Paper Citation Record · LEDGER

Optimizing quantum heuristics with meta-learning

As of 15 August 2026, this Paper Citation Record lists 84 of 84 outbound references and 2 inbound Pith citation observations for arXiv:1908.03185.

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

pith.paper-citation-record.v1
1908.03185 v1

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measured 84 of 84 reference resolution

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measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-14T11:33:55.425869Z

Reference resolution

84 of 84 outbound references displayed

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External citation measurements

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Outbound references

Observation 5b639ca7-428f-4d88-8abf-f41a38433102 · outbound

This paper cites Local optimizers have a notion of location in the solution space.

Optimizing quantum heuristics with meta-learning Local optimizers have a notion of location in the solution space

Reference 1

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Observation 6c4bb7dd-03fb-4aed-9656-dd3d53075d03 · outbound

This paper cites An important class of global black-box optimizers we consider are Bayesian optimizers.

Optimizing quantum heuristics with meta-learning An important class of global black-box optimizers we consider are Bayesian optimizers

Reference 2

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Observation a9553886-5282-49dd-aa2d-d5da2ac6311b · outbound

This paper cites The best classical eﬃcient al- gorithm known for this problem provably yields only a log-factor worst-case approximation ratio [73].

Optimizing quantum heuristics with meta-learning The best classical eﬃcient al- gorithm known for this problem provably yields only a log-factor worst-case approximation ratio [73]

Reference 3

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This paper cites Unrolling is the process of iteratively updating the inputs, x, cell state and hidden state, referred to together as s, of the LSTM.

Optimizing quantum heuristics with meta-learning Unrolling is the process of iteratively updating the inputs, x, cell state and hidden state, referred to together as s, of the LSTM

Reference 4

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This paper cites In this model there is a hopping termt, a many body interaction term U and an onsite chemical potential term µ.

Optimizing quantum heuristics with meta-learning In this model there is a hopping termt, a many body interaction term U and an onsite chemical potential term µ

Reference 5

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This paper cites the AND of a number of disjunc- tive two-variable OR clauses), MAX-SAT is the NP-hard problem of determining the maximum number of clauses which may be simultaneously satisﬁed.

Optimizing quantum heuristics with meta-learning the AND of a number of disjunc- tive two-variable OR clauses), MAX-SAT is the NP-hard problem of determining the maximum number of clauses which may be simultaneously satisﬁed

Reference 6

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Observation 486f20c4-2754-4ef4-b131-bf9032f7eab7 · outbound

This paper cites A perspective view and survey of meta-learning,.

Optimizing quantum heuristics with meta-learning A perspective view and survey of meta-learning,

Reference 7

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Observation 43b14657-55f7-469d-b6b4-bf909761c869 · outbound

This paper cites Metalearning: a survey of trends and technologies,.

Optimizing quantum heuristics with meta-learning Metalearning: a survey of trends and technologies,

Reference 8

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Observation 952e969a-dc37-4aa3-aa58-ae0be904e84c · outbound

This paper cites Deepsd: Generating high res- olution climate change projections through single image super-resolution,.

Optimizing quantum heuristics with meta-learning Deepsd: Generating high res- olution climate change projections through single image super-resolution,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:57.474570Z

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Observation a9897ef0-3475-4ba7-a5fa-2b74caa7a45e · outbound

This paper cites Machine learning applications in genetics and genomics,.

Optimizing quantum heuristics with meta-learning Machine learning applications in genetics and genomics,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:57.454505Z

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Observation 3522826a-590c-453b-a560-88a103a7a4ac · outbound

This paper cites Towards energy-aware scheduling in data centers using machine learning,.

Optimizing quantum heuristics with meta-learning Towards energy-aware scheduling in data centers using machine learning,

Reference 11

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Observation 9a8bbaf3-29f8-4a63-a829-11e893d00f53 · outbound

This paper cites Machine learning: Trends, perspectives, and prospects,.

Optimizing quantum heuristics with meta-learning Machine learning: Trends, perspectives, and prospects,

Reference 12

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This paper cites A high-bias, low- variance introduction to machine learning for physicists,.

