Quantum-inspired optimization for wavelength assignment
A. S. Boev,S. R. Usmanov,Alexander M. Semenov,M. M. Ushakova,G. V. Salahov,A.S. Mastiukova,Evgeniy O. Kiktenko,Aleksey Fedorov +7 more
TL;DR: In this article, the authors proposed and developed a quantum-inspired algorithm for solving the wavelength assignment problem in optical communications networks, and compared it with classical heuristic and industrial combinatorial solvers.
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Abstract: Problems related to wavelength assignment (WA) in optical communications networks involve allocating transmission wavelengths for known transmission paths between nodes that minimize a certain objective function, for example, the total number of wavelengths. Playing a central role in modern telecommunications, this problem belongs to NP-complete class for a general case so that obtaining optimal solutions for industry-relevant cases is exponentially hard. In this work, we propose and develop a quantum-inspired algorithm for solving the wavelength assignment problem. We propose an advanced embedding procedure to transform this problem into the quadratic unconstrained binary optimization (QUBO) form, having a improvement in the number of iterations with price-to-pay being a slight increase in the number of variables (“spins”). Then, we compare a quantum-inspired technique for solving the corresponding QUBO form against classical heuristic and industrial combinatorial solvers. The obtained numerical results indicate on an advantage of the quantum-inspired approach in a substantial number of test cases against the industrial combinatorial solver that works in the standard setting. Our results pave the way to the use of quantum-inspired algorithms for practical problems in telecommunications and open a perspective for further analysis of the use of quantum computing devices.
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Citations
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Technique for Transforming Discrete Optimization Problems into QUBO Form
A.M. Semenov,S. R. Usmanov,A. K. Fedorov,A.M. Semenov,S. R. Usmanov,A. K. Fedorov +5 more
Abstract: Practical discrete optimization problems often contain multidimensional arrays of variables interrelated by linear constraints such as equalities and inequalities. Values of each variable depend on its specific meaning and can be binary, integer, or discrete. These conditions make it technically difficult to reduce the original problem statement to QUBO form. We identify and examine three necessary transformations of the original problem statement to reduce it to QUBO form, namely transition from a multidimensional to a one-dimensional array, transition to binary variables in mixed problems, and incorporating linear constraints into the objective function in the form of quadratic penalties. We present and prove computationally convenient formulas to simplify these transformations. In particular, the formulas for the transition from a multidimensional to a one-dimensional array of variables are based on the application of the Kronecker product of matrices. The transformations considered are illustrated by numerous examples and used, as an application, to reduce a number of well-known problems in graph theory and combinatorial optimization to QUBO form.
References
•Posted Content
A Quantum Approximate Optimization Algorithm
TL;DR: A quantum algorithm that produces approximate solutions for combinatorial optimization problems that depends on a positive integer p and the quality of the approximation improves as p is increased, and is studied as applied to MaxCut on regular graphs.
3.1K
Ising formulations of many NP problems
TL;DR: This work collects and extends mappings to the Ising model from partitioning, covering and satisfiability, and provides Ising formulations for many NP-complete and NP-hard problems, including all of Karp's 21NP-complete problems.
2.5K
•Posted Content
Quantum Computation by Adiabatic Evolution
TL;DR: In this article, a quantum algorithm for solving instances of the satisfiability problem, based on adiabatic evolution, is given, where the evolution of the quantum state is governed by a time-dependent Hamiltonian that interpolates between an initial Hamiltonian and a final Hamiltonian, whose ground state encodes the satisfying assignment.
1.5K
Adiabatic quantum computation
Tameem Albash,Daniel A. Lidar +1 more
TL;DR: In this paper, the equivalence of the adiabatic and circuit models of quantum computation has been proved, and the placement of quantum computations in the more general classification of computational complexity theory is discussed.
1.2K
Colloquium: Quantum annealing and analog quantum computation
Arnab Das,Bikas K. Chakrabarti +1 more
TL;DR: The recent success in quantum annealing, i.e., optimization of the cost or energy functions of complex systems utilizing quantum fluctuations, is reviewed in this paper, where the concept is introduced in successive steps through studying the mapping of such computationally hard problems to classical spin-glass problems.