantum computation with ideas from tensor networks provides new tools to quantify the entanglement that a given quantum circuit can generate [15,16].…”

Section: Introductionmentioning

confidence: 99%

Quantum Machine Learning Tensor Network States

2021

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Tensor network algorithms seek to minimize correlations to compress the classical data representing quantum states. Tensor network algorithms and similar tools—called tensor network methods—form the backbone of modern numerical methods used to simulate many-body physics and have a further range of applications in machine learning. Finding and contracting tensor network states is a computational task, which may be accelerated by quantum computing. We present a quantum algorithm that returns a classical description of a rank-r tensor network state satisfying an area law and approximating an eigenvector given black-box access to a unitary matrix. Our work creates a bridge between several contemporary approaches, including tensor networks, the variational quantum eigensolver (VQE), quantum approximate optimization algorithm (QAOA), and quantum computation.

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“…During the development of quantum communication and quantum computation, quantum entanglement plays an increasingly important role in quantum information processing. Correspondingly, the generation of entanglement states has also been widely investigated in literature both theoretically [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16] and experimentally. [17][18][19] For multi-particle entanglement, there are two major types of entangled states, the W states and the Greenberger-Horne-Zeilinger (GHZ) states.…”

Section: Introductionmentioning

confidence: 99%

Fast generation of W state via superadiabatic-based shortcut in circuit quantum electrodynamics*

Wang¹,

Zhang²,

Xu³

et al. 2021

Chinese Phys. B

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We propose a scheme to fast prepare the three-qubit W state via superadiabatic-based shortcuts in a circuit quantum electrodynamics (circuit QED) system. We derive the effective Hamiltonian to suppress the unwanted transitions between different eigenstates by counterdiabatic driving, and obtain the W state with high-fidelity based on the superadiabatic passage. The numerical simulation results demonstrate that the proposed scheme can accelerate the evolution, and is more efficient than that with the adiabatic passage. In addition, the proposed scheme is robust to the decoherence caused by the resonator decay and qubit relaxation, and does not need additional parameters, which could be feasible in experiment.

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Entanglement scaling in quantum advantage benchmarks

Cited by 4 publications

References 17 publications

Heart Failure Detection Using Instance Quantum Circuit Approach and Traditional Predictive Analysis

Heart Failure Detection Using Instance Quantum Circuit Approach and Traditional Predictive Analysis

Quantum Machine Learning Tensor Network States

Fast generation of W state via superadiabatic-based shortcut in circuit quantum electrodynamics*

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