Optimizing quantum heuristics with meta-learning A high-bias, low- variance introduction to machine learning for physicists,

Reference 13

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Observation c9040f8e-72c8-4a24-a3a0-19e5502358cd · outbound

This paper cites Deep learning,.

Optimizing quantum heuristics with meta-learning Deep learning,

Reference 14

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Observation 1191fbfd-f3ae-4338-acb9-2ae295240170 · outbound

This paper cites Meta-learning with memory-augmented neural networks,.

Optimizing quantum heuristics with meta-learning Meta-learning with memory-augmented neural networks,

Reference 15

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Observation 56ce8d1d-a074-438b-a4e6-27474174b6c7 · outbound

This paper cites On First-Order Meta-Learning Algorithms.

Optimizing quantum heuristics with meta-learning On First-Order Meta-Learning Algorithms

Reference 16

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Observation 07fcd912-2eae-4c27-aeae-a1892b52ef91 · outbound

This paper cites Learning to learn by gradient descent by gradient de- scent,.

Optimizing quantum heuristics with meta-learning Learning to learn by gradient descent by gradient de- scent,

Reference 17

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Observation e479c97a-149d-48d0-8700-99a8f61c2548 · outbound

This paper cites Learning to Optimize.

Optimizing quantum heuristics with meta-learning Learning to Optimize

Reference 18

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Observation 4b0343d1-acbd-4fcb-ae72-066495d5fa47 · outbound

This paper cites Optimization as a model for few-shot learning,.

Optimizing quantum heuristics with meta-learning Optimization as a model for few-shot learning,

Reference 19

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Observation ee936312-1eb6-4f4d-bcc2-60061f728689 · outbound

This paper cites Learning to learn without gradient descent by gradient descent,.

Optimizing quantum heuristics with meta-learning Learning to learn without gradient descent by gradient descent,

Reference 20

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Observation 2a9975ff-35c2-460c-8eed-fd9c87222817 · outbound

This paper cites Eﬃcient and robust automated machine learning,.

Optimizing quantum heuristics with meta-learning Eﬃcient and robust automated machine learning,

Reference 21

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Observation 4b06e1cc-d8fc-44df-a76a-586c61ab5c1e · outbound

This paper cites Meta networks,.

Optimizing quantum heuristics with meta-learning Meta networks,

Reference 22

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Observation a6862613-b250-48d2-88e8-009ad903c4d7 · outbound

This paper cites From Ans\"atze to Z-gates: a NASA View of Quantum Computing.

Optimizing quantum heuristics with meta-learning From Ans\"atze to Z-gates: a NASA View of Quantum Computing

Reference 23

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Observation 29dc1dec-3364-4f37-bfdc-35e731b5b5c7 · outbound

This paper cites Optimizing QAOA: Success Probability and Runtime Dependence on Circuit Depth.

Optimizing quantum heuristics with meta-learning Optimizing QAOA: Success Probability and Runtime Dependence on Circuit Depth

Reference 24

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Observation 1a9881cf-fcb9-4af3-98bb-1d5901463f99 · outbound

This paper cites Commercialize quantum technologies in ﬁve years,.

Optimizing quantum heuristics with meta-learning Commercialize quantum technologies in ﬁve years,

Reference 25

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Observation 412f2aab-9f41-4249-b199-865c3c31f698 · outbound

This paper cites Quantum computing in the nisq era and be- yond,.

Optimizing quantum heuristics with meta-learning Quantum computing in the nisq era and be- yond,

Reference 26

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Observation 5ba9c2f5-7f12-46e1-9a1b-c4f4dd69955f · outbound

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

Optimizing quantum heuristics with meta-learning A variational eigenvalue solver on a photonic quantum processor,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:57.188445Z

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Observation 5344fb54-0939-42f3-abc0-f92c2abe6819 · outbound

This paper cites A Quantum Approximate Optimization Algorithm.

Optimizing quantum heuristics with meta-learning A Quantum Approximate Optimization Algorithm

Reference 28

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Observation 47c4bcd9-2635-4ab9-b0c7-ffdeb52b4ac0 · outbound

This paper cites From the quantum ap- proximate optimization algorithm to a quantum alter- nating operator ansatz,.

