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Demonstration of a Scaling Advantage for a Quantum Annealer over Simulated Annealing
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Benchmarks of a quantum information processor establish, for the first time, an advantage for a quantum annealer over classical simulated annealing, an important milestone in the development of quantum optimizers and in the journey towards demonstrating a quantum speedup over classical computers.
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“A comparison with SA, spin-vector-monte-carlo [215], and simulated QA suggests that QA possesses the best scaling properties [7].”
From Quantum Combinatorial Optimization in the NISQ Era: A Systematic Mapping Study (2024) · cites this paper as [7]
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“QA shows superior scaling properties compared to SA, where both are significantly inferior to simulated QA [7].”
From Quantum Combinatorial Optimization in the NISQ Era: A Systematic Mapping Study (2024) · cites this paper as [7]
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Cites
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“This advance allows us furthermore to demonstrate the first certifiable observation of an algorithmic scaling advantage obtained using quantum annealing hardware over an important general purpose classical algorithm, namely, over simulated annealing with single-spin updates (SA) [36].”
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MethodAlgorithms for quantum computation: discrete logarithms and factoring
Shor, P. W. (2002) · Proceedings 35th Annual Symposium on Foundations of Computer Science · doi:10.1109/sfcs.1994.365700 · Cited by 8,713 · cited 1×
“The holy grail is an exponential speedup, such as that believed to be attainable with Shor’s algorithm for factoring integers [1], or with the simulation of quantum systems [2–4].”
MethodQuantum Computation and Quantum Information: 10th Anniversary Edition
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Simulating physics with computers
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“[3] Seth Lloyd, “Universal quantum simulators,” Science”
Collective Monte Carlo Updating for Spin Systems
Ulli Wolff, A. (1989) · Physical Review Letters · doi:10.1103/physrevlett.62.361 · Cited by 2,564 · cited 1×
“A sweep involves performing a singleWolff cluster update [87] along the imaginary-time direction for each spin.”
Probing many-body dynamics on a 51-atom quantum simulator
Bernien, H., Schwartz, S., Keesling, A., Levine, H., Omran, A., Pichler, H., Choi, S., Zibrov, A. S., Endres, M., Greiner, M., et al. (2017) · Nature · doi:10.1038/nature24622 · Cited by 2,460 · cited 1×
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Research works printed but not linked: 2
- P. W. Shor, Proceedings of the 35th Annual Symposium on Foundations of Computer Science, 1994
- F. Hamze, 2004, UAI
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Items 101–200 of 234
The Mathematics of Quantum-Enabled Applications on the D-Wave Quantum Computer
Berwald, J. (2019) · Notices of the American Mathematical Society · doi:10.1090/noti1893 · Cited by 11
Ayodele, M., Allmendinger, R., López‐Ibáñez, M., & Parizy, M. (2022) · Genetic and Evolutionary Computation Conference (GECCO) · doi:10.1145/3512290.3528698 · Cited by 13
Quantum machine learning early opportunities for the energy industry: a scoping review
Strata, F., Migliori, L., Gebran, N., Guarino, N., Colombo, G. C., Pezzuolo, S., & Luzietti, E. (2025) · Frontiers in Quantum Science and Technology · doi:10.3389/frqst.2025.1653104 · Cited by 13
Boosting quantum amplitude exponentially in variational quantum algorithms
Kyaw, T. H., Soley, M. B., Allen, B., Bergold, P., Sun, C., Batista, V. S., & Aspuru‐Guzik, A. (2023) · Quantum Science and Technology · doi:10.1088/2058-9565/acf4ba · Cited by 17
Frustrated Ising Model on D-wave Quantum Annealing Machine
Park, H., & Lee, H. (2022) · Journal of the Physical Society of Japan · doi:10.7566/jpsj.91.074001 · Cited by 12
Mehta, V., Jin, F., Raedt, H. A. D., & Michielsen, K. F. L. (2022) · Physical Review A · doi:10.1103/physreva.105.062406 · Cited by 12
“de; Corresponding author algorithms [20, 33, 40].”
Dynamics of simulated quantum annealing in random Ising chains
Mbeng, G. B., Privitera, L., Arceci, L., & Santoro, G. E. (2019) · Physical review. B./Physical review. B · doi:10.1103/physrevb.99.064201 · Cited by 11
Disorder-assisted graph coloring on quantum annealers
Więckowski, A., Deffner, S., & Gardas, B. (2019) · Physical Review A · doi:10.1103/physreva.100.062304 · Cited by 11
“[20] Albash, T.”
