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Paper Citation Record · LEDGER

Generative Learning for Quantum Measurement Design

As of 20 August 2026, this Paper Citation Record lists 58 of 58 outbound references and 0 inbound Pith citation observations for arXiv:2608.11396.

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

pith.paper-citation-record.v1
2608.11396 v1

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measured 58 of 58 reference resolution

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58 of 58 outbound references displayed

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Outbound references

Observation aecb6ed7-3a9f-4970-9b5c-a5a009a4280e · outbound

This paper cites A variational eigenvalue solver on a photonic quantum processor.

Generative Learning for Quantum Measurement Design A variational eigenvalue solver on a photonic quantum processor

Reference 1

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Observation 27fc392e-d935-41de-ba7c-1c8103ce24fd · outbound

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

Generative Learning for Quantum Measurement Design Quantum computing in the NISQ era and beyond

Reference 2

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Observation db8e2914-4173-4954-bf84-ac676c976d13 · outbound

This paper cites The variational quantum eigensolver: A review of methods and best practices.

Generative Learning for Quantum Measurement Design The variational quantum eigensolver: A review of methods and best practices

Reference 3

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Observation b8451c96-c61d-463b-9e01-8176690e2ed6 · outbound

This paper cites Quantum Measurement for Quantum Chemistry on a Quantum Computer.

Generative Learning for Quantum Measurement Design Quantum Measurement for Quantum Chemistry on a Quantum Computer

Reference 4

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Observation 2c655757-25cf-415f-aa3d-287bd03bed7a · outbound

This paper cites Learning many-body Hamiltonians with Heisenberg-limited scaling.

Generative Learning for Quantum Measurement Design Learning many-body Hamiltonians with Heisenberg-limited scaling

Reference 5

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Observation 82398365-146f-40ac-8f0d-6f9a457ddecf · outbound

This paper cites Predicting many properties of a quantum system from very few measurements.

Generative Learning for Quantum Measurement Design Predicting many properties of a quantum system from very few measurements

Reference 6

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Observation 8c1e97ba-8627-4347-8adf-dcd5c70417c4 · outbound

This paper cites Theoretical and experimental perspectives of quantum verification.

Generative Learning for Quantum Measurement Design Theoretical and experimental perspectives of quantum verification

Reference 7

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Observation 3f2d2d88-15a3-4301-8746-2e776db27940 · outbound

This paper cites Progress towards practical quantum variational algorithms.

Generative Learning for Quantum Measurement Design Progress towards practical quantum variational algorithms

Reference 8

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Observation 94b96e77-eb6c-4dcf-99fa-b43df5669208 · outbound

This paper cites Measurements as a roadblock to near-term practical quantum advantage in chemistry: Resource analysis.

Generative Learning for Quantum Measurement Design Measurements as a roadblock to near-term practical quantum advantage in chemistry: Resource analysis

Reference 9

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Observation b02321f7-c409-44d0-b9fb-d8a46c5208c1 · outbound

This paper cites Nearly optimal measurement scheduling for partial tomography of quantum states.

Generative Learning for Quantum Measurement Design Nearly optimal measurement scheduling for partial tomography of quantum states

Reference 10

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Observation 87005a41-3196-446b-84fc-424096f8acd5 · outbound

This paper cites Measurements of quantum Hamiltonians with locally-biased classical shadows.

Generative Learning for Quantum Measurement Design Measurements of quantum Hamiltonians with locally-biased classical shadows

Reference 11

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Observation 52507e2c-232d-4f42-b85e-72b5a95bfcbd · outbound

This paper cites Efficient estimation of Pauli observables by derandomization.

Generative Learning for Quantum Measurement Design Efficient estimation of Pauli observables by derandomization

Reference 12

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Observation 42463ee1-3889-4d20-96e6-1039692d0676 · outbound

This paper cites Overlapped grouping measurement: A unified framework for measuring quantum states.

