The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Grover Mixers for QAOA: Shifting Complexity from Mixer Design to State Preparation
Open accessAbstract
We propose GM-QAOA, a variation of the Quantum Alternating Operator Ansatz (QAOA) that uses Grover-like selective phase shift mixing operators. GM-QAOA works on any NP optimization problem for which it is possible to efficiently prepare an equal superposition of all feasible solutions; it is designed to perform particularly well for constraint optimization problems, where not all possible variable assignments are feasible solutions. GM-QAOA has the following features: (i) It is not susceptible to Hamiltonian Simulation error (such as Trotterization errors) as its operators can be implemented exactly using standard gate sets and (ii) Solutions with the same objective value are always sampled with the same amplitude. We illustrate the potential of GM-QAOA on several optimization problem classes: for permutation-based optimization problems such as the Traveling Salesperson Problem, we present an efficient algorithm to prepare a superposition of all possible permutations of$n$numbers, defined on O (n2) qubits; for the hard constraint k-Vertex-Cover problem, and for an application to Discrete Portfolio Rebalancing, we show that GM-QAOA outperforms existing QAOA approaches.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“Besides the mentioned ansatz search, as the driving force behind ansatz adaptation, there is also the possibility of having these adaptations as a byproduct, like it happens, e.g., within the Quantum Alternating Operator Ansatz where the primary goal is to find a mixing Hamiltonian that ensures to stay in the feasible subspace [11, 58, 95, 112, 116, 117, 120, 127].”
From Quantum Combinatorial Optimization in the NISQ Era: A Systematic Mapping Study (2024) · cites this paper as [11]
The cited source says what the claim attributes to it.
In the citing paper
“Another possibility is to shift the complexity to the generation of the initial state [11], where the idea is to further utilize the state preparation circuit, which creates an equal superposition of all feasible states, for the design of the mixing Hamiltonian.”
From Quantum Combinatorial Optimization in the NISQ Era: A Systematic Mapping Study (2024) · cites this paper as [11]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A fast quantum mechanical algorithm for database search
Grover, L. K. (1996) · Proceedings of the twenty-eighth annual ACM symposium on Theory of computing - STOC '96 · doi:10.1145/237814.237866 · Cited by 8,830
Lloyd, S. (1996) · Science · doi:10.1126/science.273.5278.1073 · Cited by 3,088 · cited 5×
“It follows as a benefit that GM-QAOA does not suffer from numerical errors that Hamiltonian simulation methods, such as Trotterization [10], Local Combination of Unitaries [11], or Quantum Walk based simulation [12], introduce.”
MethodInfluentialAssignment Problems and the Location of Economic Activities
Koopmans, T., & Beckmann, M. (1957) · Econometrica · doi:10.2307/1907742 · Cited by 2,567
From the Quantum Approximate Optimization Algorithm to a Quantum Alternating Operator Ansatz
Hadfield, S., Wang, Z., O’Gorman, B., Rieffel, E. G., Venturelli, D., & Biswas, R. (2019) · Algorithms · doi:10.3390/a12020034 · Cited by 772
Simulating Hamiltonian Dynamics with a Truncated Taylor Series
Berry, D. W., Childs, A. M., Cleve, R., Kothari, R., & Somma, R. D. (2015) · Physical Review Letters · doi:10.1103/physrevlett.114.090502 · Cited by 742
Quantum Simulation of Electronic Structure with Linear Depth and Connectivity
Kivlichan, I. D., McClean, J., Wiebe, N., Gidney, C., Aspuru-Guzik, A., Chan, G. K.-L., & Babbush, R. (2018) · Physical Review Letters · doi:10.1103/physrevlett.120.110501 · Cited by 431
Hamiltonian Simulation with Nearly Optimal Dependence on all Parameters
Berry, D. W., Childs, A. M., & Kothari, R. (2015) · 2015 IEEE 56th Annual Symposium on Foundations of Computer Science · doi:10.1109/focs.2015.54 · Cited by 372
Average-Case Complexity Versus Approximate Simulation of Commuting Quantum Computations
Bremner, M. J., Montanaro, A., & Shepherd, D. J. (2016) · Physical Review Letters · doi:10.1103/physrevlett.117.080501 · Cited by 299
Fixed-Point Quantum Search with an Optimal Number of Queries
Yoder, T. J., Low, G. H., & Chuang, I. L. (2014) · Physical Review Letters · doi:10.1103/physrevlett.113.210501 · Cited by 267
Grover, L. K. (2005) · Physical Review Letters · doi:10.1103/physrevlett.95.150501 · Cited by 209
Research works printed but not linked: 10
- hodson, 2019, Portfolio rebalancing experiments using the Quantum Alternating Operator Ansatz
- brassard, 2000, Quantum Amplitude Amplification and Estimation
- sundar, 2019, A quantum algorithm to count weighted ground states of classical spin Hamiltonians
- farhi, 2014, A Quantum Approximate Optimization Algorithm
- farhi, 2014, A Quantum Approximate Optimization Algorithm Applied to a Bounded Occurrence Constraint Problem
- cook, 0, The Quantum Alternating Operator Ansatz on Max-k Vertex Cover, IEEE International Conference on Quantum Computing & Engineering QCE'20