BackgroundA Probabilistic Approach to Quantum Inspired Algorithms
Chowdhury, S., Datta, S., & Camsari, K. Y. (2019) · Technical digest - International Electron Devices Meeting/Technical digest · doi:10.1109/iedm19573.2019.8993655 · Cited by 11
Classical Simulation and Theory of Quantum Annealing in a Thermal Environment
Oshiyama, H., Suzuki, S., & Shibata, N. (2022) · Physical Review Letters · doi:10.1103/physrevlett.128.170502 · Cited by 11
Chook -- A comprehensive suite for generating binary optimization problems with planted solutionsPreprint
Perera, D., Akpabio, I., Hamze, F., Mandrà, S., Rose, N., Aramon, M., & Katzgraber, H. G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.14344 · Cited by 16
Towards an Automatic Framework for Solving Optimization Problems with Quantum Computers
Volpe, D., Quetschlich, N., Graziano, M., Turvani, G., & Wille, R. (2024) · IEEE International Conference on Quantum Software (QSW) · doi:10.1109/qsw62656.2024.00019 · Cited by 10
Advantage of Pausing: Parameter Setting for Quantum Annealers
Izquierdo, Z. G., Grabbe, S. R., Idris, H., Wang, Z., Marshall, J., & Rieffel, E. G. (2022) · Physical Review Applied · doi:10.1103/physrevapplied.18.054056 · Cited by 12
4-Clique network minor embedding for quantum annealers
Pelofske, E. (2024) · Physical Review Applied · doi:10.1103/physrevapplied.21.034023 · Cited by 11
“Quantum annealing has experimentally been shown to be competitive for good heuristic sampling of combinatorial optimization problems [11, 12] and simulation of frustrated magnetic systems [13, 14].”
BackgroundDemonstration of Algorithmic Quantum Speedup for an Abelian Hidden Subgroup Problem
Singkanipa, P., Kasatkin, V., Zhou, Z., Quiroz, G., & Lidar, D. A. (2025) · Physical Review X · doi:10.1103/physrevx.15.021082 · Cited by 10
“A variety of such demonstrations have been reported [15–25], but all are subject to various caveats such as relying on computational complexity conjectures or being limited to an advantage over a restricted set of classical algorithms.”
Self-citationYacoby, R., Schaar, N., Kellerhals, L., Raz, O., Hermelin, D., & Pugatch, R. (2022) · Physical review. E · doi:10.1103/physreve.105.035305 · Cited by 11
High-Performance and Reliable Probabilistic Ising Machine Based on Simulated Quantum Annealing
Raimondo, E., Garzón, E., Shao, Y., Grimaldi, A., Chiappini, S., Tomasello, R., Dávila, N., Katine, J. A., Carpentieri, M., Chiappini, M., et al. (2025) · Physical Review X · doi:10.1103/pcmz-w776 · Cited by 9
“1, +1} 𝐽𝐽 𝑖𝑖𝑖𝑖 𝑖𝑖 There are several promising paradigms exploiting the hardware implementation of IMs, including quantum annealers [6,7], simulated bifurcation machines [8], coherent [9,10] and oscillator-based IMs [11,12], and probabilistic IMs (PIMs).”
Unconventional quantum annealing methods for difficult trial problems
Tang, Z., & Kapit, E. (2021) · Physical Review A · doi:10.1103/physreva.103.032612 · Cited by 8
Quantum Algorithms for the Physical Layer: Potential Applications to Physical Layer Security
Matsumine, T., Ochiai, H., & Shikata, J. (2025) · IEEE Access · doi:10.1109/access.2025.3528443 · Cited by 9
“A substantial number of proof-of-principle demonstrations have been conducted [43], [45], [46], [47], [48], [49], [50], [51], and currently, the D-Wave quantum annealer has over 5,000 qubits [52].”
