Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations | Paper Citation Record

Observation 0fe0bbbd-0578-42d5-a413-0558be43fba3 · outbound

This paper cites Machine learning & artificial intelligence in the quantum domain: a review of recent progress.Reports on Progress in Physics, 81(7):074001, 2018.

Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations Machine learning & artificial intelligence in the quantum domain: a review of recent progress.Reports on Progress in Physics, 81(7):074001, 2018

Reference 1

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Observation 807936de-752a-460e-af7b-ee59b292df8c · outbound

This paper cites Quantum machine learning in feature hilbert spaces.

Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations Quantum machine learning in feature hilbert spaces

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T00:40:32.825669Z

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Observation aba7608e-5cbc-4a06-8abd-db0a720ef4ee · outbound

This paper cites Machine learning: Quantum vs classical.IEEE Access, 8:219275–219294, 2020.

Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations Machine learning: Quantum vs classical.IEEE Access, 8:219275–219294, 2020

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:40:39.149982Z

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Observation 757c17a4-6f5b-4497-888a-589954e74dc5 · outbound

This paper cites Sok: quantum computing methods for machine learning optimization.Quan- tum Machine Intelligence, 6(2):47, 2024.

Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations Sok: quantum computing methods for machine learning optimization.Quan- tum Machine Intelligence, 6(2):47, 2024

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:40:39.048318Z

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Observation 1bc5cf12-1af7-4a0b-886b-b0eef07a301a · outbound

This paper cites Parameterized quantum circuits as machine learning models.Quantum science and technology, 4(4):043001, 2019.

Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations Parameterized quantum circuits as machine learning models.Quantum science and technology, 4(4):043001, 2019

Reference 5

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Observation fae7f8bc-1285-4714-bf0c-b38913b1c585 · outbound

This paper cites Quantum data encoding: A comparative analysis of classical-to- quantum mapping techniques and their impact on machine learning accuracy.EPJ Quantum Technology, 11(1):72, 2024.

Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations Quantum data encoding: A comparative analysis of classical-to- quantum mapping techniques and their impact on machine learning accuracy.EPJ Quantum Technology, 11(1):72, 2024

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:40:38.941970Z

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Observation c777e5ad-cfc2-4c5d-a6cf-fd7b4e0fc5ae · outbound

This paper cites Implementing a distance-based classifier with a quantum interference circuit.Europhysics Letters, 119(6):60002, 2017.

Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations Implementing a distance-based classifier with a quantum interference circuit.Europhysics Letters, 119(6):60002, 2017

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:40:38.844873Z

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Observation 543794ba-fe6e-4ed2-8f75-de58a8fd200b · outbound

This paper cites Exponential data encoding for quan- tum supervised learning.Physical Review A, 107(1):012422, 2023.

Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations Exponential data encoding for quan- tum supervised learning.Physical Review A, 107(1):012422, 2023

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:40:38.737197Z

Source-reported events for the cited work

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Observation 859e8e85-0d60-4e25-accc-f6d75fbd6ff7 · outbound

This paper cites Robust data encodings for quantum classifiers.Physical Review A, 102(3):032420, 2020.

Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations Robust data encodings for quantum classifiers.Physical Review A, 102(3):032420, 2020

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T00:40:33.427769Z

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Observation 232baecb-ae33-40ca-8f2c-f429c36d9aad · outbound

This paper cites Quantum computation over continuous variables.Phys- ical Review Letters, 82(8):1784, 1999.

Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations Quantum computation over continuous variables.Phys- ical Review Letters, 82(8):1784, 1999

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:40:38.618778Z

Source-reported events for the cited work

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Observation c199a4fa-99a1-400c-895b-d91ccb798584 · outbound

This paper cites Quantum computing with continuous-variable clusters.Physical Review A—Atomic, Molecu- lar, and Optical Physics, 79(6):062318, 2009.

Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations Quantum computing with continuous-variable clusters.Physical Review A—Atomic, Molecu- lar, and Optical Physics, 79(6):062318, 2009

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:40:38.515587Z

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Observation 9e55f89a-3128-458e-8db0-8c943e11c075 · outbound

This paper cites Hybrid discrete-and continuous-variable quantum information.Nature Physics, 11(9):713–719, 2015.

Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations Hybrid discrete-and continuous-variable quantum information.Nature Physics, 11(9):713–719, 2015

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:40:38.422778Z

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Observation 1177b241-ae8c-481c-b205-47c49e2acfae · outbound

This paper cites Quantum computing overview: discrete vs. continuous variable models.

Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations Quantum computing overview: discrete vs. continuous variable models

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-08-07T00:40:36.137730Z

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Observation 0dbe923a-4c9c-4dc4-a183-691fe97d44c2 · outbound

This paper cites Circuit-centric quantum classifiers.Physical Review A, 101(3):032308, 2020.

Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations Circuit-centric quantum classifiers.Physical Review A, 101(3):032308, 2020

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T00:40:33.932038Z

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Observation 965f121e-dec7-4715-9b05-f93003c3836f · outbound

This paper cites Data re- uploading for a universal quantum classifier.Quantum, 4:226, 2020.

Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations Data re- uploading for a universal quantum classifier.Quantum, 4:226, 2020

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:40:38.248798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2c6545ee-fbc8-499f-9954-c11437eb97ca · outbound

This paper cites Supervised learning with quantum-enhanced feature spaces.Nature, 567(7747):209–212, 2019.

Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations Supervised learning with quantum-enhanced feature spaces.Nature, 567(7747):209–212, 2019

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:40:38.098212Z

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Observation 23e74547-18b8-4ff0-865f-9aa46775e8a2 · outbound

This paper cites Quantum circuit learning.Physical Review A, 98(3):032309, 2018.

Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations Quantum circuit learning.Physical Review A, 98(3):032309, 2018

Reference 17

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Observation 6d1a45f4-8a65-4255-8f4f-c203e50a3d14 · outbound

This paper cites The quest for a quantum neural network.Quantum Information Processing, 13(11):2567–2586, 2018.

Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations The quest for a quantum neural network.Quantum Information Processing, 13(11):2567–2586, 2018

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:40:37.928760Z

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Observation 11530475-1642-4b8c-ac5c-3c8de0252925 · outbound

This paper cites Braunstein and Peter Van Loock.

Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations Braunstein and Peter Van Loock

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:40:37.784401Z

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Observation 67adcb83-e259-4031-b869-0deb3185c041 · outbound

This paper cites Quantum embeddings for machine learning.Physical Review A, 101(3):032305, 2020.

Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations Quantum embeddings for machine learning.Physical Review A, 101(3):032305, 2020

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:40:37.631990Z

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Observation 5abcf107-f37c-414f-9d54-b3107a90e4bf · outbound

This paper cites Efficient measurement-based quantum computing with continuous-variable systems.Physical Review A—Atomic, Molecular, and Optical Physics, 85(6):062318, 2012.

Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations Efficient measurement-based quantum computing with continuous-variable systems.Physical Review A—Atomic, Molecular, and Optical Physics, 85(6):062318, 2012

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:40:37.518687Z

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Observation 8bc3f622-80d2-46c2-bed7-88682a9d265d · outbound

This paper cites Quantum-inspired machine learning: En- coding strategies and interpretability.Quantum, 8:117, 2024.

Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations Quantum-inspired machine learning: En- coding strategies and interpretability.Quantum, 8:117, 2024

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:40:37.392374Z

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Observation fd4bbdd2-38c7-4524-8444-a0acf5d6ef4b · outbound

This paper cites Continuous-variable quantum neural networks.Physical Review Research, 1(3):033063, 2019.

Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations Continuous-variable quantum neural networks.Physical Review Research, 1(3):033063, 2019

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:40:37.313362Z

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Observation 2b796f26-9f84-420f-9087-9efa3babfbff · outbound

This paper cites Measuring analytic gradients of general quantum evolution with the stochastic parameter shift rule.Quantum, 5:386, 2021.

Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations Measuring analytic gradients of general quantum evolution with the stochastic parameter shift rule.Quantum, 5:386, 2021

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:40:37.222093Z

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Observation abed4fb7-f7b0-4e5f-96ae-3ade425db538 · outbound

This paper cites Encoding strategies for efficient quantum data representation.Quantum Science and Technology, 8(1):015005, 2023.

Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations Encoding strategies for efficient quantum data representation.Quantum Science and Technology, 8(1):015005, 2023

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:40:37.164931Z

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Observation 0071b93b-ddaa-4037-8434-2b6b5bd0ee85 · outbound

This paper cites On fundamental aspects of quantum extreme learning machines.

Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations On fundamental aspects of quantum extreme learning machines

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:40:37.089231Z

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Observation 9e868cf0-8fc3-4c99-bbb3-d591605193e6 · outbound

This paper cites A quantum approximate optimization algorithm, 2014.

Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations A quantum approximate optimization algorithm, 2014

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Resolution: unresolved; no resolver link, observed 2026-08-07T00:40:35.177613Z

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Observation 80e44faf-4af1-4998-97d0-bab571afcdd0 · outbound

This paper cites Quantum approximate optimization is computationally universal, 2018.

Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations Quantum approximate optimization is computationally universal, 2018

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:40:37.053071Z

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Observation 583a6aa4-e89b-4288-9d4d-d5fcf3c65037 · outbound

This paper cites Gaussian quantum information.Reviews of Modern Physics, 84(2):621–669, 2012.

Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations Gaussian quantum information.Reviews of Modern Physics, 84(2):621–669, 2012

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:40:36.831902Z

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Observation 281e308c-17ee-4e74-8e3a-68c25d7290b9 · outbound

This paper cites John Wiley & Sons, 2013.

Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations John Wiley & Sons, 2013

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Resolution: unresolved; no resolver link, observed 2026-08-07T00:40:35.485463Z

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Observation 4d8ea361-aa2f-4ead-89e2-217cbd905f0e · outbound

This paper cites Nearest neighbor pattern classification.IEEE transactions on information theory, 13(1):21–27, 1967.

Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations Nearest neighbor pattern classification.IEEE transactions on information theory, 13(1):21–27, 1967

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Observation 10cb0106-3d44-4e36-808a-2a70d6057fda · outbound

This paper cites Support-vector networks.Machine learning, 20:273–297, 1995.

Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations Support-vector networks.Machine learning, 20:273–297, 1995

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:40:36.498816Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0948125c-eb31-45e1-b0ec-f72bbd453fa2 · outbound

This paper cites Random forests.Machine learning, 45:5–32, 2001.

Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations Random forests.Machine learning, 45:5–32, 2001

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Resolution: unresolved; no resolver link, observed 2026-08-07T00:40:35.745473Z

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Observation b38a802e-8322-44c3-89b8-e2777d82dda6 · outbound

This paper cites Lightgbm: A highly efficient gradient boosting decision tree.Advances in neural information processing systems, 30, 2017.

Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations Lightgbm: A highly efficient gradient boosting decision tree.Advances in neural information processing systems, 30, 2017

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Resolution: unresolved; no resolver link, observed 2026-08-07T00:40:35.824569Z

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Observation 6ac6f402-b99e-4e50-b08e-2297128f9d79 · outbound

This paper cites A decision-theoretic generalization of on-line learning and an application to boosting.Journal of computer and system sciences, 55(1):119–139, 1997.

Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations A decision-theoretic generalization of on-line learning and an application to boosting.Journal of computer and system sciences, 55(1):119–139, 1997

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Resolution: unresolved; no resolver link, observed 2026-08-07T00:40:35.915882Z

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Observation 712f6837-04f9-4e6a-8aba-7839837c5e9e · outbound

This paper cites Catboost: unbiased boosting with categorical features.Advances in neural information processing systems, 31, 2018.

Quantum Inspired Encoding Strategies for Machine Learning Models: Proposing and Evaluating Instance Level, Global Discrete, and Class Conditional Representations Catboost: unbiased boosting with categorical features.Advances in neural information processing systems, 31, 2018

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:40:36.289412Z

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