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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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“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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“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].”
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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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- P. W. Shor, Proceedings of the 35th Annual Symposium on Foundations of Computer Science, 1994
- F. Hamze, 2004, UAI
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A quantum engineer's guide to superconducting qubits
Krantz, P., Kjærgaard, M., Yan, F., Orlando, T. P., Gustavsson, S., & Oliver, W. D. (2019) · Applied Physics Reviews · doi:10.1063/1.5089550 · Cited by 2,321
Quantum optimization using variational algorithms on near-term quantum devices
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Perspectives of quantum annealing: methods and implementations
Hauke, P., Katzgraber, H. G., Lechner, W., Nishimori, H., & Oliver, W. D. (2020) · Reports on Progress in Physics · doi:10.1088/1361-6633/ab85b8 · Cited by 507
Physics-Inspired Optimization for Quadratic Unconstrained Problems Using a Digital Annealer
Aramon, M., Rosenberg, G., Valiante, E., Miyazawa, T., Tamura, H., & Katzgraber, H. G. (2019) · Frontiers in Physics · doi:10.3389/fphy.2019.00048 · Cited by 442
“With the same goal in mind, researchers have been inspired to push the envelope for such problems on novel hardware, such as the coherent Ising machine [32], as well as on graphics processing units (GPU) [27, 28] and application-specific CMOS hardware [34, 35].”
BackgroundHamerly, R., Inagaki, T., McMahon, P. L., Venturelli, D., Marandi, A., Onodera, T., Ng, E., Langrock, C., Inaba, K., Honjo, T., et al. (2019) · Science Advances · doi:10.1126/sciadv.aau0823 · Cited by 366
“It has been observed empirically on D-Wave quantum annealers that, for Chimera graph spin glasses, optimal time to solution scales as Tsoln o exp (O(N )) (18, 19, 49), while Tsoln at fixed anneal times increases more steeply (18).”
BackgroundHamerly, R., Inagaki, T., McMahon, P. L., Venturelli, D., Marandi, A., Onodera, T., Ng, E., Langrock, C., Inaba, K., Honjo, T., et al. (2019) · CaltechAUTHORS (California Institute of Technology) · Cited by 257
Reverse quantum annealing approach to portfolio optimization problems
Venturelli, D., & Kondratyev, A. (2019) · Quantum Machine Intelligence · doi:10.1007/s42484-019-00001-w · Cited by 233
“In particular, the annealing time is expected to be suboptimal for N ≤ 50, leading to a measurement of a flatter slope for the median TTS [33].”
BackgroundThe prospects of quantum computing in computational molecular biology
Outeiral, C., Strahm, M., Shi, J., Morris, G. M., Benjamin, S. C., & Deane, C. M. (2021) · WIREs Computational Molecular Science · doi:10.1002/wcms.1481 · Cited by 232
“Despite these concerns, it has been used widely as an optimization metaheuristic and has recently been shown to overperform simulated annealing [188].”
MethodChallenges and opportunities in quantum optimization
Abbas, A., Ambainis, A., Augustino, B., Bärtschi, A., Buhrman, H., Coffrin, C., Cortiana, G., Dunjko, V., Egger, D. J., Elmegreen, B. G., et al. (2024) · Nature Reviews Physics · doi:10.1038/s42254-024-00770-9 · Cited by 201
“Hence, quantum annealing research tends to focus on empirical studies of the behaviour of the algorithm in ideal 47 and noisy computational settings 48,49 .”
Bando, Y., Susa, Y., Oshiyama, H., Shibata, N., Ohzeki, M., Gómez-Ruiz, F. J., Lidar, D. A., Suzuki, S., del Campo, A., & Nishimori, H. (2020) · Physical Review Research · doi:10.1103/physrevresearch.2.033369 · Cited by 147
“We first consider a Boltzmann distribution of the kink density of the classical Ising chain, then the classical spin-vector Monte Carlo model [80], which has been successfully applied to at least partially describe the outcomes of experiments using the D-Wave devices in past studies [8,13,56,66,67,71,81], and also in…”
MethodSelf-citationProspects for quantum enhancement with diabatic quantum annealing
Crosson, E., & Lidar, D. A. (2021) · Nature Reviews Physics · doi:10.1038/s42254-021-00313-6 · Cited by 153
Self-citationCarapito, R., Li, R. Y., Helms, J., Carapito, C., Gujja, S., Rolli, V., Guimaraes, R., Malagon-Lopez, J., Spinnhirny, P., Lederle, A., et al. (2021) · Science Translational Medicine · doi:10.1126/scitranslmed.abj7521 · Cited by 127
“The advantage of running the optimization problem on a QA is that, since the QA samples from the quantum distribution, it retains both the lowest energy solution and some of the next lowest-energy solutions (Albash and Lidar, 2018).”