All-optical interferometer-based Ising machine
Gao, Y., Luo, Q., Lin, H., Fu, W., & Danner, A. J. (2025) · Optica · doi:10.1364/optica.557856 · Cited by 8
Counterdiabatic Driving with Performance Guarantees
Finžgar, J. R., Notarnicola, S., Cain, M., Lukin, M. D., & Sels, D. (2025) · Physical Review Letters · doi:10.1103/pqhl-nbtk · Cited by 10
A quantum computing concept for 1-D elastic wave simulation with exponential speedup
Schade, M., Bösch, C., Hapla, V., & Fichtner, A. (2024) · Geophysical Journal International · doi:10.1093/gji/ggae160 · Cited by 18
“Finally, there are quantum annealers (QAs) or adiabatic quantum computers (Albash and Lidar (2018a)), which differ from gate-based QCs. QAs are capable of solving quadratic optimization problems and their application in geoscience has been investigated in a handful of studies (O’Malley (2018);…”
BackgroundStochastic Simulated Quantum Annealing for Fast Solution of Combinatorial Optimization Problems
Onizawa, N., Sasaki, R., Shin, D., Gross, W. J., & Hanyu, T. (2024) · IEEE Access · doi:10.1109/access.2024.3431540 · Cited by 8
“The SA process starts with an initial temperature of 1,000 and ends with a final temperature of 0.1.”
BackgroundInfluentialLocalization transition induced by programmable disorder
Filho, J. L. C. D. C., Izquierdo, Z. G., Saguia, A., Albash, T., Hen, I., & Sarandy, M. S. (2022) · Physical review. B./Physical review. B · doi:10.1103/physrevb.105.134201 · Cited by 8
Self-citationA global Lyapunov function for the coherent Ising machine
Roychowdhury, J. (2022) · Nonlinear Theory and Its Applications IEICE · doi:10.1587/nolta.13.227 · Cited by 8
“While both have been portrayed as “genuinely quantum” Ising machines, only D-Wave’s machine has so far been shown to feature quantum advantage [7, 8].”
BackgroundQuantum adiabatic optimization without heuristicsPreprint
Jarret, M., Lackey, B., Liu, A., & Wan, K. (2018) · arXiv.org · doi:10.48550/arxiv.1810.04686 · Cited by 15
Quantum circuit compilation with quantum computers
Rattacaso, D., Jaschke, D., Ballarin, M., Siloi, I., & Montangero, S. (2025) · Physical Review Research · doi:10.1103/hcz4-nv2y · Cited by 8
“Since quantum annealing can outperform simulated annealing in escaping local minima [27, 28, 67], these findings provide additional motivation for the approach proposed in this work.”
Quantum Computing for Genomics: Conceptual Challenges and Practical Perspectives
Maurizio, A., & Mazzola, G. (2025) · PRX Life · doi:10.1103/h49j-bsc6 · Cited by 17
“[136], for example, identifies a class of artificial problems where quantum annealers demonstrate scaling advantages over SA.”
InfluentialBenchmarking quantum annealing with maximum cardinality matching problems
Vert, D., Willsch, M., Yenilen, B., Sirdey, R., Louise, S., & Michielsen, K. F. L. (2024) · Frontiers in Computer Science · doi:10.3389/fcomp.2024.1286057 · Cited by 8
Yang, J. M., Li, L., & Wang, W. (2024) · Molecules · doi:10.3390/molecules29174071 · Cited by 7
QUBO Formulations and Characterization of Penalty Parameters for the Multi-Knapsack Problem
Güney, E., Ehrenthal, J. C. F., & Hanne, T. (2025) · IEEE Access · doi:10.1109/access.2025.3550788 · Cited by 7
“A. QUANTUM ANNEALING Quantum annealing uses the physical properties of quantum systems to address optimization problems [14].”
Obstacles to quantum annealing in a planar embedding of XORSAT
Patil, P., Kourtis, S., Chamon, C., Mucciolo, E. R., & Ruckenstein, A. E. (2019) · Physical review. B./Physical review. B · doi:10.1103/physrevb.100.054435 · Cited by 8
Noise-tolerant quantum speedups in quantum annealing without fine tuning
Kapit, E., & Oganesyan, V. (2020) · Quantum Science and Technology · doi:10.1088/2058-9565/abd59a · Cited by 8
Improving Schrödinger Equation Implementations with Gray Code for Adiabatic Quantum Computers
Chang, C. C., McElvain, K., Rrapaj, E., & Wu, Y. (2022) · PRX Quantum · doi:10.1103/prxquantum.3.020356 · Cited by 7
Locally suppressed transverse-field protocol for diabatic quantum annealing
Fry-Bouriaux, L., O'Connor, D., Feinstein, N., & Warburton, P. (2021) · Physical Review A · doi:10.1103/physreva.104.052616 · Cited by 8
Milestones on the Quantum Utility Highway: Quantum Annealing Case Study
McGeoch, C. C., & Farré, P. (2023) · ACM Transactions on Quantum Computing · doi:10.1145/3625307 · Cited by 6
“• Several papers consider a performance metric known as quantum speedup [5, 20, 29, 30, 39, 54, 66].”
