Paper Citation Record · LEDGER

Neural optimization for quantum architectures: graph embedding problems with Distance Encoder Networks

As of 23 July 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2605.03565.

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

pith.paper-citation-record.v1
2605.03565 v1

Coverage vector

measured 42 of 42 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Source: cited_works

Reference resolution

42 of 42 outbound references displayed

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External citation measurements

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Outbound references

Observation ed0043ec-d8b1-439a-b4f9-23ac1e12d653 · outbound

This paper cites Quantum simulators.

Neural optimization for quantum architectures: graph embedding problems with Distance Encoder Networks Quantum simulators

Reference 1

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Observation e54e6e2a-18d9-4f8e-90f3-54e879cb8f2f · outbound

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

Neural optimization for quantum architectures: graph embedding problems with Distance Encoder Networks Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets

Reference 2

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Observation 5bb53cce-0082-44c9-b5ff-b142b0c55b45 · outbound

This paper cites Quantum computational chemistry.

Neural optimization for quantum architectures: graph embedding problems with Distance Encoder Networks Quantum computational chemistry

Reference 3

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This paper cites The unconstrained binary quadratic programming problem: a survey.

Neural optimization for quantum architectures: graph embedding problems with Distance Encoder Networks The unconstrained binary quadratic programming problem: a survey

Reference 4

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This paper cites Quantum annealing in the transverse ising model.

Neural optimization for quantum architectures: graph embedding problems with Distance Encoder Networks Quantum annealing in the transverse ising model

Reference 5

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Observation d058e6b7-d3b7-49ac-bb30-e7aba3b8c445 · outbound

This paper cites A Quantum Approximate Optimization Algorithm.

Neural optimization for quantum architectures: graph embedding problems with Distance Encoder Networks A Quantum Approximate Optimization Algorithm

Reference 6

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This paper cites Optical dipole traps for neutral atoms.

Neural optimization for quantum architectures: graph embedding problems with Distance Encoder Networks Optical dipole traps for neutral atoms

Reference 7

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This paper cites Unit disk graphs.

Neural optimization for quantum architectures: graph embedding problems with Distance Encoder Networks Unit disk graphs

Reference 8

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Neural optimization for quantum architectures: graph embedding problems with Distance Encoder Networks Unresolved cited work

Reference 9

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This paper cites Optimization problems in unit-disk graphs.

Neural optimization for quantum architectures: graph embedding problems with Distance Encoder Networks Optimization problems in unit-disk graphs

Reference 10

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Observation b59901c6-2fe2-4967-85d9-8e946b8e01f2 · outbound

This paper cites Approximation algorithm for minimum weight fault-tolerant virtual backbone in unit disk graphs.

Neural optimization for quantum architectures: graph embedding problems with Distance Encoder Networks Approximation algorithm for minimum weight fault-tolerant virtual backbone in unit disk graphs

Reference 11

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Observation 324a7921-2abd-4fb1-a21b-f4f711b5dae6 · outbound

This paper cites Algorithmic, geometric and graphs issues in wireless net- works.

Neural optimization for quantum architectures: graph embedding problems with Distance Encoder Networks Algorithmic, geometric and graphs issues in wireless net- works

Reference 12

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Observation f2e3ccd7-afeb-40b9-9cd7-de69242c5911 · outbound

This paper cites Ising formulations of many NP problems.

Neural optimization for quantum architectures: graph embedding problems with Distance Encoder Networks Ising formulations of many NP problems

Reference 13

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This paper cites Solving optimization problems with rydberg analog quantum computers: Realistic requirements for quantum advantage using noisy simulation and classical benchmarks.

Neural optimization for quantum architectures: graph embedding problems with Distance Encoder Networks Solving optimization problems with rydberg analog quantum computers: Realistic requirements for quantum advantage using noisy simulation and classical benchmarks

Reference 14

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Observation d6abed28-4648-4cfa-86f6-dc76f66a48fe · outbound

This paper cites Wintersperger, F.

Neural optimization for quantum architectures: graph embedding problems with Distance Encoder Networks Wintersperger, F

Reference 15

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This paper cites A Tutorial on Formulating and Using QUBO Models.

Neural optimization for quantum architectures: graph embedding problems with Distance Encoder Networks A Tutorial on Formulating and Using QUBO Models

Reference 16

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Observation 80494d11-975a-4cd3-b15b-da3afca790dc · outbound

This paper cites Adiabatic quantum computation.

Neural optimization for quantum architectures: graph embedding problems with Distance Encoder Networks Adiabatic quantum computation

Reference 17

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Observation da8b62ee-1ce9-4b54-b6f9-5f215cccab8c · outbound

This paper cites Ultracold rubidium atoms excited to rydberg levels.

Neural optimization for quantum architectures: graph embedding problems with Distance Encoder Networks Ultracold rubidium atoms excited to rydberg levels

Reference 18

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This paper cites Entanglement of neutral-atom qubits with long ground-rydberg coherence times.

Neural optimization for quantum architectures: graph embedding problems with Distance Encoder Networks Entanglement of neutral-atom qubits with long ground-rydberg coherence times

Reference 19

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This paper cites Unit disk graph recognition is NP-hard.

Neural optimization for quantum architectures: graph embedding problems with Distance Encoder Networks Unit disk graph recognition is NP-hard

Reference 20

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Neural optimization for quantum architectures: graph embedding problems with Distance Encoder Networks Unit disk graph approx- imation

Reference 21

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Observation 33af0a6a-cdb5-4f18-a593-3d700c05c46d · outbound

This paper cites Characterizations of outerplanar graphs.

