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Analyzing Heuristic-based Randomized Search Strategies for the Quantum Circuit Compilation Problem

100%

Oddi A. , Rasconi R.

Fundamenta Informaticae

2020

tom Vol. 174, nr 3/4

259--281

In this work we investigate the performance of greedy randomised search (GRS) techniques to the problem of compiling quantum circuits to emerging quantum hardware. Quantum computing (QC) represents the next big step towards power consumption minimisation and CPU speed boost in the future of computing machines. Quantum computing uses quantum gates that manipulate multi-valued bits (qubits ). A quantum circuit is composed of a number of qubits and a series of quantum gates that operate on those qubits, and whose execution realises a specific quantum algorithm. Current quantum computing technologies limit the qubit interaction distance allowing the execution of gates between adjacent qubits only. This has opened the way to the exploration of possible techniques aimed at guaranteeing nearest-neighbor (NN) compliance in any quantum circuit through the addition of a number of so-called swap gates between adjacent qubits. In addition, technological limitations (decoherence effect) impose that the overall duration (makespan) of the quantum circuit realization be minimized. One core contribution of the paper is the definition of two lexicographic ranking functions for quantum gate selection, using two keys: one key acts as a global closure metric to minimise the solution makespan; the second one is a local metric, which favours the mutual approach of the closest qstates pairs. We present a GRS procedure that synthesises NN-compliant quantum circuits realizations, starting from a set of benchmark instances of different size belonging to the Quantum Approximate Optimization Algorithm (QAOA) class tailored for the MaxCut problem. We propose a comparison between the presented meta-heuristics and the approaches used in the recent literature against the same benchmarks, both from the CPU efficiency and from the solution quality standpoint. In particular, we compare our approach against a reference benchmark initially proposed and subsequently expanded in [1] by considering: (i) variable qubit state initialisation and (ii) crosstalk constraints that further restrict parallel gate execution.

Power loss minimization by voltage transformer turns ratio selection based on Particle Swarm Optimization

84%

Manusov V. Z. , Matrenin P. V. , Khasanzoda N.

Przegląd Elektrotechniczny

2019

tom R. 95, nr 8

127--131

The research considers the optimization of the taps position of voltage transformers to minimize power loss. The Particle Swarm Optimization algorithm is implemented to this optimization problem. The advantage of this algorithm is the ability to adapt to an optimization problem. It was found out that the Particle Swarm Optimization algorithm is more productive than the greedy heuristic algorithm based on the division of this optimization problem into subtasks. Also, the paper studied the influence of particle velocity restriction on the efficiency of the algorithm.

W pracy analizowano metode optymalizacji strat transformatora przez dobór stosunku uzwojeń. Do tego celu wykorzystano algorytm genetyczny PSO. Porównano prace układu z innymi algorytmami adaptacyjnymi.