BackgroundQian, Y., Ni, K., Kämpfe, T., Zhuo, C., & Yin, X.-Z. (2024) · Design Automation Conference · doi:10.1145/3649329.3655988 · Cited by 11
A numerical approach for calculating exact non-adiabatic terms in quantum dynamics
Lawrence, E. D. C., Schmid, S. F. J., Čepaitė, I., Kirton, P., & Duncan, C. W. (2025) · SciPost Physics · doi:10.21468/scipostphys.18.1.014 · Cited by 7
“B gives full details of our numerical implementation, alongside a more detailed flow chart.”
Revisiting Old Combinatorial Beasts in the Quantum Age: Quantum Annealing Versus Maximal Matching
Vert, D., Sirdey, R., & Louise, S. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-50433-5_37 · Cited by 6
“advantage of quantum annealing as implemented in D-Wave systems over classical annealing (the existence of which, as previously emphasized, as already been established on specially designed problems [1]). However, our results tends to rule out (or conrm) the absence of an exponential advantage in the general case of q…”
BackgroundResultSpeeding up Quantum Annealing with Engineered Dephasing
Sveistrys, M., Langbehn, J., Menu, R., Campbell, S., Morigi, G., & Koch, C. P. (2025) · Quantum · doi:10.22331/q-2025-05-05-1731 · Cited by 5
“While there is numerical evidence for speedups exhibited by QA for specific problems [23, 24], the case for a general problem-independent speedup is more disputed [25, 26].”
Three-flavor collective neutrino oscillation simulations on a qubit quantum annealer
Chernyshev, I. A. (2025) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.111.043017 · Cited by 5
Dynamical System-Based Computational Models for Solving Combinatorial Optimization on Hypergraphs
Bashar, M. K., Mallick, A., Ghosh, A. W., & Shukla, N. (2023) · IEEE Journal on Exploratory Solid-State Computational Devices and Circuits · doi:10.1109/jxcdc.2023.3235113 · Cited by 5
“This has motivated the exploration of many alternate computing models and approaches spanning from quantum computing [1], [2], [3] to classical analog methods using dynamical systems such as neural networks [4], [5], [6] and oscillator networks [7], [8], [9], [10], [11], [12], [13], [14], [15], [16].”
MethodPerformance of quantum annealing for 2-satisfiability problems with multiple satisfying assignments
Mehta, V., Raedt, H. A. D., Michielsen, K. F. L., & Jin, F. (2025) · Physical Review A · doi:10.1103/n7r5-s63q · Cited by 5
Investigating the potential for a limited quantum speedup on protein lattice problems
Outeiral, C., Morris, G. M., Shi, J., Strahm, M., Benjamin, S. C., & Deane, C. M. (2021) · New Journal of Physics · doi:10.1088/1367-2630/ac29ff · Cited by 8
“[51]), we have not constrained the classical approach to solve a problem in the Ising form.”
MethodUnderstanding the physics of D-Wave annealers: From Schrödinger to Lindblad to Markovian dynamics
Mehta, V., Raedt, H. A. D., Michielsen, K. F. L., & Jin, F. (2025) · Physical Review A · doi:10.1103/3yk8-qn64 · Cited by 4
A quantum optimization approach to Nonlinear Model Predictive Control
Novara, C., Boggio, M., & Volpe, D. (2025) · European Control Conference (ECC) · doi:10.23919/ecc65951.2025.11187268 · Cited by 4
“In this “dynamic” context, it is worth mentioning a particular class of quantum computers, called quantum annealers, whose state of progress is relatively advanced, allowing the solution of complex optimization problems of interest in real applications [6]– [10].”
Entanglement generation and scaling from noisy quenches across a quantum critical point
Jafari, R., Naji, J., Langari, A., Karimipour, V., & Johannesson, H. (2025) · Physical Review Research · doi:10.1103/km39-472p · Cited by 4
“However, while intrinsically noisy, the type of noise and its intensity are presently difficult to control on these types of platforms [70].”
