Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier | Paper Citation Record

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This paper cites an unresolved cited work.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Unresolved cited work

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This paper cites P., Rancurel, P.,Iterative perturbation calculations of ground and excited state energies (CIPSI),J.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier P., Rancurel, P.,Iterative perturbation calculations of ground and excited state energies (CIPSI),J

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This paper cites Heat-bath Configuration Interaction: An efficient selected CI algorithm inspired by heat-bath sampling.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Heat-bath Configuration Interaction: An efficient selected CI algorithm inspired by heat-bath sampling

Reference 3

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This paper cites Semistochastic Heat-bath Configuration Interaction method: selected configuration interaction with semistochastic perturbation theory.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Semistochastic Heat-bath Configuration Interaction method: selected configuration interaction with semistochastic perturbation theory

Reference 4

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This paper cites M., et al., Head-Gordon, M., Whaley, K.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier M., et al., Head-Gordon, M., Whaley, K

Reference 5

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This paper cites L.,A variational eigenvalue solver on a photonic quantum processor (VQE),Nat.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier L.,A variational eigenvalue solver on a photonic quantum processor (VQE),Nat

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Observation 6571f23e-d171-4399-9bba-0fa5eea0c325 · outbound

This paper cites R., Boixo, S., Smelyanskiy, V.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier R., Boixo, S., Smelyanskiy, V

Reference 7

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Observation 20400d69-bf14-4a39-bc59-5960fc0cf9ee · outbound

This paper cites Barren Plateaus in Variational Quantum Computing.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Barren Plateaus in Variational Quantum Computing

Reference 8

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Observation d047c16b-76fd-4f73-82db-adb5836f8fbf · outbound

This paper cites Does provable absence of barren plateaus imply classical simulability?.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Does provable absence of barren plateaus imply classical simulability?

Reference 9

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Observation 9a62cd36-44ef-454c-8033-3f42d7f4a0ce · outbound

This paper cites Quantum-Selected Configuration Interaction: classical diagonalization of Hamiltonians in subspaces selected by quantum computers.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Quantum-Selected Configuration Interaction: classical diagonalization of Hamiltonians in subspaces selected by quantum computers

Reference 10

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Observation 2fc7af2c-2e96-4450-9e43-644f9149af64 · outbound

This paper cites Chemistry Beyond the Scale of Exact Diagonalization on a Quantum-Centric Supercomputer.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Chemistry Beyond the Scale of Exact Diagonalization on a Quantum-Centric Supercomputer

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Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Unresolved cited work

Reference 12

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Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Unresolved cited work

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Observation ce1b63c5-a13c-4242-be8f-bece0865bea6 · outbound

This paper cites Critical Limitations in Quantum-Selected Configuration Interaction Methods.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Critical Limitations in Quantum-Selected Configuration Interaction Methods

Reference 14

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Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Unresolved cited work

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This paper cites Machine-Learned Compact Subspace Generation for Quantum Selected Configuration Interaction within Density Matrix Embedding Framework.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Machine-Learned Compact Subspace Generation for Quantum Selected Configuration Interaction within Density Matrix Embedding Framework

Reference 16

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This paper cites An Iterative Dual-Channel Neural Quantum State Algorithm for Selected Configuration Interaction.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier An Iterative Dual-Channel Neural Quantum State Algorithm for Selected Configuration Interaction

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Observation 8932ec5d-f217-4bee-a969-0bb810a74edc · outbound

This paper cites J., Ding, L., Reiher, M.,Neural quantum states based on selected configurations (NQS-SC),J.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier J., Ding, L., Reiher, M.,Neural quantum states based on selected configurations (NQS-SC),J

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Observation 7a2bc33d-4478-4994-bbc9-58f305013727 · outbound

This paper cites SC’25 (2025).