Optimizing quantum heuristics with meta-learning From the quantum ap- proximate optimization algorithm to a quantum alter- nating operator ansatz,

Reference 29

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Observation 95f9d54c-3e99-48de-9ec7-e03eafdfa36e · outbound

This paper cites QAOA for Max- Cut requires hundreds of qubits for quantum speed-up,.

Optimizing quantum heuristics with meta-learning QAOA for Max- Cut requires hundreds of qubits for quantum speed-up,

Reference 30

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Observation 1a01a135-5c18-4082-9aa4-d212b9462cca · outbound

This paper cites Performance of hybrid quantum-classical variational heuristics for combinatorial optimization,.

Optimizing quantum heuristics with meta-learning Performance of hybrid quantum-classical variational heuristics for combinatorial optimization,

Reference 31

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Observation 1e69101e-efb5-46ca-8998-49805300c7f9 · outbound

This paper cites Optimal quan- tum measurements of expectation values of observables,.

Optimizing quantum heuristics with meta-learning Optimal quan- tum measurements of expectation values of observables,

Reference 32

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Observation cd2ade86-c6ad-436e-a0a8-6ad620f56752 · outbound

This paper cites Quantum chemistry calculations on a trapped-ion quantum simulator,.

Optimizing quantum heuristics with meta-learning Quantum chemistry calculations on a trapped-ion quantum simulator,

Reference 33

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Observation ac8268a4-fead-4776-aba7-00231b63a64e · outbound

This paper cites Scalable quantum simula- tion of molecular energies,.

Optimizing quantum heuristics with meta-learning Scalable quantum simula- tion of molecular energies,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:57.116722Z

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This paper cites A Hybrid Classical/Quantum Approach for Large-Scale Studies of Quantum Systems with Density Matrix Embedding Theory.

Optimizing quantum heuristics with meta-learning A Hybrid Classical/Quantum Approach for Large-Scale Studies of Quantum Systems with Density Matrix Embedding Theory

Reference 35

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This paper cites Training a quantum optimizer,.

Optimizing quantum heuristics with meta-learning Training a quantum optimizer,

Reference 36

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Observation af686cfe-0b50-47dd-8bb8-c14277452bcb · outbound

This paper cites Progress to- wards practical quantum variational algorithms,.

Optimizing quantum heuristics with meta-learning Progress to- wards practical quantum variational algorithms,

Reference 37

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Observation 33cb6027-796b-40fa-9570-e6b24e1e14a8 · outbound

This paper cites Whilst in the production of this FIG.

Optimizing quantum heuristics with meta-learning Whilst in the production of this FIG

Reference 38

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Source-reported events for the cited work

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Observation 80bf3f16-ed81-4f1c-baef-403bb35c2519 · outbound

This paper cites In that preprint, the authors consider only gradient-free implementations of meta- learners.

Optimizing quantum heuristics with meta-learning In that preprint, the authors consider only gradient-free implementations of meta- learners

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:57.491624Z

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Observation da2df455-4c5c-4527-9e7c-db6c7dd718f8 · outbound

This paper cites Practical optimization for hybrid quantum-classical algorithms.

Optimizing quantum heuristics with meta-learning Practical optimization for hybrid quantum-classical algorithms

Reference 40

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Observation 72cacdb0-b34c-49b1-a9a5-0061e59d6d91 · outbound

This paper cites Barren plateaus in quantum neural network training landscapes,.

Optimizing quantum heuristics with meta-learning Barren plateaus in quantum neural network training landscapes,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:57.029747Z

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Observation b2569d9a-ece8-42f3-8c23-2366d6c05367 · outbound

This paper cites A limited memory algorithm for bound constrained optimization,.

Optimizing quantum heuristics with meta-learning A limited memory algorithm for bound constrained optimization,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:57.010948Z

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Observation c21d1205-a736-495b-bf92-0204fc3cd66c · outbound

This paper cites A simplex method for func- tion minimization,.

Optimizing quantum heuristics with meta-learning A simplex method for func- tion minimization,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:56.993098Z

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Observation 03319705-2c46-4b29-a0a8-088e85f6f449 · outbound

This paper cites A new method of locating the maximum point of an arbitrary multipeak curve in the presence of noise,.

