Relevant bibliographies by topics / Quantum optimization

Academic literature on the topic 'Quantum optimization'

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Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Quantum optimization"

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Hogg, Tad, and Dmitriy Portnov. "Quantum optimization." Information Sciences 128, no. 3-4 (October 2000): 181–97. http://dx.doi.org/10.1016/s0020-0255(00)00052-9.

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Berta, Mario, Omar Fawzi, and Volkher B. Scholz. "Quantum Bilinear Optimization." SIAM Journal on Optimization 26, no. 3 (January 2016): 1529–64. http://dx.doi.org/10.1137/15m1037731.

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Malossini, A., E. Blanzieri, and T. Calarco. "Quantum Genetic Optimization." IEEE Transactions on Evolutionary Computation 12, no. 2 (April 2008): 231–41. http://dx.doi.org/10.1109/tevc.2007.905006.

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Apolloni, B., C. Carvalho, and D. de Falco. "Quantum stochastic optimization." Stochastic Processes and their Applications 33, no. 2 (December 1989): 233–44. http://dx.doi.org/10.1016/0304-4149(89)90040-9.

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Egger, Daniel J., Jakub Mareček, and Stefan Woerner. "Warm-starting quantum optimization." Quantum 5 (June 17, 2021): 479. http://dx.doi.org/10.22331/q-2021-06-17-479.

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There is an increasing interest in quantum algorithms for problems of integer programming and combinatorial optimization. Classical solvers for such problems employ relaxations, which replace binary variables with continuous ones, for instance in the form of higher-dimensional matrix-valued problems (semidefinite programming). Under the Unique Games Conjecture, these relaxations often provide the best performance ratios available classically in polynomial time. Here, we discuss how to warm-start quantum optimization with an initial state corresponding to the solution of a relaxation of a combinatorial optimization problem and how to analyze properties of the associated quantum algorithms. In particular, this allows the quantum algorithm to inherit the performance guarantees of the classical algorithm. We illustrate this in the context of portfolio optimization, where our results indicate that warm-starting the Quantum Approximate Optimization Algorithm (QAOA) is particularly beneficial at low depth. Likewise, Recursive QAOA for MAXCUT problems shows a systematic increase in the size of the obtained cut for fully connected graphs with random weights, when Goemans-Williamson randomized rounding is utilized in a warm start. It is straightforward to apply the same ideas to other randomized-rounding schemes and optimization problems.
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Li, Yangyang, Mengzhuo Tian, Guangyuan Liu, Cheng Peng, and Licheng Jiao. "Quantum Optimization and Quantum Learning: A Survey." IEEE Access 8 (2020): 23568–93. http://dx.doi.org/10.1109/access.2020.2970105.

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Sayed, Gehad Ismail, Ashraf Darwish, and Aboul Ella Hassanien. "Quantum multiverse optimization algorithm for optimization problems." Neural Computing and Applications 31, no. 7 (November 1, 2017): 2763–80. http://dx.doi.org/10.1007/s00521-017-3228-9.

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van Apeldoorn, Joran, András Gilyén, Sander Gribling, and Ronald de Wolf. "Convex optimization using quantum oracles." Quantum 4 (January 13, 2020): 220. http://dx.doi.org/10.22331/q-2020-01-13-220.

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We study to what extent quantum algorithms can speed up solving convex optimization problems. Following the classical literature we assume access to a convex set via various oracles, and we examine the efficiency of reductions between the different oracles. In particular, we show how a separation oracle can be implemented using O~(1) quantum queries to a membership oracle, which is an exponential quantum speed-up over the Ω(n) membership queries that are needed classically. We show that a quantum computer can very efficiently compute an approximate subgradient of a convex Lipschitz function. Combining this with a simplification of recent classical work of Lee, Sidford, and Vempala gives our efficient separation oracle. This in turn implies, via a known algorithm, that O~(n) quantum queries to a membership oracle suffice to implement an optimization oracle (the best known classical upper bound on the number of membership queries is quadratic). We also prove several lower bounds: Ω(n) quantum separation (or membership) queries are needed for optimization if the algorithm knows an interior point of the convex set, and Ω(n) quantum separation queries are needed if it does not.
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Krotov, V. F. "Quantum system control optimization." Doklady Mathematics 78, no. 3 (December 2008): 949–52. http://dx.doi.org/10.1134/s1064562408060380.

