A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing | Paper Citation Record

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This paper cites Learning a local Hamiltonian from local measure- ments.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Learning a local Hamiltonian from local measure- ments

Reference 1

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This paper cites Learning the dynamics of open quantum systems from their steady states.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Learning the dynamics of open quantum systems from their steady states

Reference 2

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This paper cites Quantum process tomography of unitary and near-unitary Maps.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Quantum process tomography of unitary and near-unitary Maps

Reference 3

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This paper cites On the complexity of approximating the diamond norm.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing On the complexity of approximating the diamond norm

Reference 4

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This paper cites Iterative hard thresholding for compressed sensing.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Iterative hard thresholding for compressed sensing

Reference 5

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This paper cites Normalized iterative hard thresholding: Guaranteed stability and performance.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Normalized iterative hard thresholding: Guaranteed stability and performance

Reference 6

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This paper cites Parameter estimation of ODE’s via nonparametric estimators.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Parameter estimation of ODE’s via nonparametric estimators

Reference 7

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This paper cites A tracking approach to parameter estimation in linear ordinary differential equations.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing A tracking approach to parameter estimation in linear ordinary differential equations

Reference 8

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This paper cites Random quantum operations.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Random quantum operations

Reference 9

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This paper cites A singular value thresholding algorithm for matrix completion.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing A singular value thresholding algorithm for matrix completion

Reference 10

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This paper cites The power of convex relaxation: Near-optimal matrix comple- tion.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing The power of convex relaxation: Near-optimal matrix comple- tion

Reference 11

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This paper cites Exact matrix completion via convex optimization.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Exact matrix completion via convex optimization

Reference 12

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This paper cites Stable signal recovery from incomplete and inaccurate measurements.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Stable signal recovery from incomplete and inaccurate measurements

Reference 13

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This paper cites Learning Quantum Hamiltonians from Single-Qubit Measurements.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Learning Quantum Hamiltonians from Single-Qubit Measurements

Reference 14

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This paper cites Completely positive linear maps on complex matrices.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Completely positive linear maps on complex matrices

Reference 15

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This paper cites Prescription for experimental determination of the dynamics of a quantum black box.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Prescription for experimental determination of the dynamics of a quantum black box

Reference 16

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This paper cites Iteratively reweighted least squares minimization for sparse recovery.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Iteratively reweighted least squares minimization for sparse recovery

Reference 17

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This paper cites Davenport, Marco F.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Davenport, Marco F

Reference 18

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Observation 8ee8c8de-9dd5-4e3b-8505-a21a5f3f105f · outbound

This paper cites An overview of low-rank matrix recovery from incomplete observations.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing An overview of low-rank matrix recovery from incomplete observations

Reference 19

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This paper cites The rotation of eigenvectors by a perturbation. III.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing The rotation of eigenvectors by a perturbation. III

Reference 20

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This paper cites On the eigenvalues of random matrices.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing On the eigenvalues of random matrices

Reference 21

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Observation c3f96db2-5d33-4b0e-88da-81d65f5f1ac3 · outbound

This paper cites Compressed-sensing Lindbladian quantum tomography with trapped ions.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Compressed-sensing Lindbladian quantum tomography with trapped ions

Reference 22

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This paper cites Compressed sensing.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Compressed sensing

Reference 23

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This paper cites Quantum tomography via compressed sensing: Error bounds, sample complexity and efficient estimators.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Quantum tomography via compressed sensing: Error bounds, sample complexity and efficient estimators

Reference 24

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This paper cites No spurious local minima in nonconvex low rank problems: A unified geometric analysis.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing No spurious local minima in nonconvex low rank problems: A unified geometric analysis

Reference 25

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This paper cites Distance measures to compare real and ideal quantum processes.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Distance measures to compare real and ideal quantum processes

Reference 26

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Observation d4602339-cf66-4aa8-ab4a-7d8e70ef8b65 · outbound

This paper cites Completely positive dynamical semigroups of N-level systems.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Completely positive dynamical semigroups of N-level systems

Reference 27

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Observation 8d5659a6-56cb-4240-886c-ffd947dbd7f2 · outbound

This paper cites Robust online Hamil- tonian learning.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Robust online Hamil- tonian learning

Reference 28

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This paper cites Recovering low-rank matrices from few coefficients in any basis.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Recovering low-rank matrices from few coefficients in any basis