BackgroundSelf-citationPower of Pausing: Advancing Understanding of Thermalization in Experimental Quantum Annealers
Marshall, J., Venturelli, D., Hen, I., & Rieffel, E. G. (2019) · Physical Review Applied · doi:10.1103/physrevapplied.11.044083 · Cited by 125
“03] GHz, when the problem Hamiltonian is re-scaled by [1, 2, 4, 8] re-”
InfluentialQuantum Annealing amid Local Ruggedness and Global Frustration
King, J., Yarkoni, S., Raymond, J., Ozfidan, I., King, A. D., Nevisi, M. M., Hilton, J., & McGeoch, C. C. (2019) · Journal of the Physical Society of Japan · doi:10.7566/jpsj.88.061007 · Cited by 114
Practical Annealing-Based Quantum Computing
McGeoch, C. C., Harris, R., Reinhardt, S. P., & Bunyk, P. (2019) · Computer · doi:10.1109/mc.2019.2908836 · Cited by 70
“12 Note that results about quantum speedup also tend to have little relevance to practice: the synthetic inputs do not resemble real-world inputs, and the scaling analysis ignores constant-factor speedups (sometimes favoring the QPU by many orders of magnitude 13 ), which are of keen interest to…”
BackgroundBiology and medicine in the landscape of quantum advantages
Cordier, B. A., Sawaya, N. P. D., Guerreschi, G. G., & McWeeney, S. K. (2022) · Journal of The Royal Society Interface · doi:10.1098/rsif.2022.0541 · Cited by 87
Bokhan, D., Mastiukova, A. S., Boev, A. S., Trubnikov, D. N., & Fedorov, A. K. (2022) · Frontiers in Physics · doi:10.3389/fphy.2022.1069985 · Cited by 89
“ranking DNA to RNA transcription factors [36] using a quantum annealer, weather forecasting [37] on the basis of the superconducting quantum computer, and many others [38].”
MethodDetecting multiple communities using quantum annealing on the D-Wave system
Negre, C. F. A., Ushijima-Mwesigwa, H., & Mniszewski, S. M. (2020) · PLOS ONE · doi:10.1371/journal.pone.0227538 · Cited by 80
“Problems having many local minima (that could come from large sized system) would require longer annealing times for QA [44].”
BackgroundSU(2) lattice gauge theory on a quantum annealer
Rahman, S. A., Lewis, R., Mendicelli, E., & Powell, S. J. M. (2021) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.104.034501 · Cited by 80
Benchmarking Quantum Annealing Controls with Portfolio Optimization
Grant, E., Humble, T. S., & Stump, B. (2021) · Physical Review Applied · doi:10.1103/physrevapplied.15.014012 · Cited by 90
“[18] Tameem Albash and Daniel A Lidar.”
BackgroundNishi, H., Kosugi, T., & Matsushita, Y. (2021) · npj Quantum Information · doi:10.1038/s41534-021-00409-y · Cited by 86
Dynamics of reverse annealing for the fully connected p -spin model
Yamashiro, Y., Ohkuwa, M., Nishimori, H., & Lidar, D. A. (2019) · Physical Review A · doi:10.1103/physreva.100.052321 · Cited by 76
Self-citationAnnealing-Based Quantum Computing for Combinatorial Optimal Power Flow
Morstyn, T. (2022) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2022.3200590 · Cited by 87
“Theoretically demonstrating when noisy quantum annealing has a definitive advantage over classical alternatives is challenging [34], but a performance advantage has been demonstrated for specific applications [35], [36].”
BackgroundTheory versus practice in annealing-based quantum computing
McGeoch, C. C. (2020) · Theoretical Computer Science · doi:10.1016/j.tcs.2020.01.024 · Cited by 72
Demonstration of a Nonstoquastic Hamiltonian in Coupled Superconducting Flux Qubits
Ozfidan, I., Deng, C., Smirnov, A. Y., Lanting, T. M., Harris, R., Swenson, L. J., Whittaker, J. D., Altomare, F., Babcock, M. C., Baron, C., et al. (2020) · Physical Review Applied · doi:10.1103/physrevapplied.13.034037 · Cited by 72
“[13] T.”