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Maron, Adriano, Renata Reiser, Maurício Pilla, and Adenauer Yamin. "Quantum Processes: A Novel Optimization for Quantum Simulation." TEMA (São Carlos) 14, no. 3 (November 24, 2013): 399. http://dx.doi.org/10.5540/tema.2013.014.03.0399.

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The simulation of quantum algorithms in classic computers is a task which requires high processing and storing capabilities, limiting the size of quantum systems supported by the simulators. However, optimizations for reduction of temporal and spatial complexities are promising and expanding the capabilities of some simulators. The main contribution of this work consists in designing optimizations by the description of quantum transformations using Quantum Processes and Partial Quantum Processes conceived in the qGM theoretical model. These processes, when computed on the VPE-qGM execution environment, result in lower execution time and better performance, allowing the simulation of more complex quantum algorithms. The performance evaluation of this proposal was carried out by benchmarks used in similar works and included the sequential simulation of quantum algorithms up to 24 qubits. The results show a great improvement when compared to the previous version of the environment and indicate possibilities of advances in this research.
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Dissertations / Theses on the topic "Quantum optimization"

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Arvidsson, Elisabeth. "Optimization algorithms for Quantum Annealing." Thesis, KTH, Fysik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279447.

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Pye, Cory C. "Applications of optimization to quantum chemistry." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/nq23109.pdf.

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Gosset, David (David Nicholas). "Case studies in quantum adiabatic optimization." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/68872.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Physics, 2011.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 139-143).
Quantum adiabatic optimization is a quantum algorithm for solving classical optimization problems (E. Farhi, J. Goldstone, S. Gutmann, and M. Sipser. Quantum computation by adiabatic evolution, 2000. arXiv:quant-ph/0001106). The solution to an optimization problem is encoded in the ground state of a "problem Hamiltonian" Hp which acts on the Hilbert space of n spin 1/2 particles and is diagonal in the Pauli z basis. To produce this ground state, one first initializes the quantum system in the ground state of a different Hamiltonian and then adiabatically changes the Hamiltonian into Hp. Farhi et al suggest the interpolating Hamiltonian [mathematical formula] ... where the parameter s is slowly changed as a function of time between 0 and 1. The running time of this algorithm is related to the minimum spectral gap of H(s) for s E (0, 11. We study such transverse field spin Hamiltonians using both analytic and numerical techniques. Our approach is example-based, that is, we study some specific choices for the problem Hamiltonian Hp which illustrate the breadth of phenomena which can occur. We present I A random ensemble of 3SAT instances which this algorithm does not solve efficiently. For these instances H(s) has a small eigenvalue gap at a value s* which approaches 1 as n - oc. II Theorems concerning the interpolating Hamiltonian when Hp is "scrambled" by conjugating with a random permutation matrix. III Results pertaining to phase transitions that occur as a function of the transverse field. IV A new quantum monte carlo method which can be used to compute ground state properties of such quantum systems. We discuss the implications of our results for the performance of quantum adiabatic optimization algorithms.
by David Gosset.
Ph.D.
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Nuñez, Lobato Carlos. "Optimization of MOVPE-grown Quantum Dots for long distance quantum communication." Thesis, KTH, Tillämpad fysik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-258829.

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Stojnic, Mihailo Hassibi Babak Hassibi Babak. "Optimization algorithms in wireless and quantum communications /." Diss., Pasadena, Calif. : Caltech, 2008. http://resolver.caltech.edu/CaltechETD:etd-12032007-113628.

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Bai, Jing. "Optimization of Optical Nonlinearities in Quantum Cascade Lasers." Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/19797.