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier SC’25 (2025)

Reference 19

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Observation be690ed8-f98f-4348-acc6-6b17cfd2ef87 · outbound

This paper cites Enhancing quantum-classical configuration interaction methods using a neural-network classifier.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Enhancing quantum-classical configuration interaction methods using a neural-network classifier

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-08-08T15:34:01.643792Z

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Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Unresolved cited work

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Observation cbadaaed-e6cb-4fe2-9155-d7c071768b6f · outbound

This paper cites Learning to Rank for Selected Configuration Interaction.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Learning to Rank for Selected Configuration Interaction

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-08-08T15:34:01.622292Z

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Observation 9d449093-d4ba-4ce3-a9b1-529f26a4a97c · outbound

This paper cites Generative Circuit Design for Quantum-Selected Configuration Interaction.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Generative Circuit Design for Quantum-Selected Configuration Interaction

Reference 23

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Observation 8dd36242-2314-44ce-ab3e-9f57bb5c9bd2 · outbound

This paper cites A Critical Assessment of the Sample-Based Quantum Diagonalization for Heisenberg and Hubbard Models.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier A Critical Assessment of the Sample-Based Quantum Diagonalization for Heisenberg and Hubbard Models

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Resolution: verified exact; local_arxiv, observed 2026-08-08T15:34:01.586497Z

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Observation 542f0912-85a3-414d-a0d4-e62f9cb92b2a · outbound

This paper cites Noise and Configuration Recovery Impact on Quantum Selected Configuration Interaction.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Noise and Configuration Recovery Impact on Quantum Selected Configuration Interaction

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-08-08T15:34:01.565849Z

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This paper cites Efficient classical simulation of large-scale unitary cluster Jastrow circuits.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Efficient classical simulation of large-scale unitary cluster Jastrow circuits

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Observation 37d22013-0094-4bca-b0fa-6d415304e6bb · outbound

This paper cites Hardness of classically sampling quantum chemistry circuits.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Hardness of classically sampling quantum chemistry circuits

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This paper cites Observation of Improved Accuracy over Classical Sparse Ground-State Solvers using a Quantum Computer.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Observation of Improved Accuracy over Classical Sparse Ground-State Solvers using a Quantum Computer

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Resolution: verified exact; local_arxiv, observed 2026-08-08T15:34:01.508284Z

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Observation a7da5e8b-bd5e-42b6-80c1-13beaf55654e · outbound

This paper cites J., Whaley, K.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier J., Whaley, K

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This paper cites ADAPT-QSCI: Adaptive Construction of an Input State for Quantum-Selected Configuration Interaction.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier ADAPT-QSCI: Adaptive Construction of an Input State for Quantum-Selected Configuration Interaction

Reference 30

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This paper cites O.,Quantum-selected configuration interaction with a time-evolved state (TE-QSCI), Phys.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier O.,Quantum-selected configuration interaction with a time-evolved state (TE-QSCI), Phys

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Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Unresolved cited work

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Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Unresolved cited work

Reference 33

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Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Unresolved cited work

Reference 34

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This paper cites Sample-Based Quantum Diagonalization with Amplitude Amplification.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Sample-Based Quantum Diagonalization with Amplitude Amplification

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This paper cites Active Sampling Sample-based Quantum Diagonalization from Finite-Shot Measurements.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Active Sampling Sample-based Quantum Diagonalization from Finite-Shot Measurements

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Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Unresolved cited work

Reference 37

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Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Unresolved cited work

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This paper cites Quantum-centric simulation of hydrogen abstraction by sample-based quantum diagonalization and entanglement forging.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Quantum-centric simulation of hydrogen abstraction by sample-based quantum diagonalization and entanglement forging

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Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Unresolved cited work

Reference 40

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This paper cites Resource-efficient Quantum Algorithms for Selected Hamiltonian Subspace Diagonalization.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Resource-efficient Quantum Algorithms for Selected Hamiltonian Subspace Diagonalization

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Observation a05e69bf-8a34-42a1-bfb4-2715092ba168 · outbound

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Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Unresolved cited work

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Observation 8a66ceb3-624d-48fd-b0bd-37e8fe3f9b64 · outbound

This paper cites Enhancing the accuracy and efficiency of sample-based quantum diagonalization with phaseless auxiliary-field quantum Monte Carlo.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Enhancing the accuracy and efficiency of sample-based quantum diagonalization with phaseless auxiliary-field quantum Monte Carlo