BackgroundKarimi-Rizvandi, S., Goodarzi, H. V., Hatamiafkoueieh, J., Chung, I.-M., Kişi, Ö., Kim, S., & Linh, N. T. T. (2021) · Water · doi:10.3390/w13050658 · Cited by 64
“The dvant ge of the SA algorithm was v ry low memory consumption and the ability to pass local optimization due to a randomized process [72].”
BackgroundMethodEngineering Reflective Metasurfaces With Ising Hamiltonian and Quantum Annealing
Ross, C., Gradoni, G., Lim, Q. J., & Peng, Z. (2021) · IEEE Transactions on Antennas and Propagation · doi:10.1109/tap.2021.3137424 · Cited by 64
“The probability of reaching the ground state (global optimal solution) is around 95%, which is often referred to as the success probability of a single-instance run, Ps [55], [56].”
BackgroundOptimization Applications as Quantum Performance Benchmarks
Lubinski, T., Coffrin, C., McGeoch, C. C., Sathe, P., Apanavicius, J., Bernal, D. E., & Quantum Economic Development Consortium Collaboration (2024) · ACM Transactions on Quantum Computing · doi:10.1145/3678184 · Cited by 92
“The time-to-solution (TTS) metric determines the expected wall-clock time required to solve a problem to optimality [48, 82–85].”
Quantum Optimization: Potential, Challenges, and the Path Forward
Abbas, A., (NNSA), U. N. N. S. A., Ambainis, A., Augustino, B., Bärtschi, A., Buhrman, H., Coffrin, C., Cortiana, G., Dunjko, V., Egger, D., et al. (2023) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.2172/2229681 · Cited by 65
Multilevel Combinatorial Optimization across Quantum Architectures
Ushijima-Mwesigwa, H., Shaydulin, R., Negre, C. F. A., Mniszewski, S. M., Alexeev, Y., & Safro, I. (2021) · ACM Transactions on Quantum Computing · doi:10.1145/3425607 · Cited by 57
Demonstration of Algorithmic Quantum Speedup
Pokharel, B., & Lidar, D. A. (2023) · Physical Review Letters · doi:10.1103/physrevlett.130.210602 · Cited by 66
“Quantum annealing hardware based on superconducting flux qubits was used to demonstrate a speedup against simulated annealing for certain specially crafted spin-glass problems [55].”
MethodSelf-citationBoundaries of quantum supremacy via random circuit sampling
Zlokapa, A., Villalonga, B., Boixo, S., & Lidar, D. A. (2023) · npj Quantum Information · doi:10.1038/s41534-023-00703-x · Cited by 66
Self-citationScalable Emulation of Sign-Problem–Free Hamiltonians with Room-Temperature p -bits
Camsari, K. Y., Chowdhury, S., & Datta, S. (2019) · Physical Review Applied · doi:10.1103/physrevapplied.12.034061 · Cited by 55
3-regular three-XORSAT planted solutions benchmark of classical and quantum heuristic optimizers
Kowalsky, M., Albash, T., Hen, I., & Lidar, D. A. (2022) · Quantum Science and Technology · doi:10.1088/2058-9565/ac4d1b · Cited by 52
“Since the solvers we study are stochastic, we use the optimal time-to-solution (TTS) metric [21, 24].”
Self-citationQFold: quantum walks and deep learning to solve protein folding
Casares, P. A. M., Campos, R., & Martin-Delgado, M. A. (2022) · Quantum Science and Technology · doi:10.1088/2058-9565/ac4f2f · Cited by 57
“Similar metrics have also been defined previously in the literature [37].”
Quantum Annealing vs. QAOA: 127 Qubit Higher-Order Ising Problems on NISQ Computers
Pelofske, E., Bärtschi, A., & Eidenbenz, S. (2023) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-32041-5_13 · Cited by 47
“Simulated annealing is a standard high accuracy and general purpose classical heuristic algorithm [57], and has been used as a reasonable comparison against quantum algorithms [13].”
