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We develop new approximate compilation schemes that significantly reduce the expense of compiling the Quantum Approximate Optimization Algorithm (QAOA) for solving the Max-Cut problem.
Quantum approximate optimization with a trapped-ion quantum simulator
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The quantum approximate optimization algorithm needs to see the whole graph: A typical case
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The Design of Approximation Algorithms
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Graph sparsification by effective resistances
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Quantum speedups for convex dynamic programming
D. Sutter, G. Nannicini, T. Sutter, and S. Woerner · 2011
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Nonequilibrium dynamics of arbitrary-range Ising models with decoherence: An exact analytic solution
M. Foss-Feig, K. Hazzard, J. Bollinger, and A. Rey · 2013
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A. Lucas · 2014
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A variational eigenvalue solver on a photonic quantum processor
A. Peruzzo, J. McClean, P. Shadbolt, M.-H. Yung, X.-Q. Zhou, P. Love, A. Aspuru-Guzik, and J. O’Brien · 2014
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Quantum spin dynamics and entanglement generation with hundreds of trapped ions
J. Bohnet, B. Sawyer, J. Britton, M. Wall, A. Rey, M. Foss-Feig, and J. Bollinger · 2016
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What works best when? A systematic evaluation of heuristics for Max-Cut and QUBO
I. Dunning, S. Gupta, and J. Silberholz · 2017
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Quantum computing in the NISQ era and beyond
J. Preskill · 2018
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Multistart methods for quantum approximate optimization
R. Shaydulin, I. Safro, and J. Larson · 2019
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A quantum interior point method for LPs and SDPs
I. Kerenidis and A. Prakash · 2020
Quantum approximate optimization algorithm with sparsified phase operator
X. Liu, R. Shaydulin, and I. Safro · 2022
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Scaling quantum approximate optimization on near-term hardware
P. Lotshaw, T. Nguyen, A. Santana, A. McCaskey, R. Herrman, J. Ostrowski, G. Siopsis, and T. Humble · 2022
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Fast quantum subroutines for the simplex method
G. Nannicini · 2022
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Generating target graph couplings for the quantum approximate optimization algorithm from native quantum hardware couplings
J. Rajakumar, J. Moondra, B. Gard, S. Gupta, and C. Herold · 2022
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On the emerging potential of quantum annealing hardware for combinatorial optimization
B. Tasseff, T. Albash, Z. Morrell, M. Vuffray, A. Lokhov, S. Misra, and C. Coffrin · 2022
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Quantum approximate optimization algorithm: Performance, mechanism, and implementation on near-term devices
L. Zhou, S.-T. Wang, S. Choi, H. Pichler, and M. Lukin · 2020
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Power-optimal, stabilized entangling gate between trapped-ion qubits
R. Blümel, N. Grzesiak, N. Pisenti, K. Wright, and Y. Nam · 2021
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High-fidelity Bell-state preparation with Ca + 40 {}^{40}\mathrm{Ca}^{+} optical qubits
C. Clark, H. Tinkey, B. Sawyer, A. Meier, K. Burkhardt, C. Seck, C. Shappert, N. Guise, C. Volin, S. Fallek, H. Hayden, W. Rellergert, and K. Brown · 2021
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Warm-starting quantum optimization
D. Egger, J. Mareček, and S. Woerner · 2021
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Formulating and solving routing problems on quantum computers
S. Harwood, C. Gambella, D. Trenev, A. Simonetto, D. Bernal, and D. Greenberg · 2021
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Empirical performance bounds for quantum approximate optimization
P. Lotshaw, T. Humble, R. Herrman, J. Ostrowski, and G. Siopsis · 2021
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Quantum control methods for robust entanglement of trapped ions
C. Valahu, I. Apostolatos, S. Weidt, and W. Hensinger · 2022
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Quantum interior point methods for semidefinite optimization
B. Augustino, G. Nannicini, T. Terlaky, and L. Zuluaga · 2023
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Modeling noise in global Mølmer-Sørensen interactions applied to quantum approximate optimization
P. Lotshaw, K. Battles, B. Gard, G. Buchs, T. Humble, and C. Herold · 2023
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Approximate Boltzmann distributions in quantum approximate optimization
P. Lotshaw, G. Siopsis, J. Ostrowski, R. Herrman, R. Alam, S. Powers, and T. Humble · 2023
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Parameter transfer for quantum approximate optimization of weighted MaxCut
R. Shaydulin, P. Lotshaw, J. Larson, J. Ostrowski, and T. Humble · 2023
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Real-time quantum error correction beyond break-even
V. Sivak, A. Eickbusch, B. Royer, S. Singh, I. Tsioutsios, S. Ganjam, A. Miano, B. Brock, A. Ding, L. Frunzio, S. Girvin, R. Schoelkopf, and M. Devoret · 2023
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Warm-started QAOA with custom mixers provably converges and computationally beats Goemans–Williamson’s Max-Cut at low circuit depths
R. Tate, J. Moondra, B. Gard, G. Mohler, and S. Gupta · 2023
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Strategies for running the QAOA at hundreds of qubits
B. Augustino, M. Cain, E. Farhi, S. Gupta, S. Gutmann, D. Ranard, E. Tang, and K. Van Kirk · 2024
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Elementary proof of QAOA convergence
L. Binkowski, G. Koßmann, T. Ziegler, and R. Schwonnek · 2024
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Logical quantum processor based on reconfigurable atom arrays
D. Bluvstein, S. Evered, A. Geim, S. Li, H. Zhou, T. Manovitz, S. Ebadi, M. Cain, M. Kalinowski, D. Hangleiter, et al · 2024
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Exactly solvable model of light-scattering errors in quantum simulations with metastable trapped-ion qubits
P. Lotshaw, B. Sawyer, C. Herold, and G. Buchs · 2024
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Performant near-term quantum combinatorial optimization
T. Morris and P. Lotshaw · 2024
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Large-scale quantum approximate optimization on nonplanar graphs with machine learning noise mitigation
S. Sack · 2024
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Evidence of scaling advantage for the quantum approximate optimization algorithm on a classically intractable problem
R. Shaydulin, C. Li, S. Chakrabarti, M. DeCross, D. Herman, N. Kumar, J. Larson, D. Lykov, P. Minssen, Y. Sun, et al · 2024
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Red-QAOA: Efficient variational optimization through circuit reduction
M. Wang, B. Fang, A. Li, and P. Nair · 2024
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Noisy quantum approximation optimization algorithm for solving MaxCut problem
D. Liu, J. Li, X. Chen, and N. Jiang · 2025
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Source code for “Promise of graph sparsification and decomposition for noise reduction in QAOA: Analysis for trapped-ion compilations”, 2026
J. Moondra, P. Lotshaw, G. Mohler, and S. Gupta · 2026
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Expectation values from the single-layer quantum approximate optimization algorithm on Ising problems
A. Ozaeta, W. van Dam, and P. McMahon · 2058
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