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Quantum Multiplexer Designing and Optimization applying Genetic Algorithm

Debarka Mukhopadhyay, Amalendu Si

Published Jan 1, 2010• 0 citations
OpenAlex

Abstract

This paper shows how to design efficient quantum multiplexer circuit borrowed from classical computer design. The design will show that it is composed of some Toffole gates or C 2 NOT gate and some two input CNOT gates. Every C 2 NOT gate is synthesized and optimized by applying the genetic algorithm to get the best possible combination for the design of these gate circuits. gate. It is a three input gate. The first two inputs are the controlled inputs and the third one is the target input. This gate has a 3-bit input and output. If the first two bits are set, it flips the third bit. Following is a table over the input and output bits: Keywords:

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This paper shows how to design efficient quantum multiplexer circuit borrowed from classical computer design. The design will show that it is composed of some Toffole gates or C 2 NOT gate and some two input CNOT gates. Every C 2 NOT gate is synthesized and optimized by applying the genetic algorithm to get the best possible combination for the design of these gate circuits. gate. It is a three input gate. The first two inputs are the controlled inputs and the third one is the target input. This gate has a 3-bit input and output. If the first two bits are set, it flips the third bit. Following is a table over the input and output bits: Keywords:
Due to the non- intuitive nature of Quantum algorithms, it becomes difficult for a classically trained person to efficiently construct new ones. So rather than designing new algorithms manually, lately, Genetic algorithms (GA) are being implemented for this purpose. GA is a technique to automatically solve a problem using principles of Darwinian evolution. This has been implemented to explore the possibility of evolving an n-qubit circuit when the circuit matrix has been provided using a set of single, two and three qubit gates. Using a variable length population and universal stochastic selection procedure, a number of possible solution circuits, with different number of gates can be obtained for the same input matrix during different runs of GA. The given algorithm has also been successfully implemented to obtain two and three qubit Boolean circuits using Quantum gates. The results demonstrate the effectiveness of the GA procedure even when the search spaces are large.
The majority of work in reversible logic circuits has been limited to combinational logic. Researchers are now beginning to suggest designs for sequential circuits. In this paper we propose a new method to design and optimize feedback reversible logic circuits and a specific group of quantum logic circuits based on the reversible state transition table and genetic algorithms (GA). To show the efficiency of the proposed method, some reversible sequential elements such as D and T flip-flops (FFs), with and without clock and reset, and edge triggered FFs are designed. We have also extended our method to multiple loop feedback circuits. The proposed circuits are highly optimized using a GA synthesis tool that allows don't care values. Some of the designs in this paper are presented in other papers; however, the comparisons show that the quantum cost and number of garbage inputs/outputs are reduced efficiently by our method.
Multiple-valued logic is a promising choice for future computer technologies, which provides a set of advantages comparing to binary circuits. In this paper, we have developed a genetic algorithm-based synthesis of ternary reversible circuits using Muthukrishnan-Stroud gates. The method for chromosomes coding, as well as a reasonable choice of algorithm parameters, allowed obtaining circuits for ternary Toffoli and modified Fredkin gates, which are better than other published methods in terms of quantum cost, delay times and amount of input ancillary and output garbage qutrits.
Quantum algorithms will likely play a key role in future high-performance-computing (HPC) environments. These algorithms are typically expressed as quantum circuits composed of arbitrary gates or as unitary matrices. Executing these on physical devices, however, requires translation to device-compatible circuits, in a process called quantum compilation or circuit synthesis, since these devices support a limited number of native gates. Moreover, these devices typically have specific qubit topologies, which constrain how and where gates can be applied. Consequently, logical qubits in input circuits and unitaries may need to be mapped to and routed between physical qubits. Furthermore, current Noisy Intermediate-Scale Quantum (NISQ) devices present additional constraints. They are vulnerable to errors during gate application and their short decoherence times lead to qubits rapidly succumbing to accumulated noise and possibly corrupting computations. Therefore, circuits synthesized for NISQ devices need to minimize gates and execution times. The problem of synthesizing device-compatible circuits, while optimizing for low gate count and short execution times, can be shown to be computationally intractable using analytical methods. Therefore, interest has grown towards heuristics-based synthesis techniques, which are able to produce approximations of the desired algorithm, while optimizing depth and gate-count. In this work, we investigate using genetic algorithms (GA)—a proven gradient-free optimization technique based on natural selection—for circuit synthesis. In particular, we formulate the quantum synthesis problem as a multi-objective optimization (MOO) problem, with the objectives of minimizing the approximation error, number of multi-qubit gates, and circuit depth. We also employ fuzzy logic for runtime parameter adaptation of GA to enhance search efficiency and solution quality in our proposed method.
Typical methods of quantum/reversible synthesis are based on using the binary character of quantum computing.However, multi-valued logic is a promising choice for future computer technologies, given a set of advantages when comparing to binary circuits.In this work, we have developed a genetic algorithm-based synthesis of ternary reversible circuits using Muthukrishnan-Stroud gates.The method for chromosomes coding that we present, as well as a judicious choice of algorithm parameters, allowed obtaining circuits for half-adder and full adder which are better than other published methods in terms of cost, delay times and amount of input ancillary bits.A structure of the circuits is analyzed in details, based on their decomposition.