MethodChallenges of variational quantum optimization with measurement shot noise
Scriva, G., Astrakhantsev, N., Pilati, S., & Mazzola, G. (2024) · Physical Review A · doi:10.1103/physreva.109.032408 · Cited by 64
“However, it is not easy to prove systematic quantum speedups with analog quan-∗ tum annealers [8, 9], also because quantum Monte Carlo algorithms appear to be able to emulate their tunneling dynamics [10–13].”
BackgroundMethodImproving quantum annealing of the ferromagnetic p -spin model through pausing
Passarelli, G., Cataudella, V., & Lucignano, P. (2019) · Physical review. B./Physical review. B · doi:10.1103/physrevb.100.024302 · Cited by 47
Noise-injected analog Ising machines enable ultrafast statistical sampling and machine learning
Böhm, F., Alonso-Urquijo, D., Verschaffelt, G., & der Sande, G. V. (2022) · Nature Communications · doi:10.1038/s41467-022-33441-3 · Cited by 55
“(16), and (17) with a step width of h = 1 at α = 0.”
MethodAgile Earth Observation Satellite Scheduling With a Quantum Annealer
Stollenwerk, T., Michaud, V., Lobe, E., Picard, M., Basermann, A., & Botter, T. (2021) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2021.3088490 · Cited by 52
Mishra, A., Albash, T., & Lidar, D. A. (2018) · Nature Communications · doi:10.1038/s41467-018-05239-9 · Cited by 46
Self-citationQuantum Annealing versus Digital Computing
Jünger, M., Lobe, E., Mutzel, P., Reinelt, G., Rendl, F., Rinaldi, G., & Stollenwerk, T. (2021) · ACM Journal of Experimental Algorithmics · doi:10.1145/3459606 · Cited by 51
From Charge to Spin and Spin to Charge: Stochastic Magnets for Probabilistic Switching
Camsari, K. Y., Debashis, P., Ostwal, V., Pervaiz, A. Z., Shen, T., Chen, Z., Datta, S., & Appenzeller, J. (2020) · Proceedings of the IEEE · doi:10.1109/jproc.2020.2966925 · Cited by 44
“There is emerging evidence on relative benefits of SQA compared to CA [57], attributed to the SQA’s ability to mimick “tunneling” out of local minima which is not possible in CA though it has also been noted that there might be observable scaling differences between QA and SQA [58].”
BackgroundQuantum processor-inspired machine learning in the biomedical sciences
Li, R. Y., Gujja, S., Bajaj, S. R., Gamel, O., Cilfone, N. A., Gulcher, J. R., Lidar, D. A., & Chittenden, T. W. (2021) · Patterns · doi:10.1016/j.patter.2021.100246 · Cited by 54
“16 While the computational role of quantum effects in these processors remains controversial and the subject of intensive study, quantum annealing is currently one of the few paradigms of quantum computing that are approaching a scale useful for practical applications.”
Self-citationOn the emerging potential of quantum annealing hardware for combinatorial optimization
Tasseff, B., Albash, T., Morrell, Z., Vuffray, M., Lokhov, A. Y., Misra, S., & Coffrin, C. (2024) · Journal of Heuristics · doi:10.1007/s10732-024-09530-5 · Cited by 42
“Due to the challenges associated with mapping established optimization test cases to specific QA hardware [11], the QA benchmarking community has adopted the practice of building instance generation algorithms that are tailored to specific quantum processing units (QPUs) [48, 36, 49, 19, 2, 66].”
InfluentialSelf-citationWhy and When Pausing is Beneficial in Quantum Annealing
Chen, H., & Lidar, D. A. (2020) · Physical Review Applied · doi:10.1103/physrevapplied.14.014100 · Cited by 41
“Today’s commercial quantum annealers feature thousands of superconducting flux qubits and are being used routinely to test whether this approach can provide a quantum advantage over classical computing [5–11].”
BackgroundSelf-citationCharged particle tracking with quantum annealing optimization
Zlokapa, A., Anand, A., Vlimant, J.-R., Duarte, J. M., Job, J., Lidar, D. A., & Spiropulu, M. (2021) · Quantum Machine Intelligence · doi:10.1007/s42484-021-00054-w · Cited by 42
“To test the effect of the annealing time (which in principle must be optimized in order to extract the true time to solution (Albash and Lidar 2018; Rønnow et al. 2014)), we compare runs from 5 to 800 μs.”
BackgroundMethodInfluentialSelf-citationEmbedding Overhead Scaling of Optimization Problems in Quantum Annealing
Könz, M. S., Lechner, W., Katzgraber, H. G., & Troyer, M. (2021) · PRX Quantum · doi:10.1103/prxquantum.2.040322 · Cited by 43
Two-parameter counter-diabatic driving in quantum annealing
Prielinger, L., Hartmann, A., Yamashiro, Y., Nishimura, K., Lechner, W., & Nishimori, H. (2021) · Physical Review Research · doi:10.1103/physrevresearch.3.013227 · Cited by 39
“[40] T.”
MethodMean field analysis of reverse annealing for code-division multiple-access multiuser detection
Arai, S., Ohzeki, M., & Tanaka, K. (2021) · Physical Review Research · doi:10.1103/physrevresearch.3.033006 · Cited by 36
Photonic Ising machines for combinatorial optimization problems
Gao, Y., Chen, G., Luo, Q., Fu, W., Yuan, Z., & Danner, A. J. (2024) · Applied Physics Reviews · doi:10.1063/5.0216656 · Cited by 39
Scaling Advantage in Approximate Optimization with Quantum Annealing
Munoz-Bauza, H., & Lidar, D. A. (2025) · Physical Review Letters · doi:10.1103/physrevlett.134.160601 · Cited by 46
“It is well known that the TTS metric generates unreliable results unless the annealing time is optimized for each size N [8, 13].”
BackgroundSelf-citationQuantum annealing for systems of polynomial equations
Chang, C. C., Gambhir, A. S., Humble, T. S., & Sota, S. (2019) · Scientific Reports · doi:10.1038/s41598-019-46729-0 · Cited by 36
Compressed Quadratization of Higher Order Binary Optimization Problems
Mandal, A., Roy, A., Upadhyay, S., & Ushijima‐Mwesigwa, H. (2020) · DCC · doi:10.1109/dcc47342.2020.00090 · Cited by 33
Spintronic devices as next-generation computation accelerators
González, V. H., Litvinenko, A. N., Kumar, A., Khymyn, R., & Åkerman, J. J. (2024) · Current Opinion in Solid State and Materials Science · doi:10.1016/j.cossms.2024.101173 · Cited by 36
“…technology [13, 14], electrical oscillators [15], interfering light [16], optical pulses [17, 18, 19, 20], memristor crossbar arrays [21, 22], Josephson junctions [23, 24], and even single atoms [25]; with the latter two making use of the quantum adiabatic theorem to tunnel to the ground state.”
BackgroundStrategic Portfolio Optimization Using Simulated, Digital, and Quantum Annealing
Lang, J. B., Zielinski, S., & Feld, S. (2022) · Applied Sciences · doi:10.3390/app122312288 · Cited by 31
“Parallel rejection algorithms on GPU (see [29,30]) are examples of similar efforts in the literature to address the low acceptance probability problem in Monte Carlo methods [28].”
MethodAccelerated quantum Monte Carlo with probabilistic computers
Chowdhury, S., Camsari, K. Y., & Datta, S. (2023) · Communications Physics · doi:10.1038/s42005-023-01202-3 · Cited by 35
“This is interesting because until this result, results on TFI, a well-known stoquastic Hamiltonian, have been routinely benchmarked with quantum Monte Carlo algorithms [40] with no clear scaling differences for practical problems [41].”
ResultWishart planted ensemble: A tunably rugged pairwise Ising model with a first-order phase transition
Hamze, F., Raymond, J., Pattison, C. A., Biswas, K., & Katzgraber, H. G. (2020) · Physical review. E · doi:10.1103/physreve.101.052102 · Cited by 34
“Often, it is faster to carry out many short attempts taking run time R with a lower success probability p(R, {φ}) which motivates the definition [79–81]”
BackgroundAn Application of Quantum Annealing Computing to Seismic Inversion
Souza, A. M., Martins, E. O., Roditi, I., Sá, N., Sarthour, R. S., & Oliveira, I. S. (2022) · Frontiers in Physics · doi:10.3389/fphy.2021.748285 · Cited by 35
“Recent works have proposed that, in some cases, quantum annealing is advantageous over classical computing [27–30], on the other hand, no advantage was reported in references [24, 31].”
