Evaluating QAOA expectation values can be as hard as counting optimal solutions | Paper Citation Record

Observation 9a432d83-cc3f-4ffd-86fa-b7ed5c0f6dc7 · outbound

This paper cites Challenges and opportunities in quantum optimization.Nature Reviews Physics, pages 1–18, 2024.

Evaluating QAOA expectation values can be as hard as counting optimal solutions Challenges and opportunities in quantum optimization.Nature Reviews Physics, pages 1–18, 2024

Reference 1

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Observation 3afa827b-3b2b-4843-9107-0f6b106367d9 · outbound

This paper cites A Quantum Approximate Optimization Algorithm.

Evaluating QAOA expectation values can be as hard as counting optimal solutions A Quantum Approximate Optimization Algorithm

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-15T14:26:31.733319Z

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Observation fe22a873-711b-4c0a-9754-3fb67dc7785a · outbound

This paper cites Rieffel, Davide Venturelli, and Rupak Biswas.

Evaluating QAOA expectation values can be as hard as counting optimal solutions Rieffel, Davide Venturelli, and Rupak Biswas

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:33.233793Z

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Observation 3c10eb4a-5c2c-4471-bb5f-d6892f652eee · outbound

This paper cites Quantum approximate optimization algorithm for MaxCut: A fermionic view.Physical Review A, 97(2):022304, 2018.

Evaluating QAOA expectation values can be as hard as counting optimal solutions Quantum approximate optimization algorithm for MaxCut: A fermionic view.Physical Review A, 97(2):022304, 2018

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:33.216049Z

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Observation 24dd9c96-29d7-4e4c-9b10-8c277b61156b · outbound

This paper cites A unified complexity- algorithm account of constant-round QAOA expectation computation.

Evaluating QAOA expectation values can be as hard as counting optimal solutions A unified complexity- algorithm account of constant-round QAOA expectation computation

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-15T14:26:31.751500Z

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Observation e740b920-4975-4631-adfd-8c8718cc4cac · outbound

This paper cites an unresolved cited work.

Evaluating QAOA expectation values can be as hard as counting optimal solutions Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-15T14:26:33.199460Z

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Observation 0427b049-b4ef-440e-bc90-6123ea710497 · outbound

This paper cites an unresolved cited work.

Evaluating QAOA expectation values can be as hard as counting optimal solutions Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-08-15T14:26:33.181801Z

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Observation d5257bc2-7d4a-4c24-89fb-1bc61fbf4c93 · outbound

This paper cites Polynomial-time approximation algorithms for the Ising model.

Evaluating QAOA expectation values can be as hard as counting optimal solutions Polynomial-time approximation algorithms for the Ising model

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:33.164651Z

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Observation 4f45b677-fd6a-440f-bdaa-30379df00f12 · outbound

This paper cites The complexity of counting cuts and of computing the probability that a graph is connected.SIAM Journal on Computing, 12(4):777–788, 1983.

Evaluating QAOA expectation values can be as hard as counting optimal solutions The complexity of counting cuts and of computing the probability that a graph is connected.SIAM Journal on Computing, 12(4):777–788, 1983

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:33.147335Z

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Observation f9015fb0-b451-4e03-9115-afe3351e0ec7 · outbound

This paper cites Complexity of counting the optimal solutions.Theoretical Computer Science, 410(38-40):3814–3825, 2009.

Evaluating QAOA expectation values can be as hard as counting optimal solutions Complexity of counting the optimal solutions.Theoretical Computer Science, 410(38-40):3814–3825, 2009

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:33.130696Z

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Observation 0f03e250-99e8-4153-b82a-21b43c728004 · outbound

This paper cites Standard implementations ofe−iγCG require at least∆(G)two-qubit gate depth.

Evaluating QAOA expectation values can be as hard as counting optimal solutions Standard implementations ofe−iγCG require at least∆(G)two-qubit gate depth

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:33.114256Z

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Observation dfbb3ed4-3eab-4393-a387-017f2a6e6e39 · outbound

This paper cites Quantum algorithms for scientific computing and approximate optimization.

Evaluating QAOA expectation values can be as hard as counting optimal solutions Quantum algorithms for scientific computing and approximate optimization

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:33.096630Z

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Observation 1988896d-c392-4b45-8354-f4c1a2e3b3ce · outbound

This paper cites Quantum Supremacy through the Quantum Approximate Optimization Algorithm.

