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Paper Citation Record · LEDGER

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation

As of 9 August 2026, this Paper Citation Record lists 61 of 61 outbound references and 0 inbound Pith citation observations for arXiv:2608.05819.

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

pith.paper-citation-record.v1
2608.05819 v1

Coverage vector

measured 61 of 61 reference resolution

Typed states for the displayed outbound observations.

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measured 61 of 61 standing notices

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Reference resolution

61 of 61 outbound references displayed

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External citation measurements

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Outbound references

Observation 2ab4ded8-9c7d-4d54-a3b1-94f6424c1b7d · outbound

This paper cites Simulating quantum computation by contracting tensor networks.

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation Simulating quantum computation by contracting tensor networks

Reference 1

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Observation e62df171-1828-461f-a486-3c0bfface493 · outbound

This paper cites A practical introduction to tensor networks: Matrix product states and projected entangled pair states.

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation A practical introduction to tensor networks: Matrix product states and projected entangled pair states

Reference 2

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Observation 4d53ca0e-92fc-4dfc-a95f-aa69696aaba9 · outbound

This paper cites Hyper- optimized tensor network contraction.

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation Hyper- optimized tensor network contraction

Reference 3

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Observation e7bb7af6-70c9-4e87-9161-1343650aa65d · outbound

This paper cites Faster identifica- tion of optimal contraction sequences for tensor networks.

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation Faster identifica- tion of optimal contraction sequences for tensor networks

Reference 4

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Observation 617ceddc-b3b8-49f9-970e-47d60aa58e92 · outbound

This paper cites Alineartimealgorithmforminimum fill-in and treewidth for distance hereditary graphs.

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation Alineartimealgorithmforminimum fill-in and treewidth for distance hereditary graphs

Reference 5

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Observation 4fe910a3-1fc8-4e6a-83ee-849274db9b67 · outbound

This paper cites Community structure in social and biolog- ical networks.

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation Community structure in social and biolog- ical networks

Reference 6

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Observation ce59a1a5-024a-4263-83fa-889c6add7a90 · outbound

This paper cites Graph bisection with pareto optimization.

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation Graph bisection with pareto optimization

Reference 7

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Observation 610bfb0e-343b-4327-80c1-e35825d9c043 · outbound

This paper cites cuQuantum ten- sor network contraction documentation.

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation cuQuantum ten- sor network contraction documentation

Reference 8

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Observation 002bf9e1-ea4a-47a7-bfc7-24fa7510b5b7 · outbound

This paper cites cuTENSOR user guide.

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation cuTENSOR user guide

Reference 9

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Observation 5b509023-0d6b-4d71-901e-c4591d7edbc8 · outbound

This paper cites Learning to rank for informa- tion retrieval.

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation Learning to rank for informa- tion retrieval

Reference 10

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Observation d37ddccf-1c15-485c-a722-58684b52bea3 · outbound

This paper cites Learning to rank quantum circuits for hardware-optimized performance enhancement.

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation Learning to rank quantum circuits for hardware-optimized performance enhancement

Reference 11

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Observation 838056f8-5f79-4c19-87a2-4e5caaa91465 · outbound

This paper cites XG- Boost: A scalable tree boosting system.

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation XG- Boost: A scalable tree boosting system

Reference 12

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Observation fcb3119c-d144-47c7-8007-14a0777046b9 · outbound

This paper cites Machine learning with quantum comput- ers.

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation Machine learning with quantum comput- ers

Reference 13

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Observation 2fd14344-4b30-4a18-b66e-6de0e55c009a · outbound

This paper cites A Survey on Quantum Machine Learning: Current Trends, Challenges, Opportunities, and the Road Ahead.

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation A Survey on Quantum Machine Learning: Current Trends, Challenges, Opportunities, and the Road Ahead

Reference 14

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Observation f247988f-7945-45ec-a23c-bb5885226cd2 · outbound

This paper cites Learning high-accuracy error decod- ing for quantum processors.