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Observation 86c53e38-13a0-4c21-9497-18d643f18b51 · outbound

This paper cites Coupled cluster method tailored by quantum selected configuration interaction.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Coupled cluster method tailored by quantum selected configuration interaction

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Observation fa3c3700-8554-4bca-8069-6ae74122b911 · outbound

This paper cites Quantum-centric computation of molecular excited states with extended sample-based quantum diagonalization.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Quantum-centric computation of molecular excited states with extended sample-based quantum diagonalization

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This paper cites Towards Compact Wavefunctions from Quantum-Selected Configuration Interaction.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Towards Compact Wavefunctions from Quantum-Selected Configuration Interaction

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Observation a52fe023-6ef4-45c9-ad74-89d6155adb06 · outbound

This paper cites Symmetry-adapted sample-based quantum diagonalization: Application to lattice model.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Symmetry-adapted sample-based quantum diagonalization: Application to lattice model

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Observation f43afdbf-95d4-4255-a5cc-18c232110efc · outbound

This paper cites Predicting Many Properties of a Quantum System from Very Few Measurements.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Predicting Many Properties of a Quantum System from Very Few Measurements

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Observation f3e18b85-d6d5-46b0-b1e3-6dbfe5941d40 · outbound

This paper cites Hardware Robustness of Sample-Based Quantum Diagonalization.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Hardware Robustness of Sample-Based Quantum Diagonalization

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Resolution: verified exact; local_arxiv, observed 2026-08-08T15:34:00.178820Z

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Observation 1d9063bd-4f04-419b-a05b-ec48bd53cdb2 · outbound

This paper cites Machine Learning Configuration Interaction.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Machine Learning Configuration Interaction

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Resolution: verified exact; local_arxiv, observed 2026-08-08T15:34:00.157493Z

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Observation 48117f93-de3a-4976-b701-a65a183c8d8a · outbound

This paper cites P.,Machine-learning configuration interaction for excited states and potential-energy curves,J.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier P.,Machine-learning configuration interaction for excited states and potential-energy curves,J

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Observation 8639eda6-7ae6-4fff-ae57-27950e64f82a · outbound

This paper cites A Fully GPU-Accelerated Framework for High-Performance Configuration Interaction Selection with Neural Network Quantum States.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier A Fully GPU-Accelerated Framework for High-Performance Configuration Interaction Selection with Neural Network Quantum States

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Observation 55ba2a98-4303-46cd-b918-d2b4f3e7055f · outbound

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Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Unresolved cited work

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Observation 816dea3e-9406-4b93-9f8d-981a917df01d · outbound

This paper cites J., Hu, H., Yang, C., Li, X.,Reinforcement learning configuration interaction (RL-CI),J.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier J., Hu, H., Yang, C., Li, X.,Reinforcement learning configuration interaction (RL-CI),J

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Observation 49ff7526-9679-4407-be39-f46c52b4170c · outbound

This paper cites Transformer refined quantum sampling for strongly correlated electronic structure.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Transformer refined quantum sampling for strongly correlated electronic structure

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Resolution: verified exact; local_arxiv, observed 2026-08-08T15:33:59.815319Z

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Observation 32033cdb-b39a-4e9c-8466-40fbd4dfd0fd · outbound

This paper cites Solving the Schr\"odinger Equation in the Configuration Space with Generative Machine Learning.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Solving the Schr\"odinger Equation in the Configuration Space with Generative Machine Learning

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Resolution: verified exact; local_arxiv, observed 2026-08-08T15:33:59.795674Z

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.

Observation f4248082-3822-4035-a52b-08c9f9aedcb6 · outbound

This paper cites Configuration Interaction Guided Sampling with Interpretable Restricted Boltzmann Machine.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Configuration Interaction Guided Sampling with Interpretable Restricted Boltzmann Machine

Reference 57

Resolution: malformed identifier; local_arxiv, observed 2026-08-08T15:33:59.774101Z

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.