Evaluating QAOA expectation values can be as hard as counting optimal solutions Quantum Supremacy through the Quantum Approximate Optimization Algorithm

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T14:26:31.792601Z

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Observation 9fe01bb3-2568-43d4-9cc3-01b72f8dab76 · outbound

This paper cites In particular, choosingb =O(N)permits exponentially small error while preserving a polynomial-time reduction.

Evaluating QAOA expectation values can be as hard as counting optimal solutions In particular, choosingb =O(N)permits exponentially small error while preserving a polynomial-time reduction

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:33.077827Z

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Observation bfee9827-1cd8-4e08-aa30-e6cd32789743 · outbound

This paper cites 1.2 may be formalized as follows.

Evaluating QAOA expectation values can be as hard as counting optimal solutions 1.2 may be formalized as follows

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:33.061968Z

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Observation a8d97056-8739-4131-b080-cce9ce1f0755 · outbound

This paper cites 1.2 can all be performed in polynomial time and with polynomially scaling number of bits [55–57].

Evaluating QAOA expectation values can be as hard as counting optimal solutions 1.2 can all be performed in polynomial time and with polynomially scaling number of bits [55–57]

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:33.044203Z

Source-reported events for the cited work

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Observation 1b6c5732-9cb1-486f-a5da-c1f91b3d6660 · outbound

This paper cites PP is as hard as the polynomial-time hierarchy.SIAM Journal on Computing, 20(5):865–877, 1991.

Evaluating QAOA expectation values can be as hard as counting optimal solutions PP is as hard as the polynomial-time hierarchy.SIAM Journal on Computing, 20(5):865–877, 1991

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:33.027173Z

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Observation ee077af6-6350-485e-af30-3c82b189ab8f · outbound

This paper cites A Quantum Approximate Optimization Algorithm Applied to a Bounded Occurrence Constraint Problem.

Evaluating QAOA expectation values can be as hard as counting optimal solutions A Quantum Approximate Optimization Algorithm Applied to a Bounded Occurrence Constraint Problem

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T14:26:31.818964Z

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Observation 80647356-4212-4ade-8f15-99417200d6c8 · outbound

This paper cites Near-optimal quantum circuit for Grover’s unstructured search using a transverse field.Physical Review A, 95(6):062317, 2017.

Evaluating QAOA expectation values can be as hard as counting optimal solutions Near-optimal quantum circuit for Grover’s unstructured search using a transverse field.Physical Review A, 95(6):062317, 2017

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:33.010152Z

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Observation 7d4d92d3-bf59-497f-9bd9-e6f22c1862d1 · outbound

This paper cites Obstacles to variational quantum optimization from symmetry protection.Physical Review Letters, 125(26):260505, 2020.

Evaluating QAOA expectation values can be as hard as counting optimal solutions Obstacles to variational quantum optimization from symmetry protection.Physical Review Letters, 125(26):260505, 2020

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T14:26:31.829735Z

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Observation 1b05de1b-7c04-4fb8-9bba-226d413cd8a3 · outbound

This paper cites Classical and Quantum Bounded Depth Approximation Algorithms.

Evaluating QAOA expectation values can be as hard as counting optimal solutions Classical and Quantum Bounded Depth Approximation Algorithms

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T14:26:31.834405Z

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Observation 4e03299b-336f-4f2a-97c1-c6b23f4424c8 · outbound

This paper cites Local classical MAX-CUT algorithm outperformsp = 2QAOA on high-girth regular graphs.

Evaluating QAOA expectation values can be as hard as counting optimal solutions Local classical MAX-CUT algorithm outperformsp = 2QAOA on high-girth regular graphs

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:32.982169Z

Source-reported events for the cited work

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Observation c0d9b96f-2a92-49f2-8a64-c0d01f96eea6 · outbound

This paper cites MaxCut quantum approximate optimization algorithm performance guarantees forp>1.

Evaluating QAOA expectation values can be as hard as counting optimal solutions MaxCut quantum approximate optimization algorithm performance guarantees forp>1

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:32.963048Z

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Observation 4b86f367-e9ee-4199-93b9-5dcbe8262833 · outbound

This paper cites Classical algorithms and quantum limitations for maximum cut on high-girth graphs.