NISQ-Ready Community Detection Based on Separation-Node Identification
Stein, J., Ott, D., Nüßlein, J., Bucher, D., Schönfeld, M., & Feld, S. (2023) · Mathematics · doi:10.3390/math11153323 · Cited by 4
Assessing the Effectiveness of Non-Turing Computing Paradigms
Rocutto, L., Maronese, M., Traversa, F. L., Decherchi, S., & Cavalli, A. (2023) · IEEE Access · doi:10.1109/access.2023.3312990 · Cited by 4
Posiform planting: generating QUBO instances for benchmarking
Hahn, G., Pelofske, E., & Djidjev, H. N. (2023) · Frontiers in Computer Science · doi:10.3389/fcomp.2023.1275948 · Cited by 4
Anschuetz, E. R., Funcke, L., Komiske, P., Kryhin, S., & Thaler, J. (2022) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.106.056008 · Cited by 4
Demonstration of Error-Suppressed Quantum Annealing Via Boundary Cancellation
Munoz-Bauza, H., Venuti, L. C., & Lidar, D. A. (2023) · Physical Review Applied · doi:10.1103/physrevapplied.19.034095 · Cited by 4
Self-citationLocal search algorithms based on benchmark test functions problem
Bassel, A., Haglan, H. M., & Mahmoud, A. S. (2020) · IAES International Journal of Artificial Intelligence · doi:10.11591/ijai.v9.i3.pp529-534 · Cited by 4
“Using these methods, the best solution for a given problem is generated, through the enumeration of all conceivable solutions of the full search space [7-9].”
MethodQuantum-Hybrid Stereo Matching With Nonlinear Regularization and Spatial Pyramids
Braunstein, C., Ilg, E., & Golyanik, V. (2023) · International Conference on 3D Vision · doi:10.1109/3dv62453.2024.00121 · Cited by 2
“…are theorized to provide advantages in solving Quadratic Unconstrained Binary Optimization (QUBO) problems and, e.g., outperform simulated annealing [26] for certain (rugged) energy landscapes, in terms of the absolute convergence speed and the energy level of the final returned solution [1, 55].”
BackgroundAdvancing quantum machine learning from conceptual design to practical use
Tera, S. P., Chinthaginjala, R., Zhao, X., & Hamdi, M. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.132127 · Cited by 4
Seismic traveltime inversion with quantum annealing
Nguyen, H. A., & Tura, A. (2025) · Scientific Reports · doi:10.1038/s41598-025-01188-8 · Cited by 5
Quantum Optimization for Closed-Loop Scheduling of Earth Observation Satellite Formation
Marchioli, V., Boggio, M., Volpe, D., Massotti, L., & Novara, C. (2025) · SN Computer Science · doi:10.1007/s42979-025-04252-2 · Cited by 3
“Quantum computers and, in particular, quantum annealers have the potential to significantly reduce the computational complexity of these classes of problems, finding better solutions with a polynomial speedup compared to classical computers [33].”
Optimization via quantum preconditioning
Dupont, M., Oberoi, T., & Sundar, B. (2025) · Physical Review Applied · doi:10.1103/9prw-684p · Cited by 7
“Analog platforms, through empirical observation, have demonstrated an asymptotic scaling advantage over classical methods such as SA and parallel tempering on specific classes of problems [20– 23].”
Mapping state transition susceptibility in quantum annealing
Pelofske, E. (2023) · Physical Review Research · doi:10.1103/physrevresearch.5.013224 · Cited by 3
Simulated Oscillator-Based Ising Machine for two Million Nodes Max-Cut Problems
Mazza, L., Raimondo, E., Grimaldi, A., & Puliafito, V. (2023) · IEEE International Conference on Nanotechnology (IEEE-NANO) · doi:10.1109/nano58406.2023.10231307 · Cited by 3
Efficient Pause Location Prediction Using Quantum Annealing Simulations and Machine Learning
Zielewski, M., Takahashi, K., Shimomura, Y., & Takizawa, H. (2023) · IEEE Access · doi:10.1109/access.2023.3317698 · Cited by 1
Determination of optimal chain coupling made by embedding in D-wave quantum annealer
Park, H., & Lee, H. (2024) · AVS Quantum Science · doi:10.1116/5.0205511 · Cited by 3
Cost of emulating a small quantum annealing problem in the circuit model
Gonzalez-Conde, J., Morrell, Z., Vuffray, M., Albash, T., & Coffrin, C. (2025) · Physical Review A · doi:10.1103/physreva.111.062606 · Cited by 5
Self-citationBoosting the performance of quantum annealers using machine learning
Brence, J., Mihailovic, D., Kabanov, V. V., Todorovski, L., Džeroski, S., & Vodeb, J. (2023) · Quantum Machine Intelligence · doi:10.1007/s42484-022-00092-y · Cited by 4
“…2020; Kairys et al. 2020), optimization (Neukart et al. 2017; Orús et al. 2019), and machine learning (Mott et al. 2017; Li et al. 2018; Willsch et al. 2020; Jain et al. 2020), and have shown scaling advantage in problem solving efficiency in some cases (King et al. 2021; Albash and Lidar 2018).”