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Nonlinearities in quantum cascade lasers (QCL¡¯s) have wide applications in wavelength tunability and ultra-short pulse generation. In this thesis, optical nonlinearities in InGaAs/AlInAs-based mid-infrared (MIR) QCL¡¯s with quadruple resonant levels are investigated. Design optimization for the second-harmonic generation (SHG) of the device is presented. Performance characteristics associated with the third-order nonlinearities are also analyzed. The design optimization for SHG efficiency is obtained utilizing techniques from supersymmetric quantum mechanics (SUSYQM) with both material-dependent effective mass and band nonparabolicity. Current flow and power output of the structure are analyzed by self-consistently solving rate equations for the carriers and photons. Nonunity pumping efficiency from one period of the QCL to the next is taken into account by including all relevant electron-electron (e-e) and longitudinal (LO) phonon scattering mechanisms between the injector/collector and active regions. Two-photon absorption processes are analyzed for the resonant cascading triple levels designed for enhancing SHG. Both sequential and simultaneous two-photon absorption processes are included in the rate-equation model. The current output characteristics for both the original and optimized structures are analyzed and compared. Stronger resonant tunneling in the optimized structure is manifested by enhanced negative differential resistance. Current-dependent linear optical output power is derived based on the steady-state photon populations in the active region. The second-harmonic (SH) power is derived from the Maxwell equations with the phase mismatch included. Due to stronger coupling between lasing levels, the optimized structure has both higher linear and nonlinear output powers. Phase mismatch effects are significant for both structures leading to a substantial reduction of the linear-to-nonlinear conversion efficiency. The optimized structure can be fabricated through digitally grading the submonolayer alloys by molecular beam epitaxy (MBE). In addition to the second-order nonlinearity, performance characteristics brought by the third-order nonlinearities are also discussed, which include third-harmonic generation (THG) and intensity dependent (Kerr) refractive index. Linear to third-harmonic (TH) conversion efficiency is evaluated based on the phase-mismatched condition. The enhanced self-mode-locking (SML) effect over a typical three-level laser is predicted, which will stimulate further investigations of pulse duration shortening by structures with multiple harmonic levels.
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Alanis, Dimitrios. "Quantum-assisted multi-objective optimization of heterogeneous networks." Thesis, University of Southampton, 2017. https://eprints.soton.ac.uk/419588/.

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Some of the Heterogeneous Network (HetNet) components may act autonomously for the sake of achieving the best possible performance. The attainable routing performance depends on a delicate balance of diverse and often conflicting Quality-of-Service (QoS)requirements. Finding the optimal solution typically becomes an NP-hard problem, as the network size increases in terms of the number of nodes. Moreover, the employment of user defined utility functions for the aggregation of the different objective functions often leads to suboptimal solutions. On the other hand, Pareto Optimality is capable of amalgamating the different design objectives by relying on an element of elitism. Although there is a plethora of bio-inspired algorithms that attempt to address the associated multi-component optimization problem, they often fail to generate all the routes constituting the Optimal Pareto Front (OPF). As a remedy, we initially propose an optimal multi-objective quantum-assisted algorithm, namely the Non-dominated Quantum Optimization (NDQO) algorithm, which evaluates the legitimate routes using the concept of Pareto Optimality at a reduced complexity. We then compare the performance of the NDQO algorithm to the state-of-the-art evolutionary algorithms, demonstrating that the NDQO algorithm achieves a near-optimal performance. Furthermore, we analytically derive the upper and lower bounds of the NDQO’s algorithmic complexity, which is of the order of O(N) and O(N√N) in the best- and worst-case scenario, respectively. This corresponds to a substantial complexity reduction of the NDQO from the order of O(N2)imposed by the brute-force (BF) method. However again, as the number of nodes increases, the total number of routes increases exponentially, making its employment infeasible despite the complexity reduction offered. Therefore, we propose a novel optimal quantum-assisted algorithm, namely the Non-Dominated Quantum Iterative Optimization (NDQIO) algorithm, which exploits the synergy between the hardware parallelism and the quantum parallelism for the sake of achieving a further complexity reduction, which is on the order of O(√N) and O(N√N)in the best- and worst-case scenarios, respectively. Additionally, we provide simulation results for demonstrating that our NDQIO algorithm achieves an average complexity reduction of almost an order of magnitude compared to the near-optimal NDQO algorithm,while activating the same order of comparison operators. Apart from the traditional QoS requirements, the network design also has to consider the nodes’ user-centric social behavior. Hence, the employment of socially-aware load balancing becomes imperative for avoiding the potential formation of bottlenecks in the network’s packet-flow. Therefore, we also propose a novel algorithm, referred to as the Multi-Objective Decomposition Quantum Optimization (MODQO) algorithm, which exploits the quantum parallelism to its full potential by exploiting the database correlations for performing multi-objective routing optimization, while at the same time balancing the tele-traffic load among the nodes without imposing a substantial degradation on the network’s delay and power consumption. Furthermore, we introduce a novel socially-aware load balancing metric, namely the normalized entropy of the normalized composite betweenness of the associated socially-aware network, for striking a better trade-off between the network’s delay and power consumption. We analytically prove that the MODQO algorithm achieves the full-search based accuracy at a significantly reduced complexity, which is several orders of magnitude lower than that of the full-search. Finally, we compare the MODQO algorithm to the classic NSGA-II evolutionary algorithm and demonstrate that the MODQO succeeds in halving the network’s average delay, whilst simultaneously reducing the network’s average power consumption by 6 dB without increasing the computational complexity.
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Titiloye, Olawale. "Optimization by quantum annealing for the graph colouring problem." Thesis, Manchester Metropolitan University, 2013. http://e-space.mmu.ac.uk/324247/.

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Quantum annealing is the quantum equivalent of the well known classical simulated annealing algorithm for combinatorial optimization problems. Despite the appeal of the approach, quantum annealing algorithms competitive with the state of the art for specific problems hardly exist in the literature. Graph colouring is a difficult problem of practical significance that can be formulated as combinatorial optimization. By introducing a symmetry-breaking problem representation, and finding fast incremental techniques to calculate energy changes, a competitive graph colouring algorithm based on quantum annealing is derived. This algorithm is further enhanced by tuning simplification techniques; replica spacing techniques to increase robustness; and a messaging protocol, which enables quantum annealing to efficiently take advantage of multiprocessor environments. Additionally, observations of some patterns in the tuning for random graphs led to a more effective algorithm able to find new upper bounds for several widely-used benchmark graphs, some of which had resisted improvement in the last two decades.
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Khalus, Vladislav Ivanovich. "T-COUNT OPTIMIZATION OF QUANTUM CARRY LOOK-AHEAD ADDER." UKnowledge, 2019. https://uknowledge.uky.edu/ece_etds/141.

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With the emergence of quantum physics and computer science in the 20th century, a new era was born which can solve very difficult problems in a much faster rate or problems that classical computing just can't solve. In the 21st century, quantum computing needs to be used to solve tough problems in engineering, business, medical, and other fields that required results not today but yesterday. To make this dream come true, engineers in the semiconductor industry need to make the quantum circuits a reality. To realize quantum circuits and make them scalable, they need to be fault tolerant, therefore Clifford+T gates need to be implemented into those circuits. But the main issue is that in the Clifford+T gate set, T gates are expensive to implement. Carry Look-Ahead addition circuits have caught the interest of researchers because the number of gate layers encountered by a given qubit in the circuit (or the circuit's depth) is logarithmic in terms of the input size n. Therefore, this thesis focuses on optimizing previous designs of out-of-place and in-place Carry Look-Ahead Adders to decrease the T-count, sum of all T and T Hermitian transpose gates in a quantum circuit.
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Rava, Andrea Basilio. "Quantum approximate optimization algorithm: combinatorial problems and classical statistical models." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23113/.

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The Quantum Approximate Optimization Algorithm (QAOA) is a hybrid quantum-classical algorithm for solving combinatorial optimization problems. Since most of combinatorial optimization problems may be thought as particular instances of Ising Hamiltonians, the study of the QAOA is very relevant from the physical point of view for its potential applications in describing physical systems. In the QAOA a quantum state is prepared and, through 2p parameterized quantum evolutions, a final state which represents an extreme of cost function and encodes the approximate solution of the problem is obtained. The 2p parameters are determined through a classical parameter optimization process. In this work we apply QAOA to two different problems, the Max Cut and the random bond Ising Model (RBIM). For both problems we perform an analysis of the optimization efficiency, verifying that the quality of the approximation increases with p. For the Max Cut we perform a further analysis of the p=1 case for which we obtain an analytical expression for the cost function and make observations regarding the choice of the initial parameters in the optimization procedure. For the RBIM, for different disordered configurations we obtain the ground states energies and magnetizations for different lattice sizes and different level p of the optimisation. We observe that, even if the magnetisation is obtained for small lattice sizes, its behaviour suggests the presence of a transition separating a ferromagnetic from a paramagnetic phase.
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Books on the topic "Quantum optimization"

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Feld, Sebastian, and Claudia Linnhoff-Popien, eds. Quantum Technology and Optimization Problems. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14082-3.

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Das, Arnab, and Bikas K. Chakrabarti, eds. Quantum Annealing and Other Optimization Methods. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11526216.

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Cruz-Santos, William, and Guillermo Morales-Luna. Approximability of Optimization Problems through Adiabatic Quantum Computation. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-031-02519-8.