Evaluating QAOA expectation values can be as hard as counting optimal solutions Classical algorithms and quantum limitations for maximum cut on high-girth graphs

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-15T14:26:31.849367Z

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Observation a1be3f5e-6fe9-4f50-8ed0-9034431e3599 · outbound

This paper cites Bounds on approximating MaxkXOR with quantum and classical local algorithms.Quantum, 6:757, 2022.

Evaluating QAOA expectation values can be as hard as counting optimal solutions Bounds on approximating MaxkXOR with quantum and classical local algorithms.Quantum, 6:757, 2022

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:32.946250Z

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Observation 37d84448-403c-40d9-a039-e2b343937777 · outbound

This paper cites Analytical framework for quantum alternating operator ansätze.Quantum Science and Technology, 8(1):015017, 2022.

Evaluating QAOA expectation values can be as hard as counting optimal solutions Analytical framework for quantum alternating operator ansätze.Quantum Science and Technology, 8(1):015017, 2022

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:32.930220Z

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Observation d0b7e734-a0e4-41ba-b2a3-b5af90993913 · outbound

This paper cites Expectation values from the single- layer quantum approximate optimization algorithm on Ising problems.Quantum Science and Technology, 7(4):045036, 2022.

Evaluating QAOA expectation values can be as hard as counting optimal solutions Expectation values from the single- layer quantum approximate optimization algorithm on Ising problems.Quantum Science and Technology, 7(4):045036, 2022

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:32.915281Z

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Observation 5b97e1f8-caba-4e11-a32d-e4b30f9683e4 · outbound

This paper cites The Quantum Approximate Optimization Algorithm at High Depth for MaxCut on Large-Girth Regular Graphs and the Sherrington-Kirkpatrick Model.

Evaluating QAOA expectation values can be as hard as counting optimal solutions The Quantum Approximate Optimization Algorithm at High Depth for MaxCut on Large-Girth Regular Graphs and the Sherrington-Kirkpatrick Model

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:32.899650Z

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Observation ec1f0e30-f922-448f-8048-ed7f3528475a · outbound

This paper cites Predicting parameters for the Quantum Approximate Optimization Algorithm for MAX-CUT from the infinite-size limit.

Evaluating QAOA expectation values can be as hard as counting optimal solutions Predicting parameters for the Quantum Approximate Optimization Algorithm for MAX-CUT from the infinite-size limit

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-15T14:26:31.871575Z

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Observation 5fd5f979-458a-43d6-9119-0632c8bdb2a8 · outbound

This paper cites The quantum approximate optimization algorithm and the Sherrington-Kirkpatrick model at infinite size.Quantum, 6:759, 2022.

Evaluating QAOA expectation values can be as hard as counting optimal solutions The quantum approximate optimization algorithm and the Sherrington-Kirkpatrick model at infinite size.Quantum, 6:759, 2022

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:32.882754Z

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Observation 802932a1-a922-413c-8874-5a9ef9195fd9 · outbound

This paper cites Performance and limitations of the QAOA at constant levels on large sparse hypergraphs and spin glass models.

Evaluating QAOA expectation values can be as hard as counting optimal solutions Performance and limitations of the QAOA at constant levels on large sparse hypergraphs and spin glass models

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:32.864030Z

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Observation 6d249d8a-3b53-4dd4-bc7c-65912480186a · outbound

This paper cites Spin–boson mapping of the quantum approximate optimization algorithm.

Evaluating QAOA expectation values can be as hard as counting optimal solutions Spin–boson mapping of the quantum approximate optimization algorithm

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:32.847523Z

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Observation 99151de5-e4e4-4a0a-ae4b-d6963e04f409 · outbound

This paper cites Training variational quantum algorithms is NP-hard.

Evaluating QAOA expectation values can be as hard as counting optimal solutions Training variational quantum algorithms is NP-hard

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:32.831155Z

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Observation c1a61c7b-23dc-4985-8456-0c0fe1149aa1 · outbound

This paper cites Bremner, Richard Jozsa, and Dan J.

Evaluating QAOA expectation values can be as hard as counting optimal solutions Bremner, Richard Jozsa, and Dan J

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:32.814018Z

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Observation a6ce05ba-734b-43d9-94db-ef13615dfccd · outbound

This paper cites A sharp interaction-degree threshold for simulating QAOA.