BackgroundSahai, T., Mishra, A., Pasini, J. M., & Jha, S. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i02.5524 · Cited by 2
Pelofske, E., Hahn, G., & Djidjev, H. N. (2025) · npj Unconventional Computing · doi:10.1038/s44335-025-00032-6 · Cited by 2
“NotethattheonlycomputetimeusedtomeasuretheTTSistheQPUaccess time; this does not include local embedding and data processing CPU time.”
Slongo, F., & Micheletti, C. (2025) · Physical Review Research · doi:10.1103/physrevresearch.7.023116 · Cited by 2
Cazals, P., François, A., Henriet, L., Leclerc, L., Marin, M., Naghmouchi, M. Y., Coelho, W. D. S., Sikora, F., Vitale, V., Watrigant, R., et al. (2026) · Physical Review Applied · doi:10.1103/8gjv-ll1f · Cited by 20
QDBO: A Real-Time Quantum-Augmented Database System Optimizer
Liu, H., Kumar, A., Spedalieri, F. M., & Sabek, I. (2026) · Proceedings of the VLDB Endowment · doi:10.14778/3836663.3836712 · Cited by 2
Matsuta, T., & Furue, R. (2024) · PLoS ONE · doi:10.1371/journal.pone.0326303 · Cited by 3
“If thermal noise and other imperfections are suppressed and better embedding algorithms are developed, QA will be potentially faster than SA for some problems [6,18,19].”
BackgroundParallel Ising annealer via gradient-based Hamiltonian Monte Carlo
Wang, H., Liu, Z., Xie, Z., Li, L., Miao, Z., Cui, W., & Pan, Y. (2025) · Quantum Machine Intelligence · doi:10.1007/s42484-024-00231-7 · Cited by 2
“In particular, there may exist efficient quantum algorithms to solve the NP-hard combinatorial optimization problems (Farhi et al. 2014; Albash and Lidar 2018a; Guerreschi and Matsuura 2019; Yan and Sinitsyn 2022).”
Formulation of Correction Term in QUBO Form for Phase‐Field Model
Aoki, S., Endo, K., Matsuda, Y., Seki, Y., Tanaka, S., & Muramatsu, M. (2025) · International Journal for Numerical Methods in Engineering · doi:10.1002/nme.70019 · Cited by 2
Enhancing Quantum Algorithms for Quadratic Unconstrained Binary Optimization via Integer Programming
Wagner, F., Nüßlein, J., & Liers, F. (2025) · ACM Transactions on Quantum Computing · doi:10.1145/3711935 · Cited by 13
“Important examples of such heuristics are quantum annealing [3, 60] and the quantum approximate optimization algorithm ( QAOA ) [28, 43].”
Mallick, A., Zhao, Z., Bashar, M. K., Alam, S., Islam, M. M., Xiao, Y., Xu, Y., Aziz, A., Narayanan, V., Ni, K., et al. (2022) · Research Square · doi:10.21203/rs.3.rs-1705386/v1 · Cited by 2
Scaling advantage in approximate optimization with quantum annealingPreprint
Lidar, D. A., & Munoz-Bauza, H. (2024) · Research Square · doi:10.21203/rs.3.rs-3860892/v1 · Cited by 2
Self-citationImproving Quantum Annealing Performance on Embedded Problems
(2020) · Supercomputing Frontiers and Innovations · doi:10.14529/jsfi200403 · Cited by 6
“While early results [15, 20] may not have seemed promising for QA over classical solvers, later results [2, 12, 24] were more promising, and indicated that harder benchmark problems reveal the power of QA over classical solvers.”
