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Nanosilicon for quantum information
Published in Klaus D. Sattler, Silicon Nanomaterials Sourcebook, 2017
On the other hand, there are analog quantum computers. Quantum annealing has been proposed as the quantum analogue of simulated annealing to solve optimization problems that can be reduced to finding the ground state of an Ising spin system. Such problems are of the quadratic unconstrained binary optimization (QUBO) type (Wang et al. 2016). In simulated annealing, thermal fluctuations are simulated on a traditional CPU to let the system hop from state to state over intermediate energy barriers to search for the desired lowest-energy state. In quantum annealing, quantum-mechanical tunneling through the energy barriers replaces and is supposed to outperform thermal fluctuations. Similar to simulated annealing, quantum annealing is a generic algorithm, applicable, in principle, to any QUBO problem. It provides a method to reach a solution of a specified optimality level within a given finite number of annealing runs. By tuning the local fields and coupling strengths of the Ising system and by running the evolution to the solution sufficiently slowly (adiabatically, as quantitatively described by the adiabatic theorem [Morita and Nishimori 2008]), the system moves from the ground state of the transverse field to the ground state of the coupling strength system, which represents the problem to be solved. The existing example of D-Wave machines manages eight superconducting qubit registers but at the same time their multi-micrometric size limits the coherence across many registers (Ladd et al. 2010).
Function Optimization Using IBM Q
Published in Siddhartha Bhattacharyya, Mario Köppen, Elizabeth Behrman, Ivan Cruz-Aceves, Hybrid Quantum Metaheuristics, 2022
Siddhartha Bhattacharyya, Mario Köppen, Elizabeth Behrman, Ivan Cruz-Aceves
Quantum annealing algorithm such as Quantum Processing Unit (QPU) is applicable for solving binary optimization problems [11]. In [12], quantum annealer has been utilized to optimize the traffic flow, as mentioned by [13]. The QPU is designed to solve Quadratic Unconstrained Binary Optimization (QUBO) problems, where each qubit represents a variable and couplers between qubits represent the costs associated with qubit pairs. Quantum annealing algorithm has been used in [14] to resolve the Nurse Scheduling Problem (NSP), which arises when searching the optimal schedule for a set of available nurses to create a rotating roaster.
Routing and wavelength assignment with protection: A quadratic unconstrained binary optimization approach enabled by Digital Annealer technology
Published in IISE Transactions, 2023
Oylum Şeker, Merve Bodur, Hamed Pouya
A plausible alternative to tackle such problems is to formulate them as QUBO models and generate solutions via novel computational architectures and new technologies, such as adiabatic quantum computing (e.g., Papalitsas et al., 2019), neuromorphic computing (e.g., Corder et al., 2018), and optical parametric oscillators (e.g., Inagaki et al. 2016), which have recently attracted significant attention due to their capability in tackling combinatorial optimization problems. A promising example to these new technologies is DA (Aramon et al., 2019), which is a computer architecture that rivals quantum computers in utility (Boyd, 2018). DA is designed to solve QUBO models, and uses an algorithm based on simulated annealing. In many applications, such as the minimum vertex cover problem (Javad-Kalbasi et al., 2019), maximum clique problem (Naghsh et al., 2019) and outlier rejection (Rahman et al., 2019), it has been shown to significantly improve upon the state of the art and yield high-quality solutions in radically short amount of time.