Evaluating QAOA expectation values can be as hard as counting optimal solutions A sharp interaction-degree threshold for simulating QAOA

Reference 35

Resolution: verified exact; local_arxiv, observed 2026-08-15T14:26:32.322631Z

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Observation b527dd36-6fad-48d9-836e-ff7843e0013c · outbound

This paper cites Average-case hardness of estimating probabilities of random quantum circuits with a linear scaling in the error exponent.

Evaluating QAOA expectation values can be as hard as counting optimal solutions Average-case hardness of estimating probabilities of random quantum circuits with a linear scaling in the error exponent

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-15T14:26:31.906387Z

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Observation 5d1e3163-816c-4835-bf4b-df67b6fa7ca8 · outbound

This paper cites How many qubits are needed for quantum computational supremacy?Quantum, 4:264, 2020.

Evaluating QAOA expectation values can be as hard as counting optimal solutions How many qubits are needed for quantum computational supremacy?Quantum, 4:264, 2020

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:32.796376Z

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Observation 061c71b4-6702-484a-98a8-ebc195507730 · outbound

This paper cites Quantum computational supremacy.

Evaluating QAOA expectation values can be as hard as counting optimal solutions Quantum computational supremacy

Reference 38

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Observation e5cd2f7f-2dd7-4a1a-8ea5-530ab2d9eff7 · outbound

This paper cites Classical algorithms for quantum mean values.

Evaluating QAOA expectation values can be as hard as counting optimal solutions Classical algorithms for quantum mean values

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:32.766562Z

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Observation 8082621a-a273-4b21-a191-562b09ca910f · outbound

This paper cites What do QAOA energies reveal about graphs?.

Evaluating QAOA expectation values can be as hard as counting optimal solutions What do QAOA energies reveal about graphs?

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-15T14:26:31.926722Z

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Observation c7de68ab-4f79-4e48-a3b4-60fe428b0457 · outbound

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

Evaluating QAOA expectation values can be as hard as counting optimal solutions On the representation of Boolean and real functions as Hamiltonians for quantum computing

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:32.748885Z

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Observation 6807ddc4-87b8-44a8-b640-befb26239415 · outbound

This paper cites Counting with the quantum alternating operator ansatz.

Evaluating QAOA expectation values can be as hard as counting optimal solutions Counting with the quantum alternating operator ansatz

Reference 42

Resolution: verified exact; local_arxiv, observed 2026-08-15T14:26:32.255463Z

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Observation b68af2dc-dfd4-4ec3-92f9-6de47c40bbfc · outbound

This paper cites The QAOA on the ring of disagrees.

Evaluating QAOA expectation values can be as hard as counting optimal solutions The QAOA on the ring of disagrees

Reference 43

Resolution: verified exact; local_arxiv, observed 2026-08-15T14:26:32.231032Z

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Observation 9f5a88d8-cbd3-48e5-a045-1c1c8650948f · outbound

This paper cites A Machine-Verified Proof of a Quantum-Optimization Conjecture.

Evaluating QAOA expectation values can be as hard as counting optimal solutions A Machine-Verified Proof of a Quantum-Optimization Conjecture

Reference 44

Resolution: verified exact; local_arxiv, observed 2026-08-15T14:26:32.206792Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 22d2a918-cebf-43cc-a824-d62c7796f169 · outbound

This paper cites Parameter setting in quantum approximate optimization of weighted problems.Quantum, 8:1231, 2024.

Evaluating QAOA expectation values can be as hard as counting optimal solutions Parameter setting in quantum approximate optimization of weighted problems.Quantum, 8:1231, 2024

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-15T14:26:31.952451Z

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Observation 753d48df-7498-406b-b8c7-bbade394cba1 · outbound

This paper cites Lower bounding the MaxCut of high-girth 3-regular graphs using the QAOA.arXiv preprint arXiv:2503.12789, 2025.

Evaluating QAOA expectation values can be as hard as counting optimal solutions Lower bounding the MaxCut of high-girth 3-regular graphs using the QAOA.arXiv preprint arXiv:2503.12789, 2025

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-15T14:26:31.957436Z

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Unavailable: canonical work link unavailable.