Generative Learning for Quantum Measurement Design Overlapped grouping measurement: A unified framework for measuring quantum states

Reference 13

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Observation 1b83b0ef-9624-4079-9b3b-b13a81186922 · outbound

This paper cites Measurement optimization in the variational quantum eigensolver using a minimum clique cover.

Generative Learning for Quantum Measurement Design Measurement optimization in the variational quantum eigensolver using a minimum clique cover

Reference 14

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Observation 0d5d3092-5611-4296-a486-396a356a9a48 · outbound

This paper cites Unitary partitioning approach to the measurement problem in the variational quantum eigensolver method.

Generative Learning for Quantum Measurement Design Unitary partitioning approach to the measurement problem in the variational quantum eigensolver method

Reference 15

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Observation 743f8f55-37d9-4f39-b1e1-4c5fe1610a3c · outbound

This paper cites Measuring all compatible operators in one series of single-qubit measurements using unitary transformations.

Generative Learning for Quantum Measurement Design Measuring all compatible operators in one series of single-qubit measurements using unitary transformations

Reference 16

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Observation d67e8006-dc7c-4929-b74d-6ac80b641947 · outbound

This paper cites Measurement reduction in variational quantum algorithms.

Generative Learning for Quantum Measurement Design Measurement reduction in variational quantum algorithms

Reference 17

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Observation c1cc8050-571b-4ee7-a619-6233a9cb5397 · outbound

This paper cites Efficient and noise resilient measurements for quantum chemistry on near-term quantum computers.

Generative Learning for Quantum Measurement Design Efficient and noise resilient measurements for quantum chemistry on near-term quantum computers

Reference 18

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Observation 34d7a0fe-4a29-4e2e-9406-8e1c68e14683 · outbound

This paper cites Cartan subalgebra approach to efficient measurements of quantum observables.

Generative Learning for Quantum Measurement Design Cartan subalgebra approach to efficient measurements of quantum observables

Reference 19

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Observation c6424574-cfab-42ca-af82-5fa567109995 · outbound

This paper cites Efficient quantum measurement of Pauli operators in the presence of finite sampling error.

Generative Learning for Quantum Measurement Design Efficient quantum measurement of Pauli operators in the presence of finite sampling error

Reference 20

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Observation 8e4633d5-2a37-4dc1-81e8-42bd4179cf44 · outbound

This paper cites The Heisenberg Representation of Quantum Computers.

Generative Learning for Quantum Measurement Design The Heisenberg Representation of Quantum Computers

Reference 21

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Observation 349c47d6-d72c-4739-9924-d09ad887338d · outbound

This paper cites Improved simulation of stabilizer circuits.

Generative Learning for Quantum Measurement Design Improved simulation of stabilizer circuits

Reference 22

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Observation 56fc3c86-0398-44ad-b759-dbb6e91de064 · outbound

This paper cites Hadamard-free circuits expose the structure of the Clifford group.

Generative Learning for Quantum Measurement Design Hadamard-free circuits expose the structure of the Clifford group

Reference 23

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Observation fc635875-4456-4c6d-874a-0ed6026f4531 · outbound

This paper cites The randomized measurement toolbox.

Generative Learning for Quantum Measurement Design The randomized measurement toolbox

Reference 24

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Observation b8d1ecd3-febd-4a3c-8a84-36794a923d2b · outbound

This paper cites Learning to measure: Adaptive informationally complete generalized measurements for quantum algorithms.

Generative Learning for Quantum Measurement Design Learning to measure: Adaptive informationally complete generalized measurements for quantum algorithms

Reference 25

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Observation dfcd4cee-ea06-4fca-ad3d-6e45f624cd50 · outbound

This paper cites Fermionic partial tomography via classical shadows.

Generative Learning for Quantum Measurement Design Fermionic partial tomography via classical shadows

Reference 26

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Observation 78a4f974-e89b-4692-b941-7a064f90eac6 · outbound

This paper cites Shallow shadows: Expectation estimation using low-depth random Clifford circuits.

Generative Learning for Quantum Measurement Design Shallow shadows: Expectation estimation using low-depth random Clifford circuits

Reference 27

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Observation bd753223-3480-4681-a002-5750987a7098 · outbound

This paper cites Operator relaxation and the optimal depth of classical shadows.

Generative Learning for Quantum Measurement Design Operator relaxation and the optimal depth of classical shadows

Reference 28

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Observation 45611d22-fc83-475f-b666-aaebc150030f · outbound

This paper cites Classical shadow tomography with locally scrambled quantum dynamics.

Generative Learning for Quantum Measurement Design Classical shadow tomography with locally scrambled quantum dynamics

Reference 29

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Observation e68885fa-3e6f-40b2-934e-1fcea78f0a34 · outbound

This paper cites Scalable and flexible classical shadow tomography with tensor networks.

Generative Learning for Quantum Measurement Design Scalable and flexible classical shadow tomography with tensor networks

Reference 30

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Observation 8227ad5f-f1ab-4fd4-8e16-ca7fb4d2a217 · outbound

This paper cites Demonstration of robust and efficient quantum property learning with shallow shadows.

Generative Learning for Quantum Measurement Design Demonstration of robust and efficient quantum property learning with shallow shadows

Reference 31

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Observation b30f1b1e-64d5-4d42-9a5b-0a5fe5c7a3eb · outbound

This paper cites Derandomized shallow shadows: Efficient Pauli learning with bounded-depth circuits.

Generative Learning for Quantum Measurement Design Derandomized shallow shadows: Efficient Pauli learning with bounded-depth circuits

Reference 32

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Observation 013cc913-75a5-4b79-83f1-9024cba724f0 · outbound

This paper cites Flow network based generative models for non-iterative diverse candidate generation.

Generative Learning for Quantum Measurement Design Flow network based generative models for non-iterative diverse candidate generation

Reference 33

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Observation cbe18354-2a52-4805-962e-a0aa05b534ad · outbound

This paper cites Trajectory balance: Improved credit assignment in GFlowNets.

Generative Learning for Quantum Measurement Design Trajectory balance: Improved credit assignment in GFlowNets

Reference 34

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Observation 018c4559-f311-4e61-8262-a61914d8e430 · outbound

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Generative Learning for Quantum Measurement Design GFlowNet foundations

Reference 35

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Observation cb657890-ea27-44bb-9c42-c9fd1e7a1af0 · outbound

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Generative Learning for Quantum Measurement Design Sutton and Andrew G

Reference 36

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Observation 1c5a061a-0bb0-489e-bbf2-e896a2663077 · outbound

This paper cites Biological sequence design with GFlowNets.

Generative Learning for Quantum Measurement Design Biological sequence design with GFlowNets

Reference 37

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Observation 5e0fd4cf-e067-430b-9499-e4e54918b95e · outbound

This paper cites Multi-objective GFlowNets.

Generative Learning for Quantum Measurement Design Multi-objective GFlowNets

Reference 38

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Observation c12b80b7-30a0-42e7-a0eb-3afa32cc729c · outbound

This paper cites Let the flows tell: Solving graph combinatorial problems with GFlowNets.

Generative Learning for Quantum Measurement Design Let the flows tell: Solving graph combinatorial problems with GFlowNets

Reference 39

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source=pdf_text observed=2026-08-15T14:20:18.041497Z digest=sha256:9ee35bf210761d8b9421b366f35a3e67dd1c411cd6b83146dc642f7b2faa8e45

Observation 8ee362c0-684b-477a-823b-9355f92aba87 · outbound

This paper cites Robust Scheduling with GFlowNets.

Generative Learning for Quantum Measurement Design Robust Scheduling with GFlowNets

Reference 40

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-15T14:20:18.046416Z digest=sha256:a2f88ee1f8877277766b7c8761cf5e0f253e8aa16aaf50f7f085b64816852825

Observation 3d868dac-ad89-4bcf-b031-cbe8e14ba0ef · outbound

This paper cites Bayesian structure learning with generative flow networks.