- o'gorman, 2019, Generalized swap networks for near-term quantum computing
- gidney, 2015, Constructing large controlled nots
- gidney, 2015, Using quantum gates instead of ancilla bits
- gidney, 2015, Constructing large increment gates
Cited by
Sorted by citations
Items 101–123 of 123
Wang, Z., He, Y., Luan, T., & Long, Y. (2025) · Entropy · doi:10.3390/e27030240 · Cited by 1
Lee, X., & Lau, H. C. (2025) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce65121.2025.00213 · Cited by 2
Entropic property of randomized QAOA circuits
Chernyavskiy, A. Y., Bantysh, B. I., & Bogdanov, Y. I. (2023) · Laser Physics Letters · doi:10.1088/1612-202x/ad12ad · Cited by 3
Costa, P. C. S., Morales, M. E. S., An, D., & Sanders, Y. R. (2025) · Physical Review A · doi:10.1103/hm1z-z9p2
Space-Efficient Embedding of the Clique Cover Problem for Quantum Optimization
Bakó, B., Nagy, D., Hága, P., Kallus, Z., & Zimborás, Z. (2023) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce57702.2023.10306
Fully Integrated Quantum Method for Classical Register Allocation in LLVM
Chichereau, B., Vialle, S., & Carribault, P. (2024) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce60285.2024.10295
Feed-Forward Probabilistic Error Cancellation with Noisy Recovery Gates
Kurosawa, L., Saito, Y., Lee, X., Yan, X., Xie, N., Cai, D., Shin, J., & Asai, N. (2024) · IEEE International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC) · doi:10.1109/mcsoc64144.2024.00081 · Cited by 1
Random Sampling of Permutations Using Quantum Circuits
Adhikari, B. (2025) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2025.3591735
Quantum optimization techniques and applications
Lee, E., & Kim, S. (2025) · Advances in computers · doi:10.1016/bs.adcom.2025.06.001 · Cited by 1
A Statistical Approach to Analyzing the Quantum Alternating Operator Ansatz with Grover Mixer
Bridi, G. A., & Marquezino, F. D. L. (2025) · doi:10.5753/ctd.2025.8022
Counting with the quantum alternating operator ansatz
Drapeau, J., Banerjee, S., & Kourtis, S. (2026) · Quantum Science and Technology · doi:10.1088/2058-9565/ae57d6 · Cited by 2
Studies of quantum combinatorial optimization algorithms: <?A3B2 pi6?>a review
Li, Z., Wu, Y., Zhang, R., Wen, J., Wang, T., & Wang, C. (2025) · Chinese Science Bulletin (Chinese Version) · doi:10.1360/csb-2025-5565
A Quantum Search Method for Quadratic and Multidimensional Knapsack Problems
Wilkening, S., Lefterovici, A.-I., Binkowski, L., Funck, M., Perk, M., Karimov, R., Fekete, S. P., & Osborne, T. J. (2025) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce65121.2025.00241
A General Quantum Circuit Design Method for Preparation of W State
Lee, S., & Tseng, C. (2025) · IEEE Global Conference on Consumer Electronics (GCCE) · doi:10.1109/gcce65946.2025.11275181
Quantum Optimization for Network Capacity Expansion of Microgrid Models via Grover-Enhanced QAOA
Pisaneschi, G., Poli, D., & Fioriti, D. (2026) · 2026 IEEE 23rd Mediterranean Electrotechnical Conference (MELECON) · doi:10.1109/melecon64486.2026.11418844
Quantum Heuristic Approach to Vehicle Routing Problem
Kim, J. S., Lee, D., & Ahn, C. W. (2026) · Mathematics · doi:10.3390/math14061026
Image thresholding using quantum approximate optimization algorithm and Grover’s algorithm
Pachuau, J. L., Singh, N. B., & Saha, A. K. (2026) · The Journal of Supercomputing · doi:10.1007/s11227-026-08618-y
Pandey, S. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-0156-1.ch006
Fair Sampling with Temperature-Targeted QAOA Based on Quantum–Classical Correspondence Theory
Abe, T., & Tanaka, S. (2026) · Journal of the Physical Society of Japan · doi:10.7566/jpsj.95.083001
A review of variational quantum algorithms: Insights into fault-tolerant quantum computing
Wang, Z., Huang, J., Ye, R., Li, Q., Ding, Q.-M., Huang, Y., Zhang, T., Zeng, Y., Gao, J., Yuan, X., et al. (2026) · Frontiers of Physics · doi:10.15302/frontphys.2027.023301 · Cited by 7
Quantum Key Search Algorithms under Side-channel AttackPreprint
Yang, Y., Shi, J., Zhang, H., Li, H., Fu, X., Yang, Y., Zhu, Y., & Zhou, Y. (2026) · Research Square · doi:10.21203/rs.3.rs-10245840/v1
Constraint-preserving QAOA for multi-agent task allocation
Yu, H., Kang, S.-W., Oh, K.-K., & Ahn, H.-S. (2026) · Conference on Control Technology and Applications · doi:10.1109/ccta62090.2026.11684036
Metadata
- Authors
- Bartschi, A., & Eidenbenz, S.
- Affiliations
- Los Alamos National Laboratory (US)
- ORCID
- Eidenbenz, Stephan
- Venue
- 2020 IEEE International Conference on Quantum Computing and Engineering (QCE)
- Publisher
- IEEE
- Published
- 2020-10
- Pages
- 72-82
- OpenAlex
- W3029819693
- Cited by
- 122
- References
- 21
- Citations by year
- 2026: 11 · 2025: 27 · 2024: 25 · 2023: 29 · 2022: 17 · 2021: 10 · 2020: 3
- FWCI
- 5.55
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE International Conference on Quantum Computing and Engineering (QCE) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum many-body systems
- Keywords
- Quantum Alternating Operator Ansatz, mixer design, quantum state preparation, constraint optimization, traveling salesman problem
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
