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Powerful hardware services and software libraries are vital tools for quickly and affordably designing, testing, and executing quantum algorithms.
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Demonstration of quantum advantage in machine learning
Diego Ristè, Marcus P. da Silva, Colm A. Ryan, Andrew W. Cross, Antonio D. Córcoles, John A. Smolin, Jay M. Gambetta, Jerry M. Chow, and Blake R. Johnson · 2017
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Maria Schuld and Francesco Petruccione · 2018
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Scaling for edge inference of deep neural networks
Xiaowei Xu, Yukun Ding, Sharon Xiaobo Hu, Michael Niemier, Jason Cong, Yu Hu, and Yiyu Shi · 2018
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Barren plateaus in quantum neural network training landscapes
Jarrod R. McClean, Sergio Boixo, Vadim N. Smelyanskiy, Ryan Babbush, and Hartmut Neven · 2018
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Parameterized quantum circuits as machine learning models
Marcello Benedetti, Erika Lloyd, Stefan Sack, and Mattia Fiorentini · 2019
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Evaluating analytic gradients on quantum hardware
Maria Schuld, Ville Bergholm, Christian Gogolin, Josh Izaac, and Nathan Killoran · 2019
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Qiskit: An open-source framework for quantum computing
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Benchmarking quantum computers and the impact of quantum noise, 2019
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An initialization strategy for addressing barren plateaus in parametrized quantum circuits
Edward Grant, Leonard Wossnig, Mateusz Ostaszewski, and Marcello Benedetti · 2019
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Validating quantum computers using randomized model circuits
Andrew W Cross, Lev S Bishop, Sarah Sheldon, Paul D Nation, and Jay M Gambetta · 2019
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Quantum machine learning and quantum biomimetics: A perspective
Lucas Lamata · 2020
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Classical versus quantum models in machine learning: Insights from a finance application
Javier Alcazar, Vicente Leyton-Ortega, and Alejandro Perdomo-Ortiz · 2020
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Measuring the Algorithmic Efficiency of Neural Networks
Danny Hernandez and Tom B. Brown · 2020
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Large gradients via correlation in random parameterized quantum circuits
Tyler Volkoff and Patrick J. Coles · 2021
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Scalable benchmarks for gate-based quantum computers
Arjan Cornelissen, Johannes Bausch, and András Gilyén · 2021
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Hyperparameter optimization of hybrid quantum neural networks for car classification
Asel Sagingalieva, Andrii Kurkin, Artem Melnikov, Daniil Kuhmistrov, et al · 2022
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Generation of high-resolution handwritten digits with an ion-trap quantum computer
Manuel S Rudolph, Ntwali Bashige Toussaint, Amara Katabarwa, Sonika Johri, Borja Peropadre, and Alejandro Perdomo-Ortiz · 2022
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Practical application-specific advantage through hybrid quantum computing
Michael Perelshtein, Asel Sagingalieva, Karan Pinto, Vishal Shete, et al · 2022
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Bob Coecke, Giovanni de Felice, Konstantinos Meichanetzidis, and Alexis Toumi · 2020
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Grammar-Aware Question-Answering on Quantum Computers
Konstantinos Meichanetzidis, Alexis Toumi, Giovanni de Felice, and Bob Coecke · 2020
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An application benchmark for fermionic quantum simulations, 2020
Pierre-Luc Dallaire-Demers, Michal Stechly, Jerome F. Gonthier, Ntwali Toussaint Bashige, et al · 2020
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Amazon braket
Amazon Web Services · 2020
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Efficient calculation of gradients in classical simulations of variational quantum algorithms
Tyson Jones and Julien Gacon · 2020
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Demonstrating NISQ era challenges in algorithm design on IBM’s 20 qubit quantum computer
Daniel Koch, Brett Martin, Saahil Patel, Laura Wessing, and Paul M Alsing · 2020
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Scaling IonQ’s Quantum Computers: The Roadmap
Peter Chapman · 2020
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Generalization in quantum machine learning from few training data
Matthias C. Caro, Hsin-Yuan Huang, M. Cerezo, Kunal Sharma, Andrew Sornborger, Lukasz Cincio, and Patrick J. Coles · 2022
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Quantum Machine Learning in Finance: Time Series Forecasting
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Quantum Learning Machine
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QASMBench benchmark suite
Pacific Northwest National Laboratory · 2022
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https://github.com/myQLM/qscore, 06 2022
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PennyLane: Automatic differentiation of hybrid quantum-classical computations
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An exponentially-growing family of universal quantum circuits
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The Race to Quantum Advantage Depends on Benchmarking
Matt Langione, JF Bobier, L Krayer, H Park, and A Kumar · 2022
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On the Emerging Potential of Quantum Annealing Hardware for Combinatorial Optimization
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Quantum algorithms applied to satellite mission planning for Earth observation
Serge Rainjonneau, Igor Tokarev, Sergei Iudin, Saaketh Rayaprolu, Karan Pinto, Daria Lemtiuzhnikova, Miras Koblan, Egor Barashov, Mohammad Kordzanganeh, Markus Pflitsch, et al · 2023
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Parallel hybrid networks: an interplay between quantum and classical neural networks
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Benchmarking simulated and physical quantum processing units using quantum and hybrid algorithms
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