A review of procedures to evolve quantum algorithms

Gepp, Adrian; Stocks, Phil

doi:10.1007/s10710-009-9080-7

Cited by 29 publications

(16 citation statements)

References 95 publications

(220 reference statements)

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“…In future work, we plan to automate the process of pattern recognition for algorithm generalization. As noted in [9], this field will be even more promising when quantum computers become available. This is due to the exponential speedup they provide in evaluating algorithm cost, i.e.…”

Section: Discussionmentioning

confidence: 99%

“…It may not be obvious how to optimize algorithms for a given connectivity and a given gate set. This motivates the idea of an automated approach for discovering and optimizing quantum algorithms [6][7][8][9][10][11][12][13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

“…Other automated algorithm-discovery approaches have been employed in the literature. Gepp and Stocks [9] review much of the early work to evolve quantum algorithms using genetic programming such as [10] (for more recent work see for example [19]). In these approaches the gate set is typically discrete.…”

Section: Introductionmentioning

confidence: 99%

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Learning the quantum algorithm for state overlap

et al. 2018

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Short-depth algorithms are crucial for reducing computational error on near-term quantum computers, for which decoherence and gate infidelity remain important issues. Here we present a machine-learning approach for discovering such algorithms. We apply our method to a ubiquitous primitive: computing the overlap rs ( ) Tr between two quantum states ρ and σ. The standard algorithm for this task, known as the Swap Test, is used in many applications such as quantum support vector machines, and, when specialized to ρ=σ, quantifies the Renyi entanglement. Here, we find algorithms that have shorter depths than the Swap Test, including one that has a constant depth (independent of problem size). Furthermore, we apply our approach to the hardware-specific connectivity and gate sets used by Rigetti's and IBM's quantum computers and demonstrate that the shorter algorithms that we derive significantly reduce the error-compared to the Swap Test-on these computers.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Learning the quantum algorithm for state overlap

et al. 2018

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“…To efficiently search through this exponentially large space, we adopt an approach based on simulated annealing. (An alternative approach is genetic optimization, which has been implemented previously to classically optimize quantum gate sequences [40]. )…”

Section: Small Problem Sizesmentioning

confidence: 99%

Quantum-assisted quantum compiling

Khatri

LaRose

Poremba

et al. 2019

Quantum

347

437

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Compiling quantum algorithms for near-term quantum computers (accounting for connectivity and native gate alphabets) is a major challenge that has received significant attention both by industry and academia. Avoiding the exponential overhead of classical simulation of quantum dynamics will allow compilation of larger algorithms, and a strategy for this is to evaluate an algorithm's cost on a quantum computer. To this end, we propose a variational hybrid quantum-classical algorithm called quantum-assisted quantum compiling (QAQC). In QAQC, we use the overlap between a target unitary U and a trainable unitary V as the cost function to be evaluated on the quantum computer. More precisely, to ensure that QAQC scales well with problem size, our cost involves not only the global overlap Tr(V † U ) but also the local overlaps with respect to individual qubits. We introduce novel short-depth quantum circuits to quantify the terms in our cost function, and we prove that our cost cannot be efficiently approximated with a classical algorithm under reasonable complexity assumptions. We present both gradient-free and gradient-based approaches to minimizing this cost. As a demonstration of QAQC, we compile various one-qubit gates on IBM's and Rigetti's quantum computers into their respective native gate alphabets. Furthermore, we successfully simulate QAQC up to a problem size of 9 qubits, and these simulations highlight both the scalability of our cost function as well as the noise resilience of QAQC. Future applications of QAQC include algorithm depth compression, black-box compiling, noise mitigation, and benchmarking. arXiv:1807.00800v5 [quant-ph]

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“…It was successfully used to solve a wide variety of problems, and it can be now viewed as a mature method as even patents for old and new discovery have been filled [1], [2]. GP is used in fields as different as bio-informatics [3], quantum computing [4] or robotics [5].…”

Section: Introductionmentioning

confidence: 99%

Linear imperative programming with Differential Evolution

Fonlupt

Robilliard

Marion-Poty

2011

2011 IEEE Symposium on Differential Evolution (SDE)

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Differential Evolution (DE) is an evolutionary approach for optimizing non-linear continuous space functions. This method is known to be robust and easy to use. DE manipulates vectors of floats that are improved over generations by mating with best and random individuals. Recently, DE was successfully applied to the automatic generation of programs by mapping real-valued vectors to full programs treesTree Based Differential Evolution (TreeDE). In this paper, we propose to use DE as a method to directly generate linear sequences of imperative instructions, which we call Linear Differential Evolutionary Programming (LDEP). Unlike TreeDE, LDