Generative Learning for Quantum Measurement Design | Paper Citation Record

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

Resolution: verified exact; doi, observed 2026-08-15T14:20:18.568399Z

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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

Resolution: verified exact; doi, observed 2026-08-15T14:20:18.553649Z

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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

Resolution: verified exact; doi, observed 2026-08-15T14:20:18.538859Z

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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

Resolution: malformed identifier; no resolver link, observed 2026-08-15T14:20:17.931529Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-15T14:20:17.962193Z

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:20:19.230023Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-15T14:20:17.989502Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-15T14:20:17.994406Z

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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

Resolution: malformed identifier; no resolver link, observed 2026-08-15T14:20:18.003117Z

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:20:19.199717Z

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

This paper cites GFlowNet foundations.

Generative Learning for Quantum Measurement Design GFlowNet foundations

Reference 35

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This paper cites Sutton and Andrew G.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:20:19.127785Z

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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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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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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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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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This paper cites 2025.doi: 10.

Generative Learning for Quantum Measurement Design 2025.doi: 10

Reference 44

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

This paper cites https://github.

Generative Learning for Quantum Measurement Design https://github

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:20:19.081920Z

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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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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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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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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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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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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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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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Generative Learning for Quantum Measurement Design Unresolved cited work

Reference 54

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:20:19.051919Z

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:20:19.036517Z

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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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:20:19.021299Z

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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

Resolution: verified exact; raw_fallback, observed 2026-08-15T14:20:18.724897Z

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