BackgroundBenchmarking Digital Quantum Simulations Above Hundreds of Qubits Using Quantum Critical Dynamics
Miessen, A., Egger, D. J., Tavernelli, I., & Mazzola, G. (2024) · PRX Quantum · doi:10.1103/prxquantum.5.040320 · Cited by 42
“For instance, variational algorithms [46, 83] for many-body spin systems feature circuits very similar to the ones considered in the previous section.”
BackgroundMethodLimitations of error corrected quantum annealing in improving the performance of Boltzmann machines
Li, R. Y., Albash, T., & Lidar, D. A. (2020) · Quantum Science and Technology · doi:10.1088/2058-9565/ab9aab · Cited by 31
“In reference [62], simulations demonstrated that at high temperature SQA and SVMC performed similarly, but at lower temperatures the two performed differently, most likely because SQA is able to simulate tunneling events, whereas SVMC can only make use of thermal hopping.”
BackgroundSelf-citationQuickSim: Efficient and Accurate Physical Simulation of Silicon Dangling Bond Logic
Drewniok, J., Walter, M., Ng, S. S. H., Walus, K., & Wille, R. (2023) · IEEE International Conference on Nanotechnology (IEEE-NANO) · doi:10.1109/nano58406.2023.10231266 · Cited by 28
“This trade-off is captured by the Time-To-Solution (TTS) metric that provides the total runtime needed to derive a solution of a certain confidence level η [31].”
MethodSu, Y., Kim, T. T.-H., & Kim, B. (2024) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2024.3352907 · Cited by 26
“Recently, Ising machines have been demonstrated in various technologies, including quantum computing [26], [27], [28], [29], coherent optics [30], [31], [32], and CMOS technologies [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17].”
BackgroundQuantum computation and simulation with superconducting qubits*
He, K., Geng, X., Huang, R., Liu, J., & Chen, W. (2021) · Chinese Physics B · doi:10.1088/1674-1056/ac16cf · Cited by 24
Equation Planting: A Tool for Benchmarking Ising Machines
Hen, I. (2019) · Physical Review Applied · doi:10.1103/physrevapplied.12.011003 · Cited by 25
“[50–52]).”
Benchmarking Quantum(-Inspired) Annealing Hardware on Practical Use Cases
Huang, T., Xu, J., Luo, T., Gu, X., Goh, R. S. M., & Wong, W. (2022) · IEEE Transactions on Computers · doi:10.1109/tc.2022.3219257 · Cited by 25
“[16] compares the scalability of quantum and classical annealing.”
BackgroundTaming a nonconvex landscape with dynamical long-range order: Memcomputing Ising benchmarks
Sheldon, F. C., Traversa, F. L., & Ventra, M. D. (2019) · Physical review. E · doi:10.1103/physreve.100.053311 · Cited by 25
Benchmarking Quantum Annealing Against “Hard” Instances of the Bipartite Matching Problem
Vert, D., Sirdey, R., & Louise, S. (2021) · SN Computer Science · doi:10.1007/s42979-021-00483-1 · Cited by 26
“Such an advantage has furthermore recently been experimentally demonstrated by Albash and Lidar [1].”
BackgroundResultIsing Model Optimization Problems on a FPGA Accelerated Restricted Boltzmann MachinePreprint
Patel, S., Chen, L., Canoza, P., & Salahuddin, S. (2020) · Research Square · doi:10.21203/rs.3.rs-122016/v1 · Cited by 26
Quantum Combinatorial Optimization in the NISQ Era: A Systematic Mapping Study
Gemeinhardt, F., Garmendia, A., Wimmer, M., Weder, B., & Leymann, F. (2024) · ACM Computing Surveys · doi:10.1145/3620668 · Cited by 22
“SA, spin-vector-monte-carlo [215], and simulated QA suggests that QA possesses the best scaling properties [7].”
BackgroundComputational hardness of spin-glass problems with tile-planted solutions
Perera, D., Hamze, F., Raymond, J., Weigel, M., & Katzgraber, H. G. (2020) · Physical review. E · doi:10.1103/physreve.101.023316 · Cited by 22
Wauters, M. M., Mbeng, G. B., & Santoro, G. E. (2020) · Physical Review A · doi:10.1103/physreva.102.062404 · Cited by 35
Blueprint for All-to-All-Connected Superconducting Spin Qubits
Pita‐Vidal, M., Wesdorp, J. J., & Andersen, C. K. (2025) · PRX Quantum · doi:10.1103/prxquantum.6.010308 · Cited by 22
“Flux qubit systems have been used to approach the so-lution of some problems using quantum annealing [61– 63].”