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Xiao, Chong. Synthesis and Optimization of Chalcogenides Quantum Dots Thermoelectric Materials. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-49617-6.

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Quantum quenching, annealing and computation. Heidelberg: Springer, 2010.

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Nightingale, M. P. Monte Carlo optimization of trial wave functions in quantum mechanics and statistical mechanics. Ithaca, N.Y: Cornell Theory Center, Cornell University, 1996.

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Choi-Hong, Lai, and Wu Xiao-Jun, eds. Particle swarm optimisation: Classical and quantum perspectives. Boca Raton: CRC Press, 2011.

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Antonelli, P. L. Finslerian Geometries: A Meeting of Minds. Dordrecht: Springer Netherlands, 2000.

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Loss, Michael. Inequalities: Selecta of Elliott H. Lieb. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002.

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Lieb, Elliott H. The stability of matter: From atoms to stars : selecta of Elliott H. Lieb. Berlin: Springer-Verlag, 1991.

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Book chapters on the topic "Quantum optimization"

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Yamakami, Tomoyuki. "Quantum Optimization Problems." In Unconventional Models of Computation, 300–314. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45833-6_25.

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Kommadi, Bhagvan. "Quantum Optimization Algorithms." In Quantum Computing Solutions, 125–38. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6516-1_6.

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Fefferman, Charles L., and Luis A. Seco. "Interval Arithmetic in Quantum Mechanics." In Applied Optimization, 145–67. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-3440-8_7.

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Du, Ke-Lin, and M. N. S. Swamy. "Quantum Computing." In Search and Optimization by Metaheuristics, 283–93. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41192-7_17.

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Protopopescu, V., and J. Barhen. "Quantum Algorithm for Continuous Global Optimization." In Applied Optimization, 293–303. Boston, MA: Springer US, 2005. http://dx.doi.org/10.1007/0-387-24255-4_13.

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Kasirajan, Venkateswaran. "Adiabatic Optimization and." In Fundamentals of Quantum Computing, 365–73. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63689-0_8.

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Chandra, Anjan Kumar, and Bikas K. Chakrabarti. "Optimization and Quantum Annealing." In Computational Statistical Physics, 251–77. Gurgaon: Hindustan Book Agency, 2011. http://dx.doi.org/10.1007/978-93-86279-50-7_9.

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Cariolaro, Gianfranco. "Quantum Decision Theory: Analysis and Optimization." In Quantum Communications, 183–249. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15600-2_5.

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Anschuetz, Eric, Jonathan Olson, Alán Aspuru-Guzik, and Yudong Cao. "Variational Quantum Factoring." In Quantum Technology and Optimization Problems, 74–85. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14082-3_7.

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Bhattacharyya, Siddhartha, Mario Köppen, Elizabeth Behrman, and Ivan Cruz-Aceves. "Function Optimization Using IBM Q." In Hybrid Quantum Metaheuristics, 37–56. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003283294-3.

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Conference papers on the topic "Quantum optimization"

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Bantysh, Boris I., and Yurii I. Bogdanov. "Quantum tomography for quantum systems optimization." In International Conference on Micro- and Nano-Electronics 2021, edited by Konstantin V. Rudenko and Vladimir F. Lukichev. SPIE, 2022. http://dx.doi.org/10.1117/12.2624610.

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El Gaily, Sara, and Sandor Imre. "Constrained Quantum Optimization Algorithm." In 2021 20th International Symposium INFOTEH-JAHORINA (INFOTEH). IEEE, 2021. http://dx.doi.org/10.1109/infoteh51037.2021.9400679.

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Golden, John, Andreas Bartschi, Daniel O'Malley, and Stephan Eidenbenz. "Threshold-Based Quantum Optimization." In 2021 IEEE International Conference on Quantum Computing and Engineering (QCE). IEEE, 2021. http://dx.doi.org/10.1109/qce52317.2021.00030.

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Chen, Yanzhu, Linghua Zhu, Nicholas J. Mayhall, Edwin Barnes, and Sophia E. Economou. "How Much Entanglement Do Quantum Optimization Algorithms Require?" In Quantum 2.0. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/quantum.2022.qm4a.2.

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ADAPT-QAOA, a novel problem-tailored version of quantum approximate optimization algorithm, speeds up convergence using entangling operators while reducing the total number of CNOTs. We explore how much entanglement is required to speed up optimization algorithms.
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Li, Fei, Yuting Zhang, Jiulong Wu, and Haibo Li. "Quantum bacterial foraging optimization algorithm." In 2014 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2014. http://dx.doi.org/10.1109/cec.2014.6900230.

