Analog Integrated Circuit Design Automation

Martins, Ricardo; Lourenço, Nuno; Horta, Nuno

doi:10.1007/978-3-319-34060-9

Cited by 21 publications

(35 citation statements)

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“…Let σ 1 , σ 2 be a symmetry pair [31][32][33] that refers to two matched cells placed symmetrically in relation to S v (P i ), with bottom-left coordinates σ 1 ,…”

Section: Methods 1 Quantum Wiring Optimization and Objective Function mentioning

confidence: 99%

“…with the relation y 1 i = y 2 i = y i . Let σ S = x S i , y S i be a cell which is placed centered [31][32][33] with respect to S v (P i ). Then, x Sv(P i ) can be evaluated as…”

Section: Methods 1 Quantum Wiring Optimization and Objective Function mentioning

confidence: 99%

“…The simultaneous physical-layer minimization and the maximization of the objective function are achieved by the Quantum Triple Annealing Minimization (QTAM) algorithm. The QTAM algorithm utilizes the framework of simulated annealing (SA) [31][32][33][35][36][37][38], which is a stochastic point-to-point search method.…”

Section: System Modelmentioning

confidence: 99%

“…The optimization process includes diverse objective functions, constraints, and optimization criteria to improve the performance of the quantum circuit and to take into consideration the hardware restrictions of quantum computers. In the proposed QTAM algorithm the constraints are endorsed by the modification of the Pareto dominance [31][32][33]35] values by the different sums of constraint violation values. We defined three different constraint violation values.…”

Section: Constraint Violationsmentioning

confidence: 99%

“…Following the complexity analysis of [31][32][33]35], the computational complexity of QTAM is evaluated as…”

Section: Computational Complexity Of Qtammentioning

confidence: 99%

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Quantum circuit design for objective function maximization in gate-model quantum computers

Gyöngyösi

Imre

2019

Quantum Inf Process

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Gate-model quantum computers provide an experimentally implementable architecture for near term quantum computations. To design a reduced quantum circuit that can simulate a high complexity reference quantum circuit, an optimization should be taken on the number of input quantum states, on the unitary operations of the quantum circuit, and on the number of output measurement rounds. Besides the optimization of the physical layout of the hardware layer, the quantum computer should also solve difficult computational problems very efficiently. To yield a desired output system, a particular objective function associated with the computational problem fed into the quantum computer should be maximized. The reduced gate structure should be able to produce the maximized value of the objective function. These parallel requirements must be satisfied simultaneously, which makes the optimization difficult. Here, we demonstrate a method for designing quantum circuits for gate-model quantum computers and define the Quantum Triple Annealing Minimization (QTAM) algorithm. The aim of QTAM is to determine an optimal reduced topology for the quantum circuits in the hardware layer at the maximization of the objective function of an arbitrary computational problem.

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“…Let σ 1 , σ 2 be a symmetry pair [31][32][33] that refers to two matched cells placed symmetrically in relation to S v (P i ), with bottom-left coordinates σ 1 ,…”

Section: Methods 1 Quantum Wiring Optimization and Objective Function mentioning

confidence: 99%

“…with the relation y 1 i = y 2 i = y i . Let σ S = x S i , y S i be a cell which is placed centered [31][32][33] with respect to S v (P i ). Then, x Sv(P i ) can be evaluated as…”

Section: Methods 1 Quantum Wiring Optimization and Objective Function mentioning

confidence: 99%