Fetching the paper…
Reading the bibliography…
Benchmarking of quantum machine learning (QML) algorithms is challenging due to the complexity and variability of QML systems, e.g., regarding model ansatzes, data sets, training techniques, and hyper-parameters selection.
“Improved Precision and Recall Metric for Assessing Generative Models”, 2019
Tuomas Kynkäänniemi et al · 1904
Earlier work this paper cites.
“MLPerf Inference Benchmark”, 2020
Vijay Reddi et al · 1911
Earlier work this paper cites.
“The Probability Integral Transform and Related Results”
John. Angus · 1994
Earlier work this paper cites.
“Machine Learning”
Tom. Mitchell · 1997
Earlier work this paper cites.
“Scaling Laws for Neural Language Models”
Jared Kaplan et al · 2001
Earlier work this paper cites.
“Pattern Recognition and Machine Learning (Information Science and Statistics)”
Christopher. Bishop · 2007
Earlier work this paper cites.
“ImageNet Large Scale Visual Recognition Challenge”, 2015
Olga Russakovsky et al · 2015
Earlier work this paper cites.
“Deep Learning”
Ian Goodfellow, Yoshua Bengio and Aaron Courville · 2016
Earlier work this paper cites.
“CuPy: A NumPy-Compatible Library for NVIDIA GPU Calculations”
Ryosuke Okuta et al · 2017
Earlier work this paper cites.
“Information Perspective to Probabilistic Modeling: Boltzmann Machines versus Born Machines”
Song Cheng, Jing Chen and Lei Wang · 2018
Earlier work this paper cites.
“Quantum generative adversarial networks”
Pierre-Luc Dallaire-Demers and Nathan Killoran · 2018
Earlier work this paper cites.
“Assessing Generative Models via Precision and Recall”, 2018
Mehdi.. Sajjadi et al · 2018
Earlier work this paper cites.
“Pros and cons of GAN evaluation measures”
Ali Borji · 2018
Earlier work this paper cites.
“Quantum risk analysis”
Stefan Woerner and Daniel. Egger · 2019
Earlier work this paper cites.
“A generative modeling approach for benchmarking and training shallow quantum circuits”
Marcello Benedetti et al · 2019
Earlier work this paper cites.
“Validating quantum computers using randomized model circuits”
Andrew. Cross et al · 2019
Earlier work this paper cites.
“Fidelity benchmarks for two-qubit gates in silicon”
W. Huang et al · 2019
Earlier work this paper cites.
“Generative model benchmarks for superconducting qubits”
Kathleen. Hamilton, Eugene. Dumitrescu and Raphael. Pooser · 2019
Earlier work this paper cites.
“GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding”, 2019
Alex Wang et al · 2019
Earlier work this paper cites.
“MLCommons”, https://github.com/mlcommons , 2019
2019
Cited alongside, same era.
“Industry quantum computing applications”
Andreas Bayerstadler et al · 2021
Cited alongside, same era.
“Machine Learning with Quantum Computers”, Quantum Science and Technology
Maria Schuld and Francesco Petruccione · 2021
Cited alongside, same era.
Andrew Wack et al · 2021
Cited alongside, same era.
“Generative machine learning with tensor networks: Benchmarks on near-term quantum computers”
Michael. Wall, Matthew. Abernathy and Gregory Quiroz · 2021
Cited alongside, same era.
“Do Quantum Circuit Born Machines Generalize?”, 2022
Kaitlin Gili et al · 2022
Later among the works it cites.
“Yahoo Finance API” https://finance.yahoo.com/ , 2022
2022
Later among the works it cites.
“Generative quantum learning of joint probability distribution functions”
Elton Zhu et al · 2022
Later among the works it cites.
“PennyLane: Automatic differentiation of hybrid quantum-classical computations”, 2022
Ville Bergholm et al · 2022
Later among the works it cites.
“Cirq” See full list of authors on Github: https://github .com/quantumlib/Cirq/graphs/contributors
Cirq Developers · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
“Introducing Amazon Braket Hybrid Jobs – Set Up, Monitor, and Efficiently Run Hybrid Quantum-Classical Workloads”, https://aws.amazon.com/blogs/aws/introducing-amazon-braket-hybrid-jobs-set-up-monitor-and-efficiently-run-hybrid-quantum-classical-workloads/ , 2021
Danilo Poccia · 2021
Cited alongside, same era.
“Pros and Cons of GAN Evaluation Measures: New Developments”, 2021
Ali Borji · 2021
Cited alongside, same era.
“GEO: Enhancing Combinatorial Optimization with Classical and Quantum Generative Models”
Javier Alcazar et al · 2021
Cited alongside, same era.
“Enhancing Generative Models via Quantum Correlations”
Xun Gao et al · 2022
Cited alongside, same era.
“Deep Generative Models in Engineering Design: A Review” 071704
Lyle Regenwetter, Amin Nobari and Faez Ahmed · 2022
Cited alongside, same era.
“GEO: Enhancing Combinatorial Optimization with Classical and Quantum Generative Models”, 2022
Javier Alcazar et al · 2022
Cited alongside, same era.
“SupermarQ: A Scalable Quantum Benchmark Suite”
Teague Tomesh et al · 2022
Cited alongside, same era.
“NVIDIA cuQuantum Appliance 22.11”, https://docs.nvidia.com/cuda/cuquantum/appliance/release_notes.html#cuquantum-appliance-22-11 , 2022
2022
Later among the works it cites.
“A performance characterization of quantum generative models”, 2023
Carlos. Riofrío et al · 2023
Closest in time.
“Practical overview of image classification with tensor-network quantum circuits”
Diego Guala et al · 2023
Closest in time.
“Quantum machine learning with differential privacy”
William. Watkins, Samuel-Chi Chen and Shinjae Yoo · 2023
Closest in time.
“QScore”, https://github.com/myQLM/qscore , 2023
Atos · 2023
Closest in time.
“Application-Oriented Performance Benchmarks for Quantum Computing”, 2023
Thomas Lubinski et al · 2023
Closest in time.
“Optimization Applications as Quantum Performance Benchmarks”, 2023
Thomas Lubinski et al · 2023
Closest in time.
“Defining Standard Strategies for Quantum Benchmarks”, 2023
Mirko Amico et al · 2023
Closest in time.
“The AI Index 2023 Annual Report”, https://aiindex.stanford.edu/wp-content/uploads/2023/04/HAI_AI-Index-Report_2023.pdf , 2023
Nestor Maslej et al · 2023
Closest in time.
“NVIDIA/cuQuantum: cuQuantum Python v22.11.0.1”
Leo Fang et al · 2023
Closest in time.
“NVIDIA CUDA Quantum: The platform for hybrid quantum-classical computing”, https://developer.nvidia.com/cuda-quantum , 2023
NVIDIA · 2023
Closest in time.
“CMA-ES/pycma: r3.3.0”
Nikolaus Hansen et al · 2023
Closest in time.
“XACC: a system-level software infrastructure for heterogeneous quantum–classical computing”
Alexander McCaskey et al · 2058
Closest in time.