ResultOperational Quantum Annealers are Cursed by their Qubits Interconnection Topologies
Vert, D., Sirdey, R., & Louise, S. (2020) · IEEE Computer Society Annual Symposium on VLSI · doi:10.1109/isvlsi49217.2020.00058 · Cited by 1
“Such an advantage has furthermore recently been experimentally demonstrated by Albash and Lidar [7].”
ResultVolpe, D., Cirillo, G. A., Zamboni, M., Graziano, M., & Turvani, G. (2024) · ACM Transactions on Quantum Computing · doi:10.1145/3665281 · Cited by 1
“• The TTS is usually employed in the state of the art to identify the quantum speedup in algorithms like SBa [4, 23, 32, 37, 68] and is defined as the time required to find a target solution , e.g., the optimum or a sub-optimum, with a certain percentage of confidence p conf , usually set to 99%.”
Xuan, W., Zhao, Z., Fan, L., & Han, Z. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3424433 · Cited by 1
“In theory, quantum annealing should perform better than simulated annealing because quantum annealing leverages quantum fluctuations rather than thermal fluctuations to get the optimal solution [20].”
BackgroundWang, K., Wang, S., Chen, Q., Qiao, X., Ma, H., & Qiu, T. (2024) · Quantum Information Processing · doi:10.1007/s11128-024-04512-9 · Cited by 2
“…simulated annealing algorithm, which uses thermal fluctuations to search the optimal solution of the problem, the quantum annealing algorithm [19, 20] uses quantum tunneling to enable the quantum to penetrate a potential barrier higher than its own energy, so that the algorithm gets out of the…”
CONVERGENCE RATE OF ALGORITHM FOR SOLVING LINEAR EQUATIONS BY QUANTUM ANNEALING
Tikhomirov, S. B., & Shalgin, V. S. (2024) · Журнал вычислительной математики и математической физики · doi:10.31857/s0044466924050061 · Cited by 1
Improved Posiform Planting Algorithm for Random Generation of Binary Optimization Problems
Isermann, S. (2025) · SN Computer Science · doi:10.1007/s42979-025-03964-9 · Cited by 1
“They cannot guarantee the uniqueness of the planted solution, as in [14], or the sparsity structure of a QUBO cannot be chosen.”
InfluentialJie, 刘. L., Jiakai, 董. D., Yibin, 万. W., & Yu, S. (2025) · Acta Optica Sinica · doi:10.3788/aos251081 · Cited by 1
Singkanipa, P., Xia, Z., & Lidar, D. A. (2025) · Quantum · doi:10.22331/q-2025-08-05-1821 · Cited by 2
Self-citationQuantum-classical hybrid approach for codon optimization and its practical applications
Chung, Y. K., Lee, D., Lee, J., Kim, J., Park, D. K., & Huh, J. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.114102 · Cited by 2
Planted Solutions in Quantum Chemistry: Generating Non-Trivial Hamiltonians with Known Ground States
Wang, L. W., Cantin, J. T., Patel, S., Loaiza, I., Huang, R., & Izmaylov, A. F. (2025) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5c01210 · Cited by 1
“One key recent use of planted solutions is to benchmark the performance of early-stage quantum computers ranging from quantum annealers [34, 36, 37, 42, 43] to NISQ devices [48, 49].”
Noori, M., Valiante, E., Rozada, I., Vaerenbergh, T. V., & Mohseni, M. (2026) · Physical Review Applied · doi:10.1103/2fpj-t663 · Cited by 1
“Examples include the computational effort introduced by Koza to evaluate the performance of genetic programming algorithms [18], TTS [19], and optimized TTS [34] to evaluate the performance of quantum and classical heuristic optimizers.”
Quantum Annealing Algorithms for Estimating Ising Partition Functions
Li, H., Yao, Z., & Qiu, X. (2026) · Physical Review Letters · doi:10.1103/8gmb-p619 · Cited by 2
“The protocol’s compatibility with NISQ-era devices [24–29] positions it as a timely contribution to the growing toolkit of quantum-enhanced algorithms, offering a blueprint for leveraging quantum fluctuations and tailored sampling to overcome classical limitations.”
Tsesmelis, M. (2025) · Springer eBooks · doi:10.1007/978-3-031-90727-2_7 · Cited by 1
Quantum Approaches for WCET-Related Optimization Problems
Bettonte, G., Gilbert, V., Vert, D., Louise, S., & Sirdey, R. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-08760-8_18 · Cited by 1
“Following the commonly used procedure (e.g. [1]), we randomly selected 10% of the physical qubits used as spin inversion for each instance.”
