Paper Citation Record · LEDGER

Physics-Informed Quantum Machine Learning with Hard Constraint Embedding for Nonlinear Differential Equations of the First Order

As of 9 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2608.03029.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2608.03029 v1

Coverage vector

measured 34 of 34 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

34 of 34 outbound references displayed

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External citation measurements

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Outbound references

Observation 882a925c-b677-4062-906c-a5bbefb6d7fc · outbound

This paper cites Quan- tum computation and quantum information.

Physics-Informed Quantum Machine Learning with Hard Constraint Embedding for Nonlinear Differential Equations of the First Order Quan- tum computation and quantum information

Reference 1

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Observation 6c394dbc-0e38-4602-ac3d-95192b2030b0 · outbound

This paper cites Study of stability criteria of numerical solution of or- dinary and partial diﬀerential equations using eulers and ﬁnite diﬀerence scheme.

Physics-Informed Quantum Machine Learning with Hard Constraint Embedding for Nonlinear Differential Equations of the First Order Study of stability criteria of numerical solution of or- dinary and partial diﬀerential equations using eulers and ﬁnite diﬀerence scheme

Reference 2

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This paper cites Spec- tral element method in time for rapidly actuated systems.

Physics-Informed Quantum Machine Learning with Hard Constraint Embedding for Nonlinear Differential Equations of the First Order Spec- tral element method in time for rapidly actuated systems

Reference 3

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Observation ad1b5067-22f5-40d4-a633-cc6800c25436 · outbound

This paper cites Quantum algorithm and circuit design solving the poisson equation.

Physics-Informed Quantum Machine Learning with Hard Constraint Embedding for Nonlinear Differential Equations of the First Order Quantum algorithm and circuit design solving the poisson equation

Reference 4

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Observation 9aa6dcd5-76d0-4f5f-8c1b-a1576bf251c8 · outbound

This paper cites High-order quantum algo- rithm for solving linear diﬀerential equations.

Physics-Informed Quantum Machine Learning with Hard Constraint Embedding for Nonlinear Differential Equations of the First Order High-order quantum algo- rithm for solving linear diﬀerential equations

Reference 5

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Observation 89f63d54-3507-45cf-bfc1-15427d4f355c · outbound

This paper cites Quantum algo- rithm for linear diﬀerential equations with ex- ponentially improved dependence on precision.

Physics-Informed Quantum Machine Learning with Hard Constraint Embedding for Nonlinear Differential Equations of the First Order Quantum algo- rithm for linear diﬀerential equations with ex- ponentially improved dependence on precision

Reference 6

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Observation dc376635-7090-4046-a7ef-ff889a13c8b6 · outbound

This paper cites Quan- tum spectral methods for diﬀerential equations.

Physics-Informed Quantum Machine Learning with Hard Constraint Embedding for Nonlinear Differential Equations of the First Order Quan- tum spectral methods for diﬀerential equations

Reference 7

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Observation ddd95545-ab66-480c-97bb-ed379ec0cfa9 · outbound

This paper cites Quantum computing in the nisq era and beyond.

Physics-Informed Quantum Machine Learning with Hard Constraint Embedding for Nonlinear Differential Equations of the First Order Quantum computing in the nisq era and beyond

Reference 8

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Observation 6b29db92-b0b6-4ff0-8804-badb91f63914 · outbound

This paper cites Parameterized quantum circuits as machine learning models.

Physics-Informed Quantum Machine Learning with Hard Constraint Embedding for Nonlinear Differential Equations of the First Order Parameterized quantum circuits as machine learning models

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:20:38.825442Z

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Observation 878ce403-1202-4972-b4b1-76fd5c295e77 · outbound

This paper cites The theory of variational hybrid quantum-classical algorithms.

Physics-Informed Quantum Machine Learning with Hard Constraint Embedding for Nonlinear Differential Equations of the First Order The theory of variational hybrid quantum-classical algorithms

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:20:38.814112Z

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Observation ff7a7b5b-2f86-453b-8f08-942f76c86f97 · outbound

This paper cites Hardware- eﬃcient variational quantum eigensolver for small molecules and quantum magnets.