Reference 29

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Observation d81fbf5a-c574-4573-bc12-6a7c7a76a774 · outbound

This paper cites Quantum state to- mography via compressed sensing.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Quantum state to- mography via compressed sensing

Reference 30

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Observation 1dbaae21-b9b1-4858-891d-efdd63a6f637 · outbound

This paper cites Practical Black Box Hamiltonian Learning.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Practical Black Box Hamiltonian Learning

Reference 31

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This paper cites On a combination of alternating minimization and Nesterov’s momentum.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing On a combination of alternating minimization and Nesterov’s momentum

Reference 32

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Observation de19ded3-f483-419b-9ccf-4d9af5ac9b2d · outbound

This paper cites Finding structure with randomness: Prob- abilistic algorithms for constructing approximate matrix decompositions.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Finding structure with randomness: Prob- abilistic algorithms for constructing approximate matrix decompositions

Reference 33

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This paper cites Forcing function diagnostics for nonlinear dynamics.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Forcing function diagnostics for nonlinear dynamics

Reference 34

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This paper cites Quantum process tomography and Lindblad estimation of a solid-state qubit.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Quantum process tomography and Lindblad estimation of a solid-state qubit

Reference 35

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This paper cites Guaranteed rank minimization via singular value projection.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Guaranteed rank minimization via singular value projection

Reference 36

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This paper cites Low-rank matrix completion using alternating minimization.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Low-rank matrix completion using alternating minimization

Reference 37

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This paper cites QuTiP: An open-source Python framework for the dynamics of open quantum systems.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing QuTiP: An open-source Python framework for the dynamics of open quantum systems

Reference 38

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This paper cites Algorithms for nonnegative matrix and tensor factoriza- tions: A unified view based on block coordinate descent framework.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Algorithms for nonnegative matrix and tensor factoriza- tions: A unified view based on block coordinate descent framework

Reference 39

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Observation 062173dc-99a7-4a82-b50e-ba4cb5bb20ff · outbound

This paper cites Phase retrieval using unitary 2-designs.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Phase retrieval using unitary 2-designs

Reference 40

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Observation a07c77f1-c24c-40b8-aac3-891e4f0a9e31 · outbound

This paper cites Guaranteed recovery of quantum pro- cesses from few measurements.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Guaranteed recovery of quantum pro- cesses from few measurements

Reference 41

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Observation 3b439498-065c-42ea-a4e2-c70d9ec0c84e · outbound

This paper cites Principal component analysis of three-mode data by means of alternating least squares algorithms.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Principal component analysis of three-mode data by means of alternating least squares algorithms

Reference 42

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Observation cd5e751c-089c-4c04-bcfe-f8cc63eba04a · outbound

This paper cites Low rank matrix recovery from rank one mea- surements.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Low rank matrix recovery from rank one mea- surements

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:35:24.646063Z

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This paper cites Interacting particle systems on networks: Joint inference of the network and the interaction kernel.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Interacting particle systems on networks: Joint inference of the network and the interaction kernel

Reference 44

Resolution: verified exact; raw_fallback, observed 2026-08-10T15:35:24.116228Z

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Observation c83e047e-dfd7-4d97-96bd-22582d4e2598 · outbound

This paper cites Randomly initialized alternating least squares: Fast convergence for matrix sensing.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Randomly initialized alternating least squares: Fast convergence for matrix sensing

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:35:24.631434Z

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Observation 3b2a99fb-c0d6-4609-b1c2-94f9e51f021f · outbound

This paper cites Parameter estimation of ordinary differential equations.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Parameter estimation of ordinary differential equations

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:35:24.617136Z

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Observation 392833df-6466-413b-9a70-ea0c888f8176 · outbound

This paper cites Hamiltonian tomography via quantum quench.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Hamiltonian tomography via quantum quench

Reference 47

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Observation 24080da1-443c-4646-85ae-33afee412be3 · outbound

This paper cites Adaptive geometric multiscale approximations for intrinsically low-dimensional data.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Adaptive geometric multiscale approximations for intrinsically low-dimensional data

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:35:24.587713Z

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Observation 553c4529-e538-42f8-b4f8-9265dc6082e4 · outbound

This paper cites On the generators of quantum dynamical semigroups.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing On the generators of quantum dynamical semigroups