Observation a762323c-edb7-498f-8a72-1b55245d8f94 · outbound

This paper cites an unresolved cited work.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Unresolved cited work

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-08T15:33:58.181673Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e25ddce2-b3f9-47de-8be5-85c01e3e6783 · outbound

This paper cites an unresolved cited work.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Unresolved cited work

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-08T15:33:58.185913Z

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Unavailable: canonical work link unavailable.

Observation 20ff22d9-7bfb-4ca5-95a6-8a40124b90cb · outbound

This paper cites A Neural-Network-Based Selective Configuration Interaction Approach to Molecular Electronic Structure.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier A Neural-Network-Based Selective Configuration Interaction Approach to Molecular Electronic Structure

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Resolution: unresolved; no resolver link, observed 2026-08-08T15:33:58.190187Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dd3df44c-7090-40c2-a9c6-d9d2500118cc · outbound

This paper cites an unresolved cited work.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Unresolved cited work

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-08T15:33:58.194715Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 95334be4-9206-4d81-80e0-168a0c438259 · outbound

This paper cites Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-08T15:33:58.198605Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 688ea1a8-cb45-4515-9f93-fe3f7b7f302b · outbound

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

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Trajectory balance: Improved credit assignment in GFlowNets

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Resolution: verified exact; local_arxiv, observed 2026-08-08T15:33:59.562967Z

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.

Observation 7849c2f5-5a11-4b29-a0a6-998c9d08f4dc · outbound

This paper cites GFlowNets and variational inference.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier GFlowNets and variational inference

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-08T15:33:58.207420Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50ac23bd-1bd9-4620-9d00-c84bb8ffa770 · outbound

This paper cites 12, 5 (2026), DOI 10.1038/s41534-025-01159-x; arXiv:2507.01726.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier 12, 5 (2026), DOI 10.1038/s41534-025-01159-x; arXiv:2507.01726

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Resolution: verified exact; raw_fallback, observed 2026-08-08T15:33:59.528450Z

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.

Observation ca0db1c8-b2da-4f03-b51b-b55dcb7c16ce · outbound

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

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier GFlowNets for Hamiltonian decomposition in groups of compatible operators

Reference 66

Resolution: verified exact; local_arxiv, observed 2026-08-08T15:33:59.433195Z

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.

Observation 54f85a8c-07ab-4de3-9b7e-32ee58188a03 · outbound

This paper cites Discrete Flow-Based Generative Models for Measurement Optimization in Quantum Computing.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Discrete Flow-Based Generative Models for Measurement Optimization in Quantum Computing

Reference 67

Resolution: metadata mismatch; local_arxiv, observed 2026-08-08T15:33:59.412804Z

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.

Observation 1f1114a4-e234-429f-bae8-ce0b7d0b328b · outbound

This paper cites Generative Flow Networks for Discrete Probabilistic Modeling.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Generative Flow Networks for Discrete Probabilistic Modeling

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Resolution: verified exact; local_arxiv, observed 2026-08-08T15:33:59.391508Z

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.

Observation 7a9ef9e2-9e00-446f-9bb7-637bb615ce42 · outbound

This paper cites GFlowNet Pretraining with Inexpensive Rewards.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier GFlowNet Pretraining with Inexpensive Rewards

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-08T15:33:58.228768Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a0163ab8-f086-4f17-8e50-42977923f863 · outbound

This paper cites Learning to Scale Logits for Temperature-Conditional GFlowNets.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Learning to Scale Logits for Temperature-Conditional GFlowNets

Reference 70

Resolution: verified exact; local_arxiv, observed 2026-08-08T15:33:59.355767Z

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.

Observation b5b10e8b-b035-4b57-8789-35cc42c89264 · outbound

This paper cites an unresolved cited work.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Unresolved cited work

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-08T15:33:58.237908Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 181a2782-fa90-4ffd-809b-f57daf14dfca · outbound

This paper cites S., Matthews, A.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier S., Matthews, A

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-08T15:33:58.242336Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 18f1bf3c-f01e-4dec-9fc2-db0882b41ba1 · outbound

This paper cites an unresolved cited work.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Unresolved cited work

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-08T15:33:58.246883Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b921afab-7b4c-452a-bcb8-45a5c2f50042 · outbound

This paper cites S., Pfau, D.,A self-attention ansatz for ab initio quantum chemistry (Psiformer), ICLR (2023).