Generative Learning for Quantum Measurement Design Bayesian structure learning with generative flow networks

Reference 41

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T14:20:18.051670Z digest=sha256:9ca0555c0ae624ada900293b75dc11f3ceddf8a1a9995d1e94887cd70b1514de

Observation fb0ef88a-3936-41c3-a077-ae3c05bbe1d6 · outbound

This paper cites Generative flow networks for discrete probabilistic modeling.

Generative Learning for Quantum Measurement Design Generative flow networks for discrete probabilistic modeling

Reference 42

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raw_fallback, observed 2026-08-15T14:20:19.097982Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T14:20:18.056270Z digest=sha256:7dc61924d5a386da94a502d55ce745a77e51fced34440616bcd963f6b8171066

Observation 70b2fbac-3c03-4a62-a3a2-2e44faa57d94 · outbound

This paper cites GFlowNets for Hamiltonian decomposition in groups of compatible operators.

Generative Learning for Quantum Measurement Design GFlowNets for Hamiltonian decomposition in groups of compatible operators

Reference 43

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-15T14:20:18.060773Z digest=sha256:4fd87bc35d82daa57c24709435e05f4136a2869dc33bdce214f29dd92cbed650

Observation 0a2fe433-3aba-438d-8a68-983ad1e2d851 · outbound

This paper cites 2025.doi: 10.

Generative Learning for Quantum Measurement Design 2025.doi: 10

Reference 44

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T14:20:18.066372Z digest=sha256:f4c6ca8b9f9b56d20e7c89f8858b8508409f37bdcaa88790505ef97b9f4132f6

Observation 469b8f5c-42aa-4e6c-b646-d3982af58a0c · outbound

This paper cites ¨Uber das Paulische ¨Aquivalenzverbot.

Generative Learning for Quantum Measurement Design ¨Uber das Paulische ¨Aquivalenzverbot

Reference 45

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T14:20:18.070648Z digest=sha256:db7b4e34cb6e57bf788d350faa9acd5b09a04fbe620eecc9c38489c38bc71327

Observation ea3faec8-e1f7-48e3-b851-1352f7b8addb · outbound

This paper cites https://github.

Generative Learning for Quantum Measurement Design https://github

Reference 46

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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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T14:20:18.075856Z digest=sha256:afa175fa42a6f0cc86146e588033fc12f43e896c485a5f3e6731f63d695437d1

Observation c035da63-c469-4fd4-836a-0f683fc598a6 · outbound

This paper cites Compact fermion to qubit mappings.

Generative Learning for Quantum Measurement Design Compact fermion to qubit mappings

Reference 47

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T14:20:18.080322Z digest=sha256:3982e49a6436580d5f33874436cab99b4dbf864b492306294c5ca0cdbac2d8ca

Observation 35bcde4e-3a6b-4275-8f27-e7a3dbab00a3 · outbound

This paper cites Scalable simulation of fermionic encoding performance on noisy quantum computers.

Generative Learning for Quantum Measurement Design Scalable simulation of fermionic encoding performance on noisy quantum computers

Reference 48

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T14:20:18.086058Z digest=sha256:7e1da7729617b79c49d2b4c1b5c3dc8b8a9f678fde58a957719cb5b86fcd3a43

Observation 40c29cc9-d26b-4963-ba08-c911ffb7ae8e · outbound

This paper cites Density matrix formulation for quantum renormalization groups.

Generative Learning for Quantum Measurement Design Density matrix formulation for quantum renormalization groups

Reference 49

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T14:20:18.090756Z digest=sha256:5a212fbc9f493b2b3b7a119299d06f2913574a44bfc331f6649376e5f2570af8

Observation 955f136b-1fb6-4e24-8e32-400fdfb95b20 · outbound

This paper cites The density-matrix renormalization group in the age of matrix product states.