Physics-Informed Quantum Machine Learning with Hard Constraint Embedding for Nonlinear Differential Equations of the First Order Hardware- eﬃcient variational quantum eigensolver for small molecules and quantum magnets

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:20:38.802623Z

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Observation 379d3a01-06b7-4f49-8cf2-4ecf35ab3802 · outbound

This paper cites Quantum approximate op- timization algorithm for maxcut: A fermionic view.

Physics-Informed Quantum Machine Learning with Hard Constraint Embedding for Nonlinear Differential Equations of the First Order Quantum approximate op- timization algorithm for maxcut: A fermionic view

Reference 12

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Observation c91f086e-c23e-45e5-8b6c-d402c3a490bf · outbound

This paper cites Variational quantum linear solver.

Physics-Informed Quantum Machine Learning with Hard Constraint Embedding for Nonlinear Differential Equations of the First Order Variational quantum linear solver

Reference 13

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Observation bb59c243-6a9b-4918-8bb8-bec7a5613a8a · outbound

This paper cites Quantum algo- rithms for feedforward neural networks.

Physics-Informed Quantum Machine Learning with Hard Constraint Embedding for Nonlinear Differential Equations of the First Order Quantum algo- rithms for feedforward neural networks

Reference 14

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Observation c248ec33-1774-4279-9415-7bbb3873ec27 · outbound

This paper cites A Quantum Approximate Optimization Algorithm.

Physics-Informed Quantum Machine Learning with Hard Constraint Embedding for Nonlinear Differential Equations of the First Order A Quantum Approximate Optimization Algorithm

Reference 15

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Observation d10c022c-7662-43f6-b325-7f2c114780f0 · outbound

This paper cites A variational quantum algorithm for the poisson equation based on the banded toeplitz systems.

Physics-Informed Quantum Machine Learning with Hard Constraint Embedding for Nonlinear Differential Equations of the First Order A variational quantum algorithm for the poisson equation based on the banded toeplitz systems

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:20:38.764167Z

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Observation 85075a1f-29db-41e9-a4e7-8e879e5554bd · outbound

This paper cites Variational quantum algorithm based on the minimum po- tential energy for solving the poisson equation.

Physics-Informed Quantum Machine Learning with Hard Constraint Embedding for Nonlinear Differential Equations of the First Order Variational quantum algorithm based on the minimum po- tential energy for solving the poisson equation

Reference 17

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This paper cites Variational quantum evolution equation solver.

Physics-Informed Quantum Machine Learning with Hard Constraint Embedding for Nonlinear Differential Equations of the First Order Variational quantum evolution equation solver

Reference 18

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Observation 1f748491-9d78-4bd6-9a37-72da523f30a0 · outbound

This paper cites Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial diﬀerential equations.

Physics-Informed Quantum Machine Learning with Hard Constraint Embedding for Nonlinear Differential Equations of the First Order Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial diﬀerential equations

Reference 19

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Observation 4007ac59-7875-46e3-a26e-94b84616592a · outbound

This paper cites Physics-informed neural networks (pinns) for ﬂuid mechanics: A review.

Physics-Informed Quantum Machine Learning with Hard Constraint Embedding for Nonlinear Differential Equations of the First Order Physics-informed neural networks (pinns) for ﬂuid mechanics: A review

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:20:38.723858Z

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Observation a548f89d-6fef-49b8-b20b-9c66b812fda8 · outbound

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Physics-Informed Quantum Machine Learning with Hard Constraint Embedding for Nonlinear Differential Equations of the First Order Unresolved cited work

Reference 21

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Observation 4249b796-cc7e-411f-b797-ce4e093ace7e · outbound

This paper cites Self-adaptive physics-informed quantum machine learning for solving diﬀerential equa- tions.

Physics-Informed Quantum Machine Learning with Hard Constraint Embedding for Nonlinear Differential Equations of the First Order Self-adaptive physics-informed quantum machine learning for solving diﬀerential equa- tions

Reference 22

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Observation 6d04342c-d674-48e4-a456-4bce1055fd41 · outbound

This paper cites Quantum physics-informed neural networks.

