Evolving blackbox quantum algorithms using genetic programming

Stadelhofer, Ralf; Banzhaf, Wolfgang; Suter, Dieter

doi:10.1017/s089006040800019x

Cited by 13 publications

(12 citation statements)

References 88 publications

(171 reference statements)

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“…Stadelhofer, like Leier, has also written a recent PhD thesis on evolving quantum algorithms [107]. It is noticeable that Stadelhofer approaches the field with greater thought to physical implementation of the evolved quantum algorithms, most probably as a result of his physics (rather than computer science) background.…”

Section: Stadelhofer Et Almentioning

confidence: 99%

“…It is noticeable that Stadelhofer approaches the field with greater thought to physical implementation of the evolved quantum algorithms, most probably as a result of his physics (rather than computer science) background. Two papers [108,109] co-authored with Stadelhofer's thesis supervisors, Banzaf and Suter, have been published on his major findings that are presented in Chapter 5 of his thesis [107]. New quantum algorithms to solve two different oracle problems, namely the parity [108,109] and a specific case of the hidden subgroup problem [109], have been evolved.…”

Section: Stadelhofer Et Almentioning

confidence: 99%

“…When the problem is formulated as an oracle problem, a classical algorithm needs to call the oracle for every bit in the string, but it had been previously shown by other researchers that quantum algorithms needed only half that many oracle calls [109]. Stadelhofer et al evolved such a quantum algorithm that was additionally scalable and more efficient than previously known quantum algorithms in terms of the number of gates and measurements required.…”

Section: Stadelhofer Et Almentioning

confidence: 99%

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A review of procedures to evolve quantum algorithms

Gepp

Stocks

2009

Genet Program Evolvable Mach

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There exist quantum algorithms that are more efficient than their classical counterparts; such algorithms were invented by Shor in 1994 and then Grover in 1996. A lack of invention since Grover's algorithm has been commonly attributed to the non-intuitive nature of quantum algorithms to the classically trained person. Thus, the idea of using computers to automatically generate quantum algorithms based on an evolutionary model emerged. A limitation of this approach is that quantum computers do not yet exist and quantum simulation on a classical machine has an exponential order overhead. Nevertheless, early research into evolving quantum algorithms has shown promise.This paper provides an introduction into quantum and evolutionary algorithms for the computer scientist not familiar with these fields. The exciting field of using evolutionary algorithms to evolve quantum algorithms is then reviewed.1 A more detailed and structured account of Feynman's idea was given in his book [21].

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Section: Stadelhofer Et Almentioning

confidence: 99%

Section: Stadelhofer Et Almentioning

confidence: 99%

Section: Stadelhofer Et Almentioning

confidence: 99%

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A review of procedures to evolve quantum algorithms

Gepp

Stocks

2009

Genet Program Evolvable Mach

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“…GAs borrow their optimization protocol from the basic tenets of evolutionary biology, wherein the breeding strategy of a population is to increase the fitness levels and offspring-producing capability of individuals by crossing over of genetic information [40]. In quantum information processing, GAs have been used to optimize quantum algorithms [41][42][43] and quantum entanglement [44], for optimal dynamical decoupling [45], and to optimize unitary transformations for a general quantum computation [46,47].…”

Section: Introductionmentioning

confidence: 99%

Efficient experimental design of high-fidelity three-qubit quantum gates via genetic programming

Devra

Prabhu

Singh

et al. 2018

Quantum Inf Process

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We have designed efficient quantum circuits for the three-qubit Toffoli (controlled-controlled NOT) and the Fredkin (controlled-SWAP) gate, optimized via genetic programming methods. The gates thus obtained were experimentally implemented on a three-qubit NMR quantum information processor, with a high fidelity. Toffoli and Fredkin gates in conjunction with the single-qubit Hadamard gates form a universal gate set for quantum computing, and are an essential component of several quantum algorithms. Genetic algorithms are stochastic search algorithms based on the logic of natural selection and biological genetics and have been widely used for quantum information processing applications. The numerically optimized rf pulse profiles of the three-qubit quantum gates achieve > 99% fidelity. The optimization was performed under the constraint that the experimentally implemented pulses are of short duration and can be implemented with high fidelity. Therefore the gate implementations do not suffer from the drawbacks of rf offset errors or debilitating effects of decoherence during gate action. We demonstrate the advantage of our pulse sequences by comparing our results with existing experimental schemes.

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“…Chemistry with the unconventional help of evolutionary creativity supports research and understanding of the systems under study [64,65]. For instance, parameters of models in Soil Science can be readily estimated by GP [66].…”

mentioning

confidence: 99%

Evolutionary Computation and Genetic Programming

Banzhaf

2013

Engineered Biomimicry

Self Cite

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We discuss Evolutionary Computation, in particular Genetic Programming, as examples of drawing inspiration from biological systems. We set the choice of evolution as a source for inspiration in context, discuss the history of Evolutionary Computation and its variants before looking more closely at Genetic Programming. After a discussion of methods and the state-of-theart, we review application areas of Genetic Programming and its strength in providing human-competitive solutions. KeywordsBio-inspired computing, Evolutionary Computation, Genetic Algorithms, Evolution Strategies, Evolutionary Programming, Genetic Programming, Artificial Intelligence, differential evolution, machine learning, automatic programming, natural selection, breeding, population, reproduction, mutation, Bioinspiration, Biomimetics and Bioreplication 19 July 2012 2 crossover, generation, search space, human-competitive. Bio-inspired ComputingOne of the more prominent examples of taking bio-inspiration is the application of this philosophy to the development of new ways to organize computation. Life can be paraphrased by describing it as "information processing in a body". Hence biology, the science of the living, has long been concerned with these two key aspects of life: structure and dynamics. The structure of the body, from a single-celled organism like a bacterium to extended, highly complex, multi-cellular, intelligent beings like mammals 1 , is a study object of evolutionary, developmental, and molecular biology, among others. Other branches of biology are concerned with the dynamics of behaviour of organisms, in relation to an inanimate environment as well as in relation to other organisms that often provide an important and dynamically changing part of their environment. Ultimately, behaviour requires intense processing of information, both for survival and for the benefit of an organism. Behaviour of individuals is studied in a branch of biology called ethology, the behaviour of species in their interaction with the environment is studied in ecology, while1 The spatially largest organism is a fungus, Armillaria solipides, with an extension of 8.9 km 2 , whereas the largest genome of a vertebrate is that of a fish, Protopterus aetiopicus, with a size of 130 Giga bases (as compared to the 3.2 Giga bases of Homo sapiens).A base is one of four nucleotides in the alphabet of DNA. survival are all terms that can be related to and studied in models of bioinspired computing. Essential to these models is the idea that a distributed system of interacting entities can bring about effects that are not possible to produce by single entities or entities isolated from each other.

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Evolving blackbox quantum algorithms using genetic programming

Cited by 13 publications

References 88 publications

A review of procedures to evolve quantum algorithms

A review of procedures to evolve quantum algorithms

Efficient experimental design of high-fidelity three-qubit quantum gates via genetic programming

Evolutionary Computation and Genetic Programming

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