Paper Citation Record · LEDGER

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

As of 10 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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:20:38.774538Z

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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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Observation 0c03fe99-c2c2-4dc7-acae-8f4518cbf91b · 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 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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