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Kerenidis, Iordanis, Anupam Prakash, and Dániel Szilágyi. "Quantum Algorithms for Portfolio Optimization." In AFT '19: 1st ACM Conference on Advances in Financial Technologies. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3318041.3355465.

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Gacon, Julien, Christa Zoufal, and Stefan Woerner. "Quantum-Enhanced Simulation-Based Optimization." In 2020 IEEE International Conference on Quantum Computing and Engineering (QCE). IEEE, 2020. http://dx.doi.org/10.1109/qce49297.2020.00017.

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Guzik, Vyacheslav, Sergey Gushanskiy, Evgeny Kubrakov, and Maxim Polenov. "Quantum gates transform optimization algorithm for quantum computer modeling." In 2015 9th International Conference on Application of Information and Communication Technologies (AICT). IEEE, 2015. http://dx.doi.org/10.1109/icaict.2015.7338573.

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Enomoto, Yutaro, Keitaro Anai, Kenta Udagawa, and Shuntaro Takeda. "Quantum Approximate Optimization for Continuous Problems on a Programmable Photonic Quantum Computer." In Frontiers in Optics. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/fio.2022.fm5b.3.

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We demonstrate a continuous-variable version of the quantum approximate optimization algorithm on a programmable single-mode photonic quantum computer, minimizing one-variable continuous functions. The results highlight the potential of continuous-variable quantum computing in near-term applications.
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Gupta, Ratnesh K., Jesse L. Everett, Aaron D. Tranter, René Henke, Vandna Gokhroo, Ping Koy Lam, and Síle Nic Chormaic. "Machine learner optimization of atom loading in optical nanofiber evanescent dipole traps." In Quantum 2.0. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/quantum.2022.qw2a.45.

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We use an online machine learning algorithm to optimize cooling and loading of rubidium-87 atoms into an evanescent dipole trap array along an optical nanofiber, increasing the number of trapped atoms by 50%.
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Reports on the topic "Quantum optimization"

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Bilbro, Griff. Quantum Optimization. Fort Belvoir, VA: Defense Technical Information Center, August 2000. http://dx.doi.org/10.21236/ada384604.

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Parekh, Ojas D., Ciaran Ryan-Anderson, and Sevag Gharibian. Quantum Optimization and Approximation Algorithms. Office of Scientific and Technical Information (OSTI), January 2019. http://dx.doi.org/10.2172/1492737.

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Adachi, Steven, João Caldeira, Andrea Delgado, Kathleen Hamilton, Travis Humble, Joshua Job, James Kowalkowski, et al. HEP ML/Optimization Go Quantum – QuantISED Pilot. Office of Scientific and Technical Information (OSTI), April 2020. http://dx.doi.org/10.2172/1616301.

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Ruskai, Mary Beth. Optimization of Communication in Noisy Quantum Channels. Fort Belvoir, VA: Defense Technical Information Center, September 2002. http://dx.doi.org/10.21236/ada413565.

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Parekh, Ojas D., Jeremy D. Wendt, Luke Shulenburger, Andrew J. Landahl, Jonathan Edward Moussa, and John B. Aidun. Benchmarking Adiabatic Quantum Optimization for Complex Network Analysis. Office of Scientific and Technical Information (OSTI), April 2015. http://dx.doi.org/10.2172/1459086.

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Nielsen, Erik, Xujiao Gao, Irina Kalashnikova, Richard Partain Muller, Andrew Gerhard Salinger, and Ralph Watson Young. QCAD simulation and optimization of semiconductor double quantum dots. Office of Scientific and Technical Information (OSTI), December 2013. http://dx.doi.org/10.2172/1204068.

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Coffrin, Carleton James. Challenges with Chains: Testing the Limits of a D-Wave Quantum Annealer for Discrete Optimization. Office of Scientific and Technical Information (OSTI), February 2019. http://dx.doi.org/10.2172/1498001.

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Handel, Peter H. Quantum 1/f Optimization of New Materials and Devices, Multiplexers, Low-Power Electronics and Investigation of 1/f Negative Entropystates. Fort Belvoir, VA: Defense Technical Information Center, February 1999. http://dx.doi.org/10.21236/ada380319.

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