Physics-Informed Quantum Machine Learning with Hard Constraint Embedding for Nonlinear Differential Equations of the First Order Quantum physics-informed neural networks

Reference 23

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Observation 54766900-fa3e-4c52-8987-a4aca1d0335c · outbound

This paper cites Hybrid quantum physics- informed neural networks for simulating compu- 11 tational ﬂuid dynamics in complex shapes.

Physics-Informed Quantum Machine Learning with Hard Constraint Embedding for Nonlinear Differential Equations of the First Order Hybrid quantum physics- informed neural networks for simulating compu- 11 tational ﬂuid dynamics in complex shapes

Reference 24

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Observation 2dc67c85-6ee9-4363-936a-7148aefc63bb · outbound

This paper cites Solving transport equations on quantum computers—potential and limitations of physics-informed quantum circuits.

Physics-Informed Quantum Machine Learning with Hard Constraint Embedding for Nonlinear Differential Equations of the First Order Solving transport equations on quantum computers—potential and limitations of physics-informed quantum circuits

Reference 25

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Observation 71ccda38-d53b-479c-a90a-0120f561c95f · outbound

This paper cites On physics-informed neural networks for quantum computers.

Physics-Informed Quantum Machine Learning with Hard Constraint Embedding for Nonlinear Differential Equations of the First Order On physics-informed neural networks for quantum computers

Reference 26

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Observation b5f0b825-e593-4075-a3a5-8b05b5c88bf7 · outbound

This paper cites Solving Differential Equations via Continuous-Variable Quantum Computers.

Physics-Informed Quantum Machine Learning with Hard Constraint Embedding for Nonlinear Differential Equations of the First Order Solving Differential Equations via Continuous-Variable Quantum Computers

Reference 27

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This paper cites Quantum physics-informed neural net- works for multivariable partial diﬀerential equa- tions.

Physics-Informed Quantum Machine Learning with Hard Constraint Embedding for Nonlinear Differential Equations of the First Order Quantum physics-informed neural net- works for multivariable partial diﬀerential equa- tions

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:20:38.640129Z

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Observation b38a08fe-bf3e-44e2-b867-42dc0e737c44 · outbound

This paper cites Physics-Informed Quantum Machine Learning: Solving nonlinear differential equations in latent spaces without costly grid evaluations.

Physics-Informed Quantum Machine Learning with Hard Constraint Embedding for Nonlinear Differential Equations of the First Order Physics-Informed Quantum Machine Learning: Solving nonlinear differential equations in latent spaces without costly grid evaluations

Reference 29

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Observation 1f6522df-3dd0-45ed-a4a2-b58f395de8ae · outbound

This paper cites Approximation Theory and Approximation Practice.

Physics-Informed Quantum Machine Learning with Hard Constraint Embedding for Nonlinear Differential Equations of the First Order Approximation Theory and Approximation Practice

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:20:38.628948Z

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Observation 00d52a49-8bc3-4357-80fb-6ca3ce8accbd · outbound

This paper cites Eval- uating analytic gradients on quantum hardware.

Physics-Informed Quantum Machine Learning with Hard Constraint Embedding for Nonlinear Differential Equations of the First Order Eval- uating analytic gradients on quantum hardware

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:20:38.617963Z

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Observation 108c2c9b-39be-4456-8879-a735af6e981b · outbound

This paper cites Qadence: A python package for diﬀeren- tiable digital-analog quantum programs.

Physics-Informed Quantum Machine Learning with Hard Constraint Embedding for Nonlinear Differential Equations of the First Order Qadence: A python package for diﬀeren- tiable digital-analog quantum programs

Reference 32

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Physics-Informed Quantum Machine Learning with Hard Constraint Embedding for Nonlinear Differential Equations of the First Order Unresolved cited work

Reference 2022

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Physics-Informed Quantum Machine Learning with Hard Constraint Embedding for Nonlinear Differential Equations of the First Order Unresolved cited work

Reference 2024

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Pith citing papers

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