Nested quantum annealing correction at finite temperature: p -spin models
Matsuura, S., Nishimori, H., Vinci, W., & Lidar, D. A. (2019) · Physical Review A · doi:10.1103/physreva.99.062307 · Cited by 21
Self-citationQuantum adiabatic machine learning by zooming into a region of the energy surface
Zlokapa, A., Mott, A., Job, J., Vlimant, J.-R., Lidar, D. A., & Spiropulu, M. (2020) · Physical Review A · doi:10.1103/physreva.102.062405 · Cited by 27
Self-citationCryogenic Electronics and Quantum Information Processing
Holmes, D. S. (2021) · doi:10.1109/irds54852.2021.00012 · Cited by 24
Perils of embedding for sampling problems
Marshall, J., Gioacchino, A. D., & Rieffel, E. G. (2020) · Physical Review Research · doi:10.1103/physrevresearch.2.023020 · Cited by 18
A superconducting adiabatic neuron in a quantum regime
Bastrakova, M. V., Pashin, D. S., Rybin, D. A., Schegolev, A. E., Klenov, N. V., Soloviev, I. I., Gorchavkina, A. A., & Михайлович, С. А. (2022) · Beilstein Journal of Nanotechnology · doi:10.3762/bjnano.13.57 · Cited by 19
Quantum annealing for vehicle routing problem with weighted segment
Tambunan, T. D., Suksmono, A. B., Edward, I. J. M., & Mulyawan, R. (2023) · AIP conference proceedings · doi:10.1063/5.0178362 · Cited by 22
Continuous quantum error correction for evolution under time-dependent Hamiltonians
Atalaya, J., Zhang, S., Niu, M. Y., Babakhani, A., Chan, C. H. H., Epstein, J. M., & Whaley, K. B. (2021) · Physical Review A · doi:10.1103/physreva.103.042406 · Cited by 19
“This TTS metric, which is applied to both algorithmic and error mitigation protocols, reflects a trade-off between a high single-run success probability with a long operation time, and a low single-run success probability with a short operation time [59].”
MethodA Quantum Annealer-Enabled Decoder and Hardware Topology for NextG Wireless Polar Codes
Kasi, S., Kaewell, J., & Jamieson, K. (2023) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2023.3311204 · Cited by 18
“It is computed as [30]: where T a is the annealing time and P 1 is the probability of R 1 , the minimum energy solution.”
MethodVolpe, D., Cirillo, G. A., Zamboni, M., & Turvani, G. (2023) · IEEE Access · doi:10.1109/access.2023.3260765 · Cited by 18
“This figure of merit is commonly employed in the literature for comparing heuristic algorithms and in particular, for detecting quantum speed-up [96], [97], [98], [99], [100] given by quantum, quantum-inspired and quantum-compliant optimization approaches.”
MethodEfficient low temperature Monte Carlo sampling using quantum annealing
Sandt, R., & Spatschek, R. (2023) · Scientific Reports · doi:10.1038/s41598-023-33828-2 · Cited by 17
Improving performance of logical qubits by parameter tuning and topology compensation
Raymond, J., Ndiaye, N., Rayaprolu, G., & King, A. D. (2020) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce49297.2020.00044 · Cited by 19
A Quantum Algorithm for Model-Independent Searches for New Physics
Matchev, K. T., Shyamsundar, P., & Smolinsky, J. (2023) · Letters in High Energy Physics · doi:10.31526/lhep.2023.301 · Cited by 19
“A promising approach to tackle large N cases is offered by current and future AQO implementations on quantum computers [55–57].”
The potential of quantum annealing for rapid solution structure identification
Pang, Y., Coffrin, C., Lokhov, A. Y., & Vuffray, M. (2020) · Constraints · doi:10.1007/s10601-020-09315-0 · Cited by 20
Multilevel Combinatorial Optimization Across Quantum Architectures
Ushijima‐Mwesigwa, H., Shaydulin, R., Negre, C. F. A., Mniszewski, S. M., Alexeev, Y., & Safro, I. (2019) · TigerPrints (Clemson University) · Cited by 15
Hamerly, R., Inagaki, T., McMahon, P. L., Venturelli, D., Marandi, A., Onodera, T., Ng, E., Langrock, C., Inaba, K., Honjo, T., et al. (2018) · arXiv (Cornell University) · Cited by 14
Schulz, S., Willsch, D., & Michielsen, K. F. L. (2024) · Physical Review Research · doi:10.1103/physrevresearch.6.013312 · Cited by 14
“Panels (a)–(e) present the averaged results for the EC problems with sizes N ∈ { 12 , 15 , 18 , 21 , 24 } , respectively.”