MethodPhillipson, F. (2022) · doi:10.26481/spe.20220525fp · Cited by 1
Improving Parameter Training for VQEs by Sequential Hamiltonian Assembly
Stein, J., Roshani, N., Zorn, M., Altmann, P. J., Kölle, M., & Linnhoff–Popien, C. (2024) · International Conference on Agents and Artificial Intelligence (ICAART) · doi:10.5220/0012312500003636 · Cited by 2
“An important class of problems exemplifying the property of having a local decomposition while also being a promising contender towards quantum advantage are combinatorial optimization problems (Lucas, 2014; Albash and Lidar, 2018; Pirnay et al., 2023).”
BackgroundGao, Y., Luo, Q., Lin, H., Chen, G., Fu, W., Lan, Y., & Danner, A. J. (2024) · doi:10.1364/cleo_at.2024.jw2a.166 · Cited by 1
Quantum eigensolver on extension of optimized binary configurations
Park, H., & Lee, H. (2024) · Physical review. B./Physical review. B · doi:10.1103/physrevb.110.205142 · Cited by 1
Compressed sensing enhanced by a quantum approximate optimization algorithm
Chevalier, B., Roga, W., & Takeoka, M. (2024) · Physical Review A · doi:10.1103/physreva.110.062410 · Cited by 3
Benchmarking a heuristic Floquet adiabatic algorithm for the Max-Cut problem
Granet, E., & Dreyer, H. (2025) · Scientific Reports · doi:10.1038/s41598-025-13923-2 · Cited by 1
High-order-modulation large MIMO detector based on physics-inspired methods
Zeng, Q., Cui, X., Tao, X.-Z., Hu, J.-Q., Pan, S., Sha, W. E. I., & Yung, M. (2025) · Physical Review Applied · doi:10.1103/zmgv-m2ql · Cited by 1
“Quantum Annealing-based method is promising and has the potential to achieve polynomial or exponential speedups with extremely near-optimal performance in large MIMO systems [9–11].”
Subsampling factorization machine annealing
Hama, Y., & Kadowaki, T. (2025) · Physical Review Research · doi:10.1103/qgpq-6dmr · Cited by 2
“[ 23, 24], it has been high-lighted that for certain optimization problems QA might be more advantageous than SA.”
Embedding quantum optimization problems using AC driven quantum ferromagnets
Mossi, G., Oganesyan, V., & Kapit, E. (2026) · Physica Scripta · doi:10.1088/1402-4896/ae5fec · Cited by 1
“Recent experimental results [10, 13, 18] on large problems in both neutral atom and flux qubit analog devices suggest quantum scaling advantage over classical heuristics, for problems which are short ranged and map natively to the underlying hardware graph.”
BackgroundK-spin Hamiltonian for quantum-resolvable Markov decision processes
Jones, E., Graf, P., Kapit, E., & Jones, W. (2020) · Quantum Machine Intelligence · doi:10.1007/s42484-020-00026-6 · Cited by 2
“[42] T.”
BackgroundInfluential
Metadata
- Authors
- Albash, T., & Lidar, D. A.
- Affiliations
- University of Southern California (US)
- ORCID
- Albash, Tameem
- Venue
- Physical Review X
- Publisher
- American Physical Society (APS)
- Published
- 2018-07-19
- Volume
- 8
- Issue
- 3
- ISSN
- 2160-3308 (ISSN-L)
- OpenAlex
- W2992146840
- Cited by
- 238
- References
- 70
- Citations by year
- 2026: 13 · 2025: 46 · 2024: 33 · 2023: 28 · 2022: 28 · 2021: 28 · 2020: 33 · 2019: 24 · 2018: 4
- FWCI
- 12.50
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Physical Review X (publishedVersion, cc-by)
- arXiv (Cornell University) (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- arXiv (Cornell University)
- APS March Meeting Abstracts
- Funders
- Army Research Office (W911NF-12-1-0523, W911NF-11-1-0268, W911NF-15-1-0582, W911NF-17-C-0050)
- National Science Foundation (INSPIRE-1551064)
- U.S. Department of Energy (DE-AC05-00OR22725)
- Office of the Director of National Intelligence
- Intelligence Advanced Research Projects Activity
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum Information and Cryptography
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum many-body systems
- Keywords
- quantum annealing, simulated annealing, quantum advantage, benchmarking, combinatorial optimization, quantum annealers
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