Observation 565c6f5f-2ac2-4e0b-b258-3c739a71d5fc · outbound

This paper cites Characterizing local noise in QAOA circuits.IOP SciNotes, 1(2):025208, 2020.

Evaluating QAOA expectation values can be as hard as counting optimal solutions Characterizing local noise in QAOA circuits.IOP SciNotes, 1(2):025208, 2020

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:32.711599Z

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Observation 411c2c85-1172-4e2b-af91-cc9d28d577bc · outbound

This paper cites Hybrid quantum-classical algorithms for approximate graph coloring.Quantum, 6:678, 2022.

Evaluating QAOA expectation values can be as hard as counting optimal solutions Hybrid quantum-classical algorithms for approximate graph coloring.Quantum, 6:678, 2022

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:32.693874Z

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Observation 5b363ad1-b778-47be-975d-1139f442cc2a · outbound

This paper cites Hodson, Bhuvanesh Sundar, Stephen Jeffrey, Yuki Yamaguchi, Dennis Feng, Filip B.

Evaluating QAOA expectation values can be as hard as counting optimal solutions Hodson, Bhuvanesh Sundar, Stephen Jeffrey, Yuki Yamaguchi, Dennis Feng, Filip B

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:32.674627Z

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Observation dab7a6f6-2bad-4e2e-a9b7-f799a35a5c4b · outbound

This paper cites Iterative quantum algorithms for maximum independent set.

Evaluating QAOA expectation values can be as hard as counting optimal solutions Iterative quantum algorithms for maximum independent set

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:32.658764Z

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Observation 9a508bb3-a257-4941-b15d-54200a15364b · outbound

This paper cites Quantum-informed recursive optimization algorithms.PRX Quantum, 5(2):020327, 2024.

Evaluating QAOA expectation values can be as hard as counting optimal solutions Quantum-informed recursive optimization algorithms.PRX Quantum, 5(2):020327, 2024

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:32.643191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0149c67f-e5a4-4bc4-b92b-937d91e2e66f · outbound

This paper cites Brady and Stuart Hadfield.

Evaluating QAOA expectation values can be as hard as counting optimal solutions Brady and Stuart Hadfield

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:32.624574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 99f5be1d-3261-457a-ba87-15f2bdc6aac6 · outbound

This paper cites A scalable quantum-enhanced greedy algorithm for maximum independent set problems.arXiv preprint arXiv:2601.21923, 2026.

Evaluating QAOA expectation values can be as hard as counting optimal solutions A scalable quantum-enhanced greedy algorithm for maximum independent set problems.arXiv preprint arXiv:2601.21923, 2026

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-15T14:26:31.992401Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 54044a2b-938f-432e-beb4-79c711576729 · outbound

This paper cites Extendingrelax-and-roundcombinatorialoptimization solvers with quantum correlations.Physical Review A, 109(1):012429, 2024.

Evaluating QAOA expectation values can be as hard as counting optimal solutions Extendingrelax-and-roundcombinatorialoptimization solvers with quantum correlations.Physical Review A, 109(1):012429, 2024

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:32.606394Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 012cda86-69a6-4d71-ae71-1c067d461f67 · outbound

This paper cites A multilevel approach for solving large-scale QUBO problems with noisy hybrid quantum approximate optimization.

Evaluating QAOA expectation values can be as hard as counting optimal solutions A multilevel approach for solving large-scale QUBO problems with noisy hybrid quantum approximate optimization

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:32.587958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e0ae0e49-c4da-4741-9928-607b2d71a066 · outbound

This paper cites Lenstra, Hendrik W.

Evaluating QAOA expectation values can be as hard as counting optimal solutions Lenstra, Hendrik W

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:32.570407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 493b7c0b-897c-4a80-aec3-ba4ac1c6a9e8 · outbound

This paper cites Algorithms in real algebraic geometry.

Evaluating QAOA expectation values can be as hard as counting optimal solutions Algorithms in real algebraic geometry

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:32.553488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 69562b21-9ea4-4de7-b4bd-047369fe9646 · outbound

This paper cites Springer Science & Business Media, 2013.

Evaluating QAOA expectation values can be as hard as counting optimal solutions Springer Science & Business Media, 2013

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:26:32.535266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers