Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T22:59:01.464519Z
Paper Citation Record · LEDGER
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.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T22:59:01.464519Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
61 of 61 outbound references displayed
External citation measurements
No source-named external measurement is stored.
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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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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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Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation Hyper- optimized tensor network contraction
Reference 3
Source-reported events for the cited work
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Reference 4
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Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation Alineartimealgorithmforminimum fill-in and treewidth for distance hereditary graphs
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Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation Graph bisection with pareto optimization
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Reference 10
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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
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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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
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
Source-reported events for the cited work
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Observation f247988f-7945-45ec-a23c-bb5885226cd2 · outbound
Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation Learning high-accuracy error decod- ing for quantum processors
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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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Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation Support vector machine classification of topological phase transitions
Reference 17
Source-reported events for the cited work
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Observation 48e46575-8d23-499a-9a81-3802642890f1 · outbound
Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation Predicting Good Quantum Circuit Compilation Options
Reference 18
Source-reported events for the cited work
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Observation 289bac75-e207-4ec7-8a78-a32bb1418574 · outbound
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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Reference 20
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Observation dc4a8fec-34a6-4c1d-9f07-551eb760fa1b · outbound
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Reference 21
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Reference 22
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Reference 23
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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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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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Observation 03ebc35c-971d-495f-bd05-bc8fc612ceb5 · outbound
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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Observation bb5dabfa-a636-4202-b510-104a31cfc650 · outbound
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
Source-reported events for the cited work
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Observation 67679be2-7f7a-4323-a74d-aa58080d08b0 · outbound
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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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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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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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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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
Source-reported events for the cited work
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Observation 04e0f1d2-be6d-40c3-abe7-4cad342ac1a2 · outbound
Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation Computing Tree Decompositions with FlowCutter: PACE 2017 Submission
Reference 34
Source-reported events for the cited work
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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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Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation Learning to rank us- ing gradient descent
Reference 36
Source-reported events for the cited work
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Observation e0fc2ab3-2ad9-4b1d-b121-38094c8f9894 · outbound
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Reference 37
Source-reported events for the cited work
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Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation Minimal triangulations of graphs: A survey
Reference 38
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Observation 9d11cc21-35c9-4eea-b045-355063ec2e69 · outbound
Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation An introduction to chordal graphs and clique trees
Reference 39
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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
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Reference 41
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Reference 42
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Observation 21b84b70-9f16-41ab-8565-9c71ec83e80b · outbound
Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation Accelerating the XGBoost algo- rithm using GPU computing
Reference 43
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Observation 76447a59-6b91-44e8-87d6-7f3b3f20e3a1 · outbound
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Reference 44
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Reference 45
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Reference 46
Source-reported events for the cited work
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Reference 47
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Reference 48
Source-reported events for the cited work
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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
Source-reported events for the cited work
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Reference 50
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Reference 51
Source-reported events for the cited work
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Reference 52
Source-reported events for the cited work
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Reference 53
Source-reported events for the cited work
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Reference 54
Source-reported events for the cited work
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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
Source-reported events for the cited work
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Reference 56
Source-reported events for the cited work
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Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation Unresolved cited work
Reference 82
Source-reported events for the cited work
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Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation Unresolved cited work
Reference 331
Source-reported events for the cited work
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Reference 794
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Observation 6c55b003-5c58-486d-889b-1aa585a4484e · outbound
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Reference 3443
Source-reported events for the cited work
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Reference 9258
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No inbound Pith citation observations are available.