Optimizing quantum heuristics with meta-learning A new method of locating the maximum point of an arbitrary multipeak curve in the presence of noise,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:56.975584Z

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Observation c702cbf1-3832-4889-90d7-d6eb3938ef7b · outbound

This paper cites Evolution strategies as a scalable alternative to rein- forcement learning,.

Optimizing quantum heuristics with meta-learning Evolution strategies as a scalable alternative to rein- forcement learning,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:56.958504Z

Source-reported events for the cited work

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Observation 31209820-46ce-41b2-9638-db92c0f6dab3 · outbound

This paper cites Long short-term memory,.

Optimizing quantum heuristics with meta-learning Long short-term memory,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:56.941948Z

Source-reported events for the cited work

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Observation 55d393d9-6e4a-41fd-80c8-7ccbd018a51c · outbound

This paper cites Learning to learn with quantum neural networks via classical neural networks.

Optimizing quantum heuristics with meta-learning Learning to learn with quantum neural networks via classical neural networks

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-14T14:26:55.662498Z

Source-reported events for the cited work

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Observation bb547d49-6cda-4f09-8743-21331b07d63e · outbound

This paper cites Multivariate stochastic approximation using a simultaneous perturbation gradient approxima- tion,.

Optimizing quantum heuristics with meta-learning Multivariate stochastic approximation using a simultaneous perturbation gradient approxima- tion,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:56.925405Z

Source-reported events for the cited work

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Observation bce393c8-1746-47a6-8928-5f268411416f · outbound

This paper cites Theoreti- cal framework for comparing several stochastic optimiza- tion approaches,.

Optimizing quantum heuristics with meta-learning Theoreti- cal framework for comparing several stochastic optimiza- tion approaches,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:56.908714Z

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Observation 223f1793-e9d7-4dcc-9e17-589e8bb75da8 · outbound

This paper cites Quantum optimization using variational algorithms on near-term quantum devices,.

Optimizing quantum heuristics with meta-learning Quantum optimization using variational algorithms on near-term quantum devices,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:56.890494Z

Source-reported events for the cited work

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Observation 5318b6b2-a6e4-4150-b187-44a09b3b3fda · outbound

This paper cites Hardware- eﬃcient variational quantum eigensolver for small molecules and quantum magnets,.

Optimizing quantum heuristics with meta-learning Hardware- eﬃcient variational quantum eigensolver for small molecules and quantum magnets,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:56.872854Z

Source-reported events for the cited work

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Observation cdba6b47-0c5c-43c8-80f2-95a4c82ea3e7 · outbound

This paper cites Papadimitriou, Computational complexity.

Optimizing quantum heuristics with meta-learning Papadimitriou, Computational complexity

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:56.856888Z

Source-reported events for the cited work

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Observation bcf56d00-7453-49e8-b70a-a5aa573ff85f · outbound

This paper cites Electron correlations in narrow energy bands,.

Optimizing quantum heuristics with meta-learning Electron correlations in narrow energy bands,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:56.840625Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ef6aefc5-5df6-4029-934d-1f2fe415117f · outbound

This paper cites Quantum approximate op- timization with hard and soft constraints,.

Optimizing quantum heuristics with meta-learning Quantum approximate op- timization with hard and soft constraints,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:56.823762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 46078195-00aa-4f25-9442-922609e3a1da · outbound

This paper cites Quan- tum approximate optimization algorithm for MaxCut: A fermionic view,.

Optimizing quantum heuristics with meta-learning Quan- tum approximate optimization algorithm for MaxCut: A fermionic view,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:56.806360Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3f20e9b5-caf2-47d2-99ef-51cba9f67b49 · outbound

This paper cites Ausiello, P.

Optimizing quantum heuristics with meta-learning Ausiello, P

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:56.788891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6904243e-924b-49d9-b4dd-cd8c11d26e16 · outbound

This paper cites $XY$-mixers: analytical and numerical results for QAOA.

Optimizing quantum heuristics with meta-learning $XY$-mixers: analytical and numerical results for QAOA

Reference 57

Resolution: verified exact; local_arxiv, observed 2026-08-14T14:26:56.225360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4ef5cc82-dad9-42a7-99fd-7ea9a1b6e501 · outbound

This paper cites Strategies for quantum computing molecular energies using the unitary coupled cluster ansatz,.