Neural optimization for quantum architectures: graph embedding problems with Distance Encoder Networks Characterizations of outerplanar graphs

Reference 22

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Neural optimization for quantum architectures: graph embedding problems with Distance Encoder Networks Unit Disk Representations of Embedded Trees, Outerplanar and Multi-Legged Graphs

Reference 23

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Observation fe65ddce-01fa-43b0-8bc2-5ae6dfb0a0a4 · outbound

This paper cites Virtual coordinates for ad hoc and sensor networks.

Neural optimization for quantum architectures: graph embedding problems with Distance Encoder Networks Virtual coordinates for ad hoc and sensor networks

Reference 24

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Observation d41d6e1c-fc6f-40f3-94cb-c3f80d4da57c · outbound

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Neural optimization for quantum architectures: graph embedding problems with Distance Encoder Networks Good quality virtual realization of unit disk graphs

Reference 25

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Observation ddfae449-25da-4868-9bf0-e8f955c94f60 · outbound

This paper cites On the approximation on unit disk graph coordinates.

Neural optimization for quantum architectures: graph embedding problems with Distance Encoder Networks On the approximation on unit disk graph coordinates

Reference 26

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Observation e11d9f7a-b949-4393-b413-725e15e9b831 · outbound

This paper cites Practical virtual coordinates for large wireless sensor networks.

Neural optimization for quantum architectures: graph embedding problems with Distance Encoder Networks Practical virtual coordinates for large wireless sensor networks

Reference 27

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This paper cites The gurobi optimizer.

Neural optimization for quantum architectures: graph embedding problems with Distance Encoder Networks The gurobi optimizer

Reference 28

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Observation b7072f19-1c34-4080-a945-74708a46f402 · outbound

This paper cites On the implementation of an interior- point filter line-search algorithm for large-scale nonlinear programming.

Neural optimization for quantum architectures: graph embedding problems with Distance Encoder Networks On the implementation of an interior- point filter line-search algorithm for large-scale nonlinear programming

Reference 29

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Observation dd417e92-b121-4772-91b7-193cf09c5eb3 · outbound

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Neural optimization for quantum architectures: graph embedding problems with Distance Encoder Networks Deep neural networks and mixed integer linear optimization

Reference 30

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Observation 70dd91e3-4cbb-4680-837a-cdf44fca1308 · outbound

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Neural optimization for quantum architectures: graph embedding problems with Distance Encoder Networks Optnet: Differentiable optimization as a layer in neural networks

Reference 31

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Neural optimization for quantum architectures: graph embedding problems with Distance Encoder Networks Tounn: topology optimization using neural networks

Reference 32

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Neural optimization for quantum architectures: graph embedding problems with Distance Encoder Networks Multi-material topology optimization using neural networks

Reference 33

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This paper cites Reinforcement Learning to Solve NP-hard Problems: an Application to the CVRP.

Neural optimization for quantum architectures: graph embedding problems with Distance Encoder Networks Reinforcement Learning to Solve NP-hard Problems: an Application to the CVRP

Reference 34

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This paper cites Solving NP-Hard Problems on Graphs with Extended AlphaGo Zero.

Neural optimization for quantum architectures: graph embedding problems with Distance Encoder Networks Solving NP-Hard Problems on Graphs with Extended AlphaGo Zero

Reference 35

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This paper cites Nn-baker: A neural-network infused algorithmic framework for optimization prob- lems on geometric intersection graphs.

Neural optimization for quantum architectures: graph embedding problems with Distance Encoder Networks Nn-baker: A neural-network infused algorithmic framework for optimization prob- lems on geometric intersection graphs

Reference 36

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This paper cites A Simple Proof of Thue's Theorem on Circle Packing.

Neural optimization for quantum architectures: graph embedding problems with Distance Encoder Networks A Simple Proof of Thue's Theorem on Circle Packing

Reference 37

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Observation 39aae55c-3c4b-4a4c-a206-9e3f25f02976 · outbound

This paper cites Approximating maximum indepen- dent sets by excluding subgraphs.

Neural optimization for quantum architectures: graph embedding problems with Distance Encoder Networks Approximating maximum indepen- dent sets by excluding subgraphs

Reference 38

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Neural optimization for quantum architectures: graph embedding problems with Distance Encoder Networks Graph drawing by force- directed placement

Reference 39

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Observation c9a42e92-0c97-4290-9aee-a36de323db74 · outbound

This paper cites Exploring network struc- ture, dynamics, and function using networkx.

Neural optimization for quantum architectures: graph embedding problems with Distance Encoder Networks Exploring network struc- ture, dynamics, and function using networkx

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T12:04:03.304086Z

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Observation 64202593-6b06-4ba2-a93d-0fd5f2c79833 · outbound

This paper cites Dropout training as adaptive regularization.

Neural optimization for quantum architectures: graph embedding problems with Distance Encoder Networks Dropout training as adaptive regularization

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-27T12:04:03.349184Z

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This paper cites Decoupled Weight Decay Regularization.

Neural optimization for quantum architectures: graph embedding problems with Distance Encoder Networks Decoupled Weight Decay Regularization

Reference 42

Resolution: verified exact; local_arxiv, observed 2026-05-12T11:01:30.205422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-20T06:30:07.809122+00:00.

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