BackgroundCummins, J., Salman, H., & Berloff, N. G. (2025) · Physical Review Research · doi:10.1103/physrevresearch.7.013150 · Cited by 15
Noise can speed Markov chain Monte Carlo estimation and quantum annealing
Franzke, B., & Kosko, B. (2019) · Physical review. E · doi:10.1103/physreve.100.053309 · Cited by 14
On the Computational Viability of Quantum Optimization for PMU Placement
Jones, E. B., Kapit, E., Chang, C.-Y., Biagioni, D., Vaidhynathan, D., Graf, P. A., & Jones, W. B. (2020) · IEEE Power Engineering Society General Meeting · doi:10.1109/pesgm41954.2020.9281420 · Cited by 15
“…speedups have been demonstrated for AQA against both simulated annealing (SA)– in the form of a scaling advantage– and quantum Monte Carlo (QMC)– as a fixed prefactor– using the D-Wave 2X and 2000Q machines on proof-of-principle problems designed to have tall and narrow energy barriers [8] [9].”
Standard quantum annealing outperforms adiabatic reverse annealing with decoherence
Passarelli, G., Yip, K.-W., Lidar, D. A., & Lucignano, P. (2022) · Physical Review A · doi:10.1103/physreva.105.032431 · Cited by 14
Self-citationPhysics-inspired Ising Computing with Ring Oscillator Activated p-bits
Aadit, N. A., Grimaldi, A., Finocchio, G., & Camsari, K. Y. (2022) · IEEE International Conference on Nanotechnology (IEEE-NANO) · doi:10.1109/nano54668.2022.9928681 · Cited by 14
“We follow the time-to-solution formulation [17], [18] to measure performance of the physics-inspired asynchronous architecture.”
MethodEvidence that PUBO outperforms QUBO when solving continuous optimization problems with the QAOA
Stein, J., Chamanian, F., Zorn, M., Nüßlein, J., Zieliński, S., Kölle, M., & Linnhoff-Popien, C. (2023) · GECCO Companion · doi:10.1145/3583133.3596358 · Cited by 24
“…promising approach to solving such optimization problems is quantum computing, which has already shown results comparable to classical state-of-the-art methods for small problem sizes [Albash and Lidar 2018; Denchev et al. 2016; Ebadi et al. 2022] despite current quantum hardware limitations.”
ResultLeib, D., Seidel, T., Jäger, S., Heese, R., Jones, C. R., Awasthi, A., Niederle, A. E., & Bortz, M. (2023) · Scientific Reports · doi:10.1038/s41598-023-45668-1 · Cited by 18
“Quantum annealing has proven to offer some advantage against the classical simulated annealing algorithm for a spin-glass problem, using D-Wave hardware (Raymond et al. 2023), but this is no conclusive evidence.”
BackgroundOptimization of quantum computing models inspired by D-wave quantum annealing
Wang, B., Hu, F., & Wang, C. (2020) · Tsinghua Science & Technology · doi:10.26599/tst.2019.9010030 · Cited by 13
Investigating the Chinese postman problem on a quantum annealer
Siloi, I., Carnevali, V., Pokharel, B., Fornari, M., & Felice, R. D. (2021) · Quantum Machine Intelligence · doi:10.1007/s42484-020-00031-9 · Cited by 15
Few-qubit quantum refrigerator for cooling a multi-qubit system
Arısoy, O., & Müstecaplıoğlu, Ö. E. (2021) · Scientific Reports · doi:10.1038/s41598-021-92258-0 · Cited by 15
“We take ~ = 1 and set h = 1 GHz for all of our calculations as it is a typical order of magnitude for superconducting qubits [32] and we will assume that g can be in the order of h [33].”
BackgroundDeep recurrent networks predicting the gap evolution in adiabatic quantum computing
Mohseni, N., Navarrete–Benlloch, C., Byrnes, T., & Marquardt, F. (2023) · Quantum · doi:10.22331/q-2023-06-12-1039 · Cited by 15
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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