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation Learning high-accuracy error decod- ing for quantum processors

Reference 15

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Observation 9199e7ba-dbbb-47b2-957d-a7c15f3b5b4f · outbound

This paper cites Support vector machines for 19 quantum state tomography.

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation Support vector machines for 19 quantum state tomography

Reference 16

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Observation 5bca055e-7179-4c7a-b499-b3567ab501f5 · outbound

This paper cites Support vector machine classification of topological phase transitions.

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation Support vector machine classification of topological phase transitions

Reference 17

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Observation 48e46575-8d23-499a-9a81-3802642890f1 · outbound

This paper cites Predicting Good Quantum Circuit Compilation Options.

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation Predicting Good Quantum Circuit Compilation Options

Reference 18

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Observation 289bac75-e207-4ec7-8a78-a32bb1418574 · outbound

This paper cites Neural-network quantum state tomogra- phy.

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation Neural-network quantum state tomogra- phy

Reference 19

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Observation e3b37cce-9b70-46b3-8241-7e0cd00fe032 · outbound

This paper cites Learning to learn with quantum neural networks via classical neural networks.

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation Learning to learn with quantum neural networks via classical neural networks

Reference 20

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This paper cites Methods, applications, and directions of learning-to- rank in NLP research.

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation Methods, applications, and directions of learning-to- rank in NLP research

Reference 21

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Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation A Survey on E-Commerce Learning to Rank

Reference 22

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Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation Unified learning-to-rank for multi-channel retrieval in large-scale e-commerce search

Reference 23

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This paper cites Efficient and effec- tive tree-based and neural learning to rank.

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation Efficient and effec- tive tree-based and neural learning to rank

Reference 24

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This paper cites Re- cent advances in the foundations and appli- cations of unbiased learning to rank.

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation Re- cent advances in the foundations and appli- cations of unbiased learning to rank

Reference 25

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Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation A literature review on methods for learning to rank

Reference 26

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This paper cites Empowering quantum serverless circuit de- ployment optimization via graph contrastive learning and learning-to-rank co-designed approaches.

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation Empowering quantum serverless circuit de- ployment optimization via graph contrastive learning and learning-to-rank co-designed approaches

Reference 27

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Observation 67679be2-7f7a-4323-a74d-aa58080d08b0 · outbound

This paper cites Efficient quantum cir- cuit contraction using tensor decision dia- grams.

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation Efficient quantum cir- cuit contraction using tensor decision dia- grams

Reference 28

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This paper cites Parameterization of ten- sor network contraction.

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation Parameterization of ten- sor network contraction

Reference 29

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This paper cites Design of a high-performance GEMM-like tensor- tensor multiplication.

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation Design of a high-performance GEMM-like tensor- tensor multiplication

Reference 30

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Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation High-performance tensor contraction without transposition

Reference 31

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This paper cites A flexible high-performance simulator for ver- ifying and benchmarking quantum circuits implemented on real hardware.

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation A flexible high-performance simulator for ver- ifying and benchmarking quantum circuits implemented on real hardware

Reference 32

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This paper cites Benchmarking treewidth as a practical component of tensor network simulations.

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation Benchmarking treewidth as a practical component of tensor network simulations

Reference 33

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Observation 04e0f1d2-be6d-40c3-abe7-4cad342ac1a2 · outbound

This paper cites Computing Tree Decompositions with FlowCutter: PACE 2017 Submission.

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation Computing Tree Decompositions with FlowCutter: PACE 2017 Submission

Reference 34

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Observation b053c870-2ff5-4a3b-979f-65faad23fd9d · outbound

This paper cites Finding and evaluating community struc- ture in networks.

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation Finding and evaluating community struc- ture in networks

Reference 35

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation fbba46d8-cfb9-464b-b950-3fd13241fd74 · outbound

This paper cites Learning to rank us- ing gradient descent.

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation Learning to rank us- ing gradient descent

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T22:59:02.170469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation e0fc2ab3-2ad9-4b1d-b121-38094c8f9894 · outbound

This paper cites From RankNet to LambdaRank to LambdaMART: An overview.

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation From RankNet to LambdaRank to LambdaMART: An overview

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T22:59:02.159053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T22:59:01.389664Z digest=sha256:a958bd63173217a84d36d92f871954ab96f8a127f3f44611afaf943267f943b0

Observation fe434c19-52f8-4411-9575-11a232e8d811 · outbound

This paper cites Minimal triangulations of graphs: A survey.

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation Minimal triangulations of graphs: A survey

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T22:59:02.136029Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 9d11cc21-35c9-4eea-b045-355063ec2e69 · outbound

This paper cites An introduction to chordal graphs and clique trees.

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation An introduction to chordal graphs and clique trees

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T22:59:02.124399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 561f7cb6-7076-4e57-baee-31df1ac7053f · outbound

This paper cites MQT Bench: Benchmark- ing software and design automation tools for quantum computing.

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation MQT Bench: Benchmark- ing software and design automation tools for quantum computing

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T22:59:02.102735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T22:59:01.404970Z digest=sha256:6e7f4b260e78eceed214775b765e7a2718b9740f19621e86aa85a8bc68dfd467

Observation 5b95eb3b-ff8d-43c9-af79-eee936fbc30d · outbound

This paper cites The PACE 2017 Parameterized Algorithms and Computational Experiments Challenge: The Second Iteration.

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation The PACE 2017 Parameterized Algorithms and Computational Experiments Challenge: The Second Iteration

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T22:59:02.088872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 2bb7af2b-e8ba-419c-b31e-ff323f3de139 · outbound

This paper cites Intro- duction to machine learning with python: A guide for data scientists.

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation Intro- duction to machine learning with python: A guide for data scientists

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T22:59:02.070814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 21b84b70-9f16-41ab-8565-9c71ec83e80b · outbound

This paper cites Accelerating the XGBoost algo- rithm using GPU computing.

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation Accelerating the XGBoost algo- rithm using GPU computing

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T22:59:02.059105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 76447a59-6b91-44e8-87d6-7f3b3f20e3a1 · outbound

This paper cites Feature Interactions in XGBoost.

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation Feature Interactions in XGBoost

Reference 44

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verified exact
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 4a4515f9-c988-444c-8309-45fb4afb4676 · outbound

This paper cites A uni- fied approach to interpreting model predic- tions.

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation A uni- fied approach to interpreting model predic- tions

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T22:59:02.047358Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T22:59:01.424079Z digest=sha256:c948fd94997bf6949606b1b5ad36a27b81600d8b842bab0a13c68460c5184760

Observation 84cc5dc1-4094-4f6b-b7f1-3feebd59dd3d · outbound

This paper cites QXTools: A Julia framework for distributed quantum circuit simula- tion.

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation QXTools: A Julia framework for distributed quantum circuit simula- tion

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T22:59:02.035104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 87f64feb-1895-4411-bcf8-f6e750d7de71 · outbound

This paper cites XG- Boost.jl: Julia interface to XGBoost.

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation XG- Boost.jl: Julia interface to XGBoost

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T22:59:02.022644Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 99c04075-7ee8-4cc7-9371-f8120eefca21 · outbound

This paper cites Rankcor- relation methods.

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation Rankcor- relation methods

Reference 48

Resolution
verified exact
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation ccdbe28a-2259-4558-b6f5-2389635bf924 · outbound

This paper cites Efficient parallelization of tensor network contraction for simulating quantum computation.

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation Efficient parallelization of tensor network contraction for simulating quantum computation

Reference 49

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 043777c1-3339-4f86-b417-293a9a4206e4 · outbound

This paper cites Jet: Fast quan- tum circuit simulations with parallel task- based tensor-network contraction.

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation Jet: Fast quan- tum circuit simulations with parallel task- based tensor-network contraction

Reference 50

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 99ec9827-f9d2-4006-a9e1-0c52ce21edd2 · outbound

This paper cites Efficient quantum cir- 21 cuit simulation by tensor network methods on modern GPUs.

