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Parameterized quantum circuits as machine learning models

Quantum science and technology, 2019-11, Vol.4 (4), p.43001

2019

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An initialization strategy for addressing barren plateaus in parametrized quantum circuits

Quantum (Vienna, Austria), 2019-12, Vol.3, p.214, Article 214

2019

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A generative modeling approach for benchmarking and training shallow quantum circuits

npj quantum information, 2019-05, Vol.5 (1), Article 45

2019

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Adversarial quantum circuit learning for pure state approximation

New journal of physics, 2019-04, Vol.21 (4), p.43023

2019

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Structure optimization for parameterized quantum circuits

Quantum (Vienna, Austria), 2021-01, Vol.5, p.391, Article 391

2021

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Hardware-efficient variational quantum algorithms for time evolution

Physical review research, 2021-07, Vol.3 (3), Article 033083

2021

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Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Models

Physical review. X, 2017-11, Vol.7 (4), p.041052, Article 041052

2017

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Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers

Quantum science and technology, 2018-06, Vol.3 (3), p.30502

2018

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Filtering variational quantum algorithms for combinatorial optimization

Quantum science and technology, 2022-01, Vol.7 (1), p.15021

2022

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On the sample complexity of quantum Boltzmann machine learning

Communications physics, 2024-08, Vol.7 (1), p.274-9

2024

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F-Divergences and Cost Function Locality in Generative Modelling with Quantum Circuits

Entropy (Basel, Switzerland), 2021-09, Vol.23 (10), p.1281

2021

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Predicting Gibbs-State Expectation Values with Pure Thermal Shadows

PRX quantum, 2023-01, Vol.4 (1), p.010305, Article 010305

2023

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Bayesian learning of parameterised quantum circuits

Machine learning: science and technology, 2023-06, Vol.4 (2), p.25007

2023

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Training quantum Boltzmann machines with the β-variational quantum eigensolver

Machine learning: science and technology, 2024-06, Vol.5 (2), p.25017

2024

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Realization of quantum signal processing on a noisy quantum computer

npj quantum information, 2023-09, Vol.9 (1), p.93-12, Article 93

2023

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Protecting expressive circuits with a quantum error detection code

Nature physics, 2024-02, Vol.20 (2), p.219-224

2024

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Hierarchical quantum classifiers

npj quantum information, 2018-12, Vol.4 (1), p.1-8, Article 65

2018

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Estimation of effective temperatures in quantum annealers for sampling applications: A case study with possible applications in deep learning

Physical review. A, 2016-08, Vol.94 (2), Article 022308

2016

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Variational Inference with a Quantum Computer

Physical review applied, 2021-10, Vol.16 (4), Article 044057

2021

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Quantum-assisted Helmholtz machines: A quantum-classical deep learning framework for industrial datasets in near-term devices

Quantum science and technology, 2018-07, Vol.3 (3), p.34007

2018

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Quantum-Classical Generative Models for Machine Learning

Benedetti, Marcello

2019

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Erratum: Parameterized quantum circuits as machine learning models (2019 Quant. Sci. Tech. 4 043001)

Quantum science and technology, 2019-12, Vol.5 (1), p.19601

2019

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Tracing the Paths between Concepts in Large Bio-Medical Corpora

2015 20th International Conference on Control Systems and Computer Science, 2015, p.357-364

2015

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Explorative study on waste heat production intensity and recovery practices in the textile sector: First steps towards the creation of a decision support tool based on real data

Journal of cleaner production, 2022-07, Vol.359, p.131928, Article 131928

2022

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Accuracy of the Edinburgh Postnatal Depression Scale (EPDS) for screening to detect major depression among pregnant and postpartum women: systematic review and meta-analysis of individual participant data

BMJ (Online), 2020-11, Vol.371, p.m4022

2020

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Parameterized quantum circuits as machine learning models

An initialization strategy for addressing barren plateaus in parametrized quantum circuits

A generative modeling approach for benchmarking and training shallow quantum circuits

Adversarial quantum circuit learning for pure state approximation

Structure optimization for parameterized quantum circuits

Hardware-efficient variational quantum algorithms for time evolution

Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Models

Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers

Filtering variational quantum algorithms for combinatorial optimization

On the sample complexity of quantum Boltzmann machine learning

F-Divergences and Cost Function Locality in Generative Modelling with Quantum Circuits

Predicting Gibbs-State Expectation Values with Pure Thermal Shadows

Bayesian learning of parameterised quantum circuits

Training quantum Boltzmann machines with the β-variational quantum eigensolver

Realization of quantum signal processing on a noisy quantum computer

Protecting expressive circuits with a quantum error detection code

Hierarchical quantum classifiers

Estimation of effective temperatures in quantum annealers for sampling applications: A case study with possible applications in deep learning

Variational Inference with a Quantum Computer

Quantum-assisted Helmholtz machines: A quantum-classical deep learning framework for industrial datasets in near-term devices

Quantum-Classical Generative Models for Machine Learning

Erratum: Parameterized quantum circuits as machine learning models (2019 Quant. Sci. Tech. 4 043001)

Tracing the Paths between Concepts in Large Bio-Medical Corpora

Explorative study on waste heat production intensity and recovery practices in the textile sector: First steps towards the creation of a decision support tool based on real data

Accuracy of the Edinburgh Postnatal Depression Scale (EPDS) for screening to detect major depression among pregnant and postpartum women: systematic review and meta-analysis of individual participant data

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