Optimizing quantum heuristics with meta-learning Strategies for quantum computing molecular energies using the unitary coupled cluster ansatz,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:56.770466Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3c708c25-95d0-46cb-a79a-6b04666cbde4 · outbound

This paper cites Bayesian optimisation for variational quantum eigensolvers,.

Optimizing quantum heuristics with meta-learning Bayesian optimisation for variational quantum eigensolvers,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:56.753042Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a5162fcb-bd73-4a83-a40e-40b87f768884 · outbound

This paper cites Bengio, S.

Optimizing quantum heuristics with meta-learning Bengio, S

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:56.735239Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ef4bd5c0-d43f-4e1d-8c07-d1ae036aa0f0 · outbound

This paper cites Neural optimizer search with reinforcement learning,.

Optimizing quantum heuristics with meta-learning Neural optimizer search with reinforcement learning,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:56.716210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 93869d04-d445-459a-9b7f-88c69917bb09 · outbound

This paper cites Learned optimizers that scale and general- ize,.

Optimizing quantum heuristics with meta-learning Learned optimizers that scale and general- ize,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:56.699165Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4532ce41-e0fb-4caa-8587-fd133792c0d2 · outbound

This paper cites Learning long- term dependencies with gradient descent is diﬃcult,.

Optimizing quantum heuristics with meta-learning Learning long- term dependencies with gradient descent is diﬃcult,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:56.682786Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 25d89e48-0a5a-4e53-9002-9aeac42f0887 · outbound

This paper cites The vanishing gradient problem dur- ing learning recurrent neural nets and problem solu- tions,.

Optimizing quantum heuristics with meta-learning The vanishing gradient problem dur- ing learning recurrent neural nets and problem solu- tions,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:56.665799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 08474f0f-b5cb-46de-885c-c8cd753c53c9 · outbound

This paper cites A quantum algorithm to train neural networks using low-depth circuits.

Optimizing quantum heuristics with meta-learning A quantum algorithm to train neural networks using low-depth circuits

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-14T14:26:55.763903Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0dc6fec0-27ec-4f4d-90b1-41d8dbd2eae3 · outbound

This paper cites A quantum engineer’s guide to superconducting qubits,.

Optimizing quantum heuristics with meta-learning A quantum engineer’s guide to superconducting qubits,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:56.648435Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation fa97cbd6-4566-4d73-80ca-90b93d815344 · outbound

This paper cites Forest SDK.

Optimizing quantum heuristics with meta-learning Forest SDK

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:56.631234Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1e8d5607-561c-4a56-bbcf-94b078599b41 · outbound

This paper cites Taking the human out of the loop: A re- view of bayesian optimization,.

Optimizing quantum heuristics with meta-learning Taking the human out of the loop: A re- view of bayesian optimization,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:56.614166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8c50777f-b766-4909-90e8-9525e52b893f · outbound

This paper cites Evolution strategies for deep neural network models design,.

Optimizing quantum heuristics with meta-learning Evolution strategies for deep neural network models design,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:56.596457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 042d84b2-d099-4d07-80e4-94fda4ea794e · outbound

This paper cites An overview of genetic algorithms: Part 1, fundamentals,.

Optimizing quantum heuristics with meta-learning An overview of genetic algorithms: Part 1, fundamentals,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:56.578822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 89c16ae6-c155-465d-8187-74972d1d8bfa · outbound

This paper cites An introduction to genetic algorithms for numerical optimization,.

Optimizing quantum heuristics with meta-learning An introduction to genetic algorithms for numerical optimization,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:56.560902Z

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ae97916c-e0b1-45a6-85d0-3ad30ce66c47 · outbound

This paper cites An initialization strategy for addressing barren plateaus in parametrized quantum circuits.

Optimizing quantum heuristics with meta-learning An initialization strategy for addressing barren plateaus in parametrized quantum circuits

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-14T14:26:55.803167Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f043c4a6-2632-4461-9796-e22310f8d40d · outbound

This paper cites Evaluating analytic gradients on quantum hard- ware,.