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier S., Pfau, D.,A self-attention ansatz for ab initio quantum chemistry (Psiformer), ICLR (2023)

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Resolution: unresolved; no resolver link, observed 2026-08-08T15:33:58.251342Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 185632b1-2229-464d-8ab3-9cbf089a6c86 · outbound

This paper cites an unresolved cited work.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Unresolved cited work

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-08T15:33:58.255786Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 24e3891a-2d56-487a-bfa5-fd5be5117a39 · outbound

This paper cites An ab initio foundation model of wavefunctions that accurately describes chemical bond breaking.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier An ab initio foundation model of wavefunctions that accurately describes chemical bond breaking

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-08T15:33:58.260171Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 22f3471e-3514-495a-b638-bb6c9c2f38cd · outbound

This paper cites K.,Neural network backflow for ab initio quantum chemistry (NNBF),Phys.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier K.,Neural network backflow for ab initio quantum chemistry (NNBF),Phys

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Resolution: unresolved; no resolver link, observed 2026-08-08T15:33:58.264734Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7820f43e-6387-4546-bcac-b382478886ba · outbound

This paper cites Efficient optimization of neural network backflow for ab-initio quantum chemistry.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Efficient optimization of neural network backflow for ab-initio quantum chemistry

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-08T15:33:58.269357Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b8cf47b6-4dcf-4a5e-921f-87a90b3b0e20 · outbound

This paper cites Precise Quantum Chemistry calculations with few Slater Determinants.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Precise Quantum Chemistry calculations with few Slater Determinants

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-08T15:33:58.274252Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b36198a-be55-41b0-b772-d2bb14513464 · outbound

This paper cites Ab-initio quantum chemistry with neural-network wavefunctions.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Ab-initio quantum chemistry with neural-network wavefunctions

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-08T15:33:58.278896Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bbaa818d-dfee-4d9f-be21-ac09e9b9bf11 · outbound

This paper cites Autoregressive neural-network wavefunctions for ab initio quantum chemistry.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Autoregressive neural-network wavefunctions for ab initio quantum chemistry

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-08T15:33:58.283568Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1ed25d47-0ff4-4e9e-be04-40a071486392 · outbound

This paper cites Quantum Package 2.0: An Open-Source Determinant-Driven Suite of Programs.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Quantum Package 2.0: An Open-Source Determinant-Driven Suite of Programs

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-08T15:33:58.288275Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bc363cfb-0769-4cad-aa98-5746495f2af8 · outbound

This paper cites Go Green: Selected Configuration Interaction as a More Sustainable Alternative for High Accuracy.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Go Green: Selected Configuration Interaction as a More Sustainable Alternative for High Accuracy

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-08T15:33:58.292911Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e969e18a-a2fb-41dc-956f-47057fc9cfe1 · outbound

This paper cites R.,Density matrix formulation for quantum renormalization groups (DMRG),Phys.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier R.,Density matrix formulation for quantum renormalization groups (DMRG),Phys

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Resolution: unresolved; no resolver link, observed 2026-08-08T15:33:58.297491Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 27978b86-e044-45ff-ab98-7bc75271d09f · outbound

This paper cites K.-L., Head-Gordon, M.,Highly correlated calculations with a polynomial cost algorithm (DMRG),J.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier K.-L., Head-Gordon, M.,Highly correlated calculations with a polynomial cost algorithm (DMRG),J

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Resolution: unresolved; no resolver link, observed 2026-08-08T15:33:58.301919Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e94797f-2bd8-4b37-8f37-880cb1501064 · outbound

This paper cites an unresolved cited work.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Unresolved cited work

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-08T15:33:58.306285Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e67b17b-2935-4218-b897-89a447594245 · outbound

This paper cites H., Thom, A.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier H., Thom, A

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-08T15:33:58.310706Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 80bb6fe4-efa9-4902-a3cd-565051a081c4 · outbound

This paper cites The Ground State Electronic Energy of Benzene.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier The Ground State Electronic Energy of Benzene

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Resolution: verified exact; local_arxiv, observed 2026-08-08T15:33:59.231493Z

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.