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation Efficient quantum cir- 21 cuit simulation by tensor network methods on modern GPUs

Reference 51

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verified fuzzy
raw_fallback, observed 2026-08-07T22:59:01.984519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation e10dcc3a-2eb5-405a-9722-770e72b39e43 · outbound

This paper cites Parallel tensor network con- traction for efficient quantum circuit simu- lation on multicore CPUs and GPUs.

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation Parallel tensor network con- traction for efficient quantum circuit simu- lation on multicore CPUs and GPUs

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T22:59:01.970936Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 48209678-0267-4ff8-a9e0-a2ed36a11d7e · outbound

This paper cites A community detection-based parallel algorithm for quantum circuit simu- lation using tensor networks.

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation A community detection-based parallel algorithm for quantum circuit simu- lation using tensor networks

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T22:59:01.958736Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T22:59:01.453302Z digest=sha256:5e90c98696ac3975a541caba51bea9e733e5d1e9b432f3a0e389eb50177f509f

Observation 40623e44-9e5e-40e3-8ff2-d46f18b295ea · outbound

This paper cites Tensor networks for quan- tum computing.

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation Tensor networks for quan- tum computing

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T22:59:01.946578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T22:59:01.456846Z digest=sha256:61013d259dfabac146240a8249c6afe63d4a201000266c0c1f927470a4496480

Observation 790bb182-5e27-4870-989f-aea4f3e0b857 · outbound

This paper cites Roofline: An insightful visual performance model for multicore ar- chitectures.

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation Roofline: An insightful visual performance model for multicore ar- chitectures

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T22:59:01.933842Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T22:59:01.460832Z digest=sha256:af966ad0804b81ec8ae64aa23a050cb43fdc4609f3683667a489066960331846

Observation a38fe7f5-f136-494d-b817-6180a06475c9 · outbound

This paper cites TensorOpera- tions.jl documentation.

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation TensorOpera- tions.jl documentation

Reference 56

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verified fuzzy
raw_fallback, observed 2026-08-07T22:59:01.921539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T22:59:01.464519Z digest=sha256:e5d5cd2da168368a8fcc9301969197d74d4c1ac22ae05d189e032af519d44ae8

Observation 66cb2502-ea0e-422e-802c-ffd1d88b0947 · outbound

This paper cites an unresolved cited work.

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation Unresolved cited work

Reference 82

Resolution
unresolved
raw_fallback, observed 2026-08-07T22:59:02.147426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 8e476f67-324f-438a-a252-03cb672fea4f · outbound

This paper cites an unresolved cited work.

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation Unresolved cited work

Reference 331

Resolution
parse uncertain
raw_fallback, observed 2026-08-07T22:59:02.415029Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T22:59:01.275275Z digest=sha256:5872bbf6ddbe4cfd0627810190be1b5fd7b0361013ce5146a07a25125621b395

Observation aed49c21-0595-4d54-8dea-1415c28711fc · outbound

This paper cites an unresolved cited work.

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation Unresolved cited work

Reference 794

Resolution
unresolved
raw_fallback, observed 2026-08-07T22:59:02.391643Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T22:59:01.287150Z digest=sha256:2d30eb2b6a00196635a167f51425164ac40317de1c04e45b4366d8bc85488d36

Observation 6c55b003-5c58-486d-889b-1aa585a4484e · outbound

This paper cites Recent Advances in the Foundations and Applications of Unbiased Learning to Rank.

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation Recent Advances in the Foundations and Applications of Unbiased Learning to Rank

Reference 3443

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unresolved
no resolver link, observed 2026-08-07T22:59:01.340072Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T22:59:01.340072Z digest=sha256:dc1c97809d97c360fa55a48be20292174484f76c85422b069c40d3e9f268d84c

Observation 974360fb-c91b-4137-a9f2-e0ac291197df · outbound

This paper cites an unresolved cited work.

Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation Unresolved cited work

Reference 9258

Resolution
unresolved
raw_fallback, observed 2026-08-07T22:59:02.263227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T22:59:01.351944Z digest=sha256:94f0aa3a343707e059c4d980ffb8d71b1e6df0456ab989e20e20dabaaefa1bf4

Pith citing papers

No inbound Pith citation observations are available.