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:35:24.573593Z

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Observation 7e992964-ca26-4527-be40-59fdcde230fe · outbound

This paper cites A unified approach to synchronization problems over subgroups of the orthogonal group.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing A unified approach to synchronization problems over subgroups of the orthogonal group

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:35:24.559575Z

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Observation fb191804-1030-4434-9e0a-55018cfc9103 · outbound

This paper cites Universal low-rank matrix recovery from Pauli measurements.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Universal low-rank matrix recovery from Pauli measurements

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:35:24.545735Z

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Observation 13c0d604-35d2-4f62-8bac-d10b16b2d891 · outbound

This paper cites Learning $k$-body Hamiltonians via compressed sensing.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Learning $k$-body Hamiltonians via compressed sensing

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-10T15:35:23.760091Z

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Observation fe6f1ee0-8bd5-4704-9299-6dc4c4dfab78 · outbound

This paper cites Dynamic anomalography: Tracking network anomalies via sparsity and low rank.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Dynamic anomalography: Tracking network anomalies via sparsity and low rank

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:35:24.530698Z

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Observation ee99017d-29a9-4f97-9a55-79b6f263c4f9 · outbound

This paper cites A randomized algorithm for the decom- position of matrices.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing A randomized algorithm for the decom- position of matrices

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:35:24.516183Z

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Observation 607ad270-3dd9-471b-a5e0-509ddb0a20b5 · outbound

This paper cites Nesterov acceleration of alternating least squares for canonical tensor decomposition: Momentum step size selection and restart mechanisms.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Nesterov acceleration of alternating least squares for canonical tensor decomposition: Momentum step size selection and restart mechanisms

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:35:24.502085Z

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Observation 44c52547-b1b5-4473-a1c6-f6709c16c93b · outbound

This paper cites Quantum-process tomography: Resource analysis of different strategies.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Quantum-process tomography: Resource analysis of different strategies

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:35:24.487124Z

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Observation 58716274-1594-40dc-9019-0cc24a72d60a · outbound

This paper cites Self-adjoint elements in the pseudo-unitary group U (p, p).

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Self-adjoint elements in the pseudo-unitary group U (p, p)

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:35:24.472810Z

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Observation d1e13b16-1efe-48ce-b583-bfb739317404 · outbound

This paper cites Joint covariate selection and joint subspace selection for multiple classification problems.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Joint covariate selection and joint subspace selection for multiple classification problems

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:35:24.457960Z

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Observation ff5470c2-54ec-441a-b033-ef26d793981f · outbound

This paper cites Fitting quantum noise models to tomography data.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Fitting quantum noise models to tomography data

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:35:24.443416Z

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Observation 7d5af245-7dfd-44ac-a678-2cd0c6a0c248 · outbound

This paper cites Non-square matrix sensing without spurious local minima via the Burer-Monteiro approach.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Non-square matrix sensing without spurious local minima via the Burer-Monteiro approach

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:35:24.427976Z

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Observation 1262c316-a628-41a2-83bb-19eac2cad4be · outbound

This paper cites Block Acceleration Without Momentum: On Optimal Stepsizes of Block Gradient Descent for Least-Squares.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Block Acceleration Without Momentum: On Optimal Stepsizes of Block Gradient Descent for Least-Squares

Reference 61

Resolution: verified exact; local_arxiv, observed 2026-08-10T15:35:24.008188Z

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Observation dd781420-49bc-44d6-974c-bb4b65ca088f · outbound

This paper cites Guaranteed minimum-rank solutions linear matrix equations via nuclear norm minimization.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Guaranteed minimum-rank solutions linear matrix equations via nuclear norm minimization

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:35:24.412612Z

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Observation 28add1c3-564a-4ba7-b1df-5247e1a04f92 · outbound

This paper cites Compressed sensing quantum process tomography for superconducting quantum gates.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Compressed sensing quantum process tomography for superconducting quantum gates

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:35:24.395326Z

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Observation 77f06d74-dd07-4490-b049-e8155de5414f · outbound

This paper cites Lindblad tomography of a superconducting quantum processor.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Lindblad tomography of a superconducting quantum processor

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:35:24.373485Z

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Observation 18691a38-df5b-44f8-baee-c5fc28e8af83 · outbound

This paper cites Efficient measurement of quantum dynamics via compressive sensing.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Efficient measurement of quantum dynamics via compressive sensing