Observation 487c9866-223a-4015-9685-96f0ab10ebe5 · outbound

This paper cites M., Chan, G.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier M., Chan, G

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-08T15:33:58.319217Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2162199b-c9d7-4a0e-9b3c-ecbb07d1d123 · outbound

This paper cites Direct comparison of many-body methods for realistic electronic Hamiltonians.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Direct comparison of many-body methods for realistic electronic Hamiltonians

Reference 90

Resolution: metadata mismatch; local_arxiv, observed 2026-08-08T15:33:59.210604Z

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.

Observation 7e3951e0-5331-4a6f-9587-8519e68a15a1 · outbound

This paper cites P., Abraham, V., Peng, B., Asthana, A.,Chemically decisive benchmarks on the path to quantum utility, arXiv:2601.10813 (2026).

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier P., Abraham, V., Peng, B., Asthana, A.,Chemically decisive benchmarks on the path to quantum utility, arXiv:2601.10813 (2026)

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Resolution: unresolved; no resolver link, observed 2026-08-08T15:33:58.327779Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0369f60f-cc59-48eb-b9e3-5f3a538e2912 · outbound

This paper cites M., Zhang, H., Motta, M., Faulstich, F.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier M., Zhang, H., Motta, M., Faulstich, F

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Resolution: unresolved; no resolver link, observed 2026-08-08T15:33:58.331934Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 78fb8236-eb47-4f83-81b0-2f9e631b23c6 · outbound

This paper cites an unresolved cited work.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Unresolved cited work

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-08T15:33:58.336118Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d7adcde3-d0ae-47c2-b879-d68939e90e21 · outbound

This paper cites ExtraFerm: An Extended Matchgate Simulator.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier ExtraFerm: An Extended Matchgate Simulator

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-08T15:33:58.340358Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 89e64353-a002-46b5-bf37-61b4dbf055f9 · outbound

This paper cites Polynomial-time exact diagonalization via sparse guided eigenwalks.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Polynomial-time exact diagonalization via sparse guided eigenwalks

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Resolution: metadata mismatch; local_arxiv, observed 2026-08-08T15:33:58.956416Z

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.

Observation 036f87ea-bc0c-4aa6-9258-740c26d6ba6c · outbound

This paper cites Classical computational simulation of the FeMo-cofactor model to chemical accuracy and its implications.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Classical computational simulation of the FeMo-cofactor model to chemical accuracy and its implications

Reference 96

Resolution: unresolved; no resolver link, observed 2026-08-08T15:33:58.349851Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4a54e6ce-dd82-490a-a2ca-50f5b5bb836c · outbound

This paper cites an unresolved cited work.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Unresolved cited work

Reference 97

Resolution: unresolved; no resolver link, observed 2026-08-08T15:33:58.354570Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 82cf1ad4-cfe4-419f-9f43-b6193119a556 · outbound

This paper cites Is there evidence for exponential quantum advantage in quantum chemistry?.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Is there evidence for exponential quantum advantage in quantum chemistry?

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Resolution: verified exact; local_arxiv, observed 2026-08-08T15:33:58.842533Z

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.

Observation fe3f9fc1-0cf7-44d2-9723-b27d77a60725 · outbound

This paper cites Quantum Advantage in Computational Chemistry?.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier Quantum Advantage in Computational Chemistry?

Reference 99

Resolution: unresolved; no resolver link, observed 2026-08-08T15:33:58.363453Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c87a0721-390e-4a91-8ba8-dae730d2601b · outbound

This paper cites A., Xantheas, S.

Machine learning for sample-based quantum diagonalization: generative configuration recovery and the classical-simulability frontier A., Xantheas, S

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Resolution: unresolved; no resolver link, observed 2026-08-08T15:33:58.367996Z

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