Generative Learning for Quantum Measurement Design The density-matrix renormalization group in the age of matrix product states

Reference 50

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T14:20:18.096412Z digest=sha256:856e0c792db8464db01125c31f3b216e6a5575a4ab9a6b8f664b1ad57ab83c08

Observation f1219343-744e-4139-9caf-8093ae5c1785 · outbound

This paper cites On the two different aspects of the representative method: The method of stratified sampling and the method of purposive selection.

Generative Learning for Quantum Measurement Design On the two different aspects of the representative method: The method of stratified sampling and the method of purposive selection

Reference 51

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T14:20:18.101130Z digest=sha256:a518ea6271078734e21cfa30272c6f59b9cc081f70fa3595209377ef62b46c2d

Observation 35fc58b6-c38a-445e-af22-d5454e36fef4 · outbound

This paper cites Improving quantum measurements by introducing “ghost.

Generative Learning for Quantum Measurement Design Improving quantum measurements by introducing “ghost

Reference 52

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T14:20:18.105995Z digest=sha256:c2bb551dc5d005a234668a4bc2f41f0b6b9d82691318d9373eafdb3822e0ec34

Observation 11937246-f17d-4298-8269-ecaf51953216 · outbound

This paper cites Deterministic improvements of quantum measurements with grouping of compatible operators, non-local transformations, and covariance estimates.

Generative Learning for Quantum Measurement Design Deterministic improvements of quantum measurements with grouping of compatible operators, non-local transformations, and covariance estimates

Reference 53

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no resolver link, observed 2026-08-15T14:20:18.110613Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T14:20:18.110613Z digest=sha256:e4cb6223461e78e8f09838bc9b98aca30dd1be96fd73587a1d26c00071dfc86a

Observation b0efe369-612c-499b-a30d-a73ac5f65643 · outbound

This paper cites an unresolved cited work.

Generative Learning for Quantum Measurement Design Unresolved cited work

Reference 54

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T14:20:18.115109Z digest=sha256:51a14d8045abce029803644db3cb866f0bca02a0423b784b7ab214291055254f

Observation 70e4ef3f-c9c2-4ac4-913f-978a58ec402b · outbound

This paper cites Learning GFlowNets from partial episodes for improved convergence and stability.

Generative Learning for Quantum Measurement Design Learning GFlowNets from partial episodes for improved convergence and stability

Reference 55

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raw_fallback, observed 2026-08-15T14:20:19.051919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T14:20:18.124204Z digest=sha256:df0419087256bcdf02e9a4a76dc20c6222aedd44bdf619799e6cec8a6d4a6cba

Observation 3ed79d3e-d7a1-4331-8578-ba9d950ebe83 · outbound

This paper cites PyTorch: An imperative style, high-performance deep learning library.

Generative Learning for Quantum Measurement Design PyTorch: An imperative style, high-performance deep learning library

Reference 56

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raw_fallback, observed 2026-08-15T14:20:19.036517Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T14:20:18.128673Z digest=sha256:8acdac52336a000735febc2bf7b4798bf33b5f31a347b64f123e605a7164707c

Observation c83ed003-69c3-489f-8525-bd550b9a8874 · outbound

This paper cites CuPy: A NumPy-compatible library for NVIDIA GPU calculations.

Generative Learning for Quantum Measurement Design CuPy: A NumPy-compatible library for NVIDIA GPU calculations

Reference 57

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raw_fallback, observed 2026-08-15T14:20:19.021299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T14:20:18.133146Z digest=sha256:0e4febbeefc253377361732f845a9c76a251df9200190c1249d5195e8e3b8699

Observation ace2da30-ff9f-40e9-860c-ac6ca628b223 · outbound

This paper cites an unresolved cited work.

Generative Learning for Quantum Measurement Design Unresolved cited work

Reference 2025

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T14:20:18.119839Z digest=sha256:4ec8505b9707d36acee2697ad58052fef1925e1c57de335a1dcefc9127a5b1ae

Pith citing papers

No inbound Pith citation observations are available.