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:35:24.353511Z

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Observation 5de2582d-db0d-4c5b-a2a8-705f02430bf8 · outbound

This paper cites da Silva.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing da Silva

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:35:24.336179Z

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Observation 29860eb6-3239-4bd5-a147-d5c9d6ee9c27 · outbound

This paper cites Angular synchronization by eigenvectors and semidefinite programming.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Angular synchronization by eigenvectors and semidefinite programming

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:35:24.321042Z

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Observation 06531cb6-c65c-4b0d-a7f2-64021ecaedf1 · outbound

This paper cites Efficient and robust estimation of many-qubit Hamiltonians.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Efficient and robust estimation of many-qubit Hamiltonians

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:35:24.305975Z

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Observation df64b6e8-3953-4e25-9785-da8737ea85ed · outbound

This paper cites Convergence of a block coordinate descent method for nondifferentiable minimization.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Convergence of a block coordinate descent method for nondifferentiable minimization

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:35:24.292168Z

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Observation d3f53b3a-5609-4448-9ef2-951a096561e3 · outbound

This paper cites Approximation with Kronecker products.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Approximation with Kronecker products

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:35:24.278352Z

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Observation 575c3b07-ec88-4700-9cda-0a300ab3c8fc · outbound

This paper cites Simulation-assisted learning of open quantum systems.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Simulation-assisted learning of open quantum systems

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:35:24.263828Z

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Observation fb3f3161-a765-4b34-80df-b0224d4d60aa · outbound

This paper cites Exact and stable recovery of rotations for robust synchronization.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Exact and stable recovery of rotations for robust synchronization

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:35:24.246724Z

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Observation d6630e7e-0f39-48ad-97d3-9cb8b9a15b9b · outbound

This paper cites Semidefinite programs for completely bounded norms.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Semidefinite programs for completely bounded norms

Reference 73

Resolution: verified exact; local_arxiv, observed 2026-08-10T15:35:23.986470Z

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Observation f0ce24da-1ffc-4bca-b1f6-0be10c9a14ce · outbound

This paper cites Simpler semidefinite programs for completely bounded norms.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Simpler semidefinite programs for completely bounded norms

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-10T15:35:23.855464Z

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Observation 183261a8-c184-4f79-92b3-b279eb6a53c0 · outbound

This paper cites The power of preconditioning in overparameterized low-rank matrix sensing.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing The power of preconditioning in overparameterized low-rank matrix sensing

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:35:24.229532Z

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Observation bee2ab61-efbd-42ef-8aac-a56bfb1050b1 · outbound

This paper cites A block coordinate descent method for regularized multiconvex opti- mization with applications to nonnegative tensor factorization and completion.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing A block coordinate descent method for regularized multiconvex opti- mization with applications to nonnegative tensor factorization and completion

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:35:24.214179Z

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Observation bca1f18e-8f4f-457a-86f7-612ee3e4fe22 · outbound

This paper cites On global convergence of alternating least squares for tensor approximation.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing On global convergence of alternating least squares for tensor approximation

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:35:24.197045Z

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Observation 381394a4-011c-43e0-b36e-d93fea9aa884 · outbound

This paper cites Robust and Efficient Hamiltonian Learning.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Robust and Efficient Hamiltonian Learning

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-10T15:35:23.873045Z

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Observation ad80b9f5-f55d-443d-a85d-2aada37cce04 · outbound

This paper cites Convergence Analysis for Rectangular Matrix Completion Using Burer-Monteiro Factorization and Gradient Descent.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Convergence Analysis for Rectangular Matrix Completion Using Burer-Monteiro Factorization and Gradient Descent

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-10T15:35:23.877579Z

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Observation 2e8c038f-178a-45bb-8bfc-fd3b77b9b76a · outbound

This paper cites Efficient matrix sensing using rank-1 Gaussian mea- surements.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Efficient matrix sensing using rank-1 Gaussian mea- surements

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T15:35:24.181232Z

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Observation 71b67cdc-3837-4be1-82af-67d8b26f6afb · outbound

This paper cites Optimal short-time measurements for Hamiltonian learning.

A Unified Blockwise Measurement Design for Learning Quantum Channels and Lindbladians via Low-Rank Matrix Sensing Optimal short-time measurements for Hamiltonian learning

Reference 81

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