Optimizing quantum heuristics with meta-learning Evaluating analytic gradients on quantum hard- ware,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:56.542899Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c18880d4-8575-4dcf-94da-1ab26f8cf025 · outbound

This paper cites Solutions of the two-dimensional hubbard model: benchmarks and results from a wide range of numerical algorithms,.

Optimizing quantum heuristics with meta-learning Solutions of the two-dimensional hubbard model: benchmarks and results from a wide range of numerical algorithms,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:56.524935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 20a0e5c8-d5b7-43ca-b35b-c564c4d2eaf2 · outbound

This paper cites Interacting fermions in one dimen- sion: from weak to strong correlation,.

Optimizing quantum heuristics with meta-learning Interacting fermions in one dimen- sion: from weak to strong correlation,

Reference 75

Resolution: verified exact; raw_fallback, observed 2026-08-14T14:26:56.164805Z

Source-reported events for the cited work

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Observation a0852ee5-738a-4f6f-826d-c5f76e6d20c9 · outbound

This paper cites Numerical evi- dence of ﬂuctuating stripes in the normal state of high-tc cuprate superconductors,.

Optimizing quantum heuristics with meta-learning Numerical evi- dence of ﬂuctuating stripes in the normal state of high-tc cuprate superconductors,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:56.507637Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b8058a17-f135-43a8-83c2-ed730c615e19 · outbound

This paper cites ¨ uber das Paulische ¨ aquivalenzverbot,.

Optimizing quantum heuristics with meta-learning ¨ uber das Paulische ¨ aquivalenzverbot,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:56.488285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d7e4fbcb-c28d-4dc4-b27d-8127eba5e7c1 · outbound

This paper cites Entanglement in quantum-classical vari- ational algorithms,.

Optimizing quantum heuristics with meta-learning Entanglement in quantum-classical vari- ational algorithms,

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:56.468378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a7aa8695-758f-4496-bcf7-6e0d3962b5c5 · outbound

This paper cites Entanglement re- quirements for hybrid quantum-classical algorithms,.

Optimizing quantum heuristics with meta-learning Entanglement re- quirements for hybrid quantum-classical algorithms,

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:56.450911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a5d19494-6fbe-4e35-aa2d-7f6bfd8d4a81 · outbound

This paper cites On the representation of Boolean and real functions as Hamiltonians for quantum computing.

Optimizing quantum heuristics with meta-learning On the representation of Boolean and real functions as Hamiltonians for quantum computing

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-14T14:26:55.845008Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 23112804-4a8c-43e8-9295-f30983c3c975 · outbound

This paper cites A polylogarithmic ap- proximation of the minimum bisection,.

Optimizing quantum heuristics with meta-learning A polylogarithmic ap- proximation of the minimum bisection,

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:56.431799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2402d00f-bf95-464d-aa3a-3276cbd6039d · outbound

This paper cites SciPy: Open source scientiﬁc tools for Python,.

Optimizing quantum heuristics with meta-learning SciPy: Open source scientiﬁc tools for Python,

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:56.413354Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 49590ee6-dbc7-47b8-b6ec-ea1c1f729407 · outbound

This paper cites GPyOpt: A bayesian optimiza- tion framework in python.

Optimizing quantum heuristics with meta-learning GPyOpt: A bayesian optimiza- tion framework in python

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:26:56.395891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 35c5fea6-3f40-4c34-864a-415b17d6bd7b · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Optimizing quantum heuristics with meta-learning Adam: A Method for Stochastic Optimization

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-14T14:26:55.867357Z

Source-reported events for the cited work

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Pith citing papers

Observation e5ecd2a3-7322-4d30-ad28-362e9d009e4c · inbound

Training the Quantum Approximate Optimization Algorithm without access to a Quantum Processing Unit cites this paper.

Training the Quantum Approximate Optimization Algorithm without access to a Quantum Processing Unit Optimizing quantum heuristics with meta-learning

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-08-14T11:33:55.435917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3ce2e216-5cb2-41b8-96b9-60e2bd3ba69c · inbound

A unifying account of warm start guarantees for patches of quantum landscapes cites this paper.

A unifying account of warm start guarantees for patches of quantum landscapes Optimizing quantum heuristics with meta-learning

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-08T11:35:58.465826Z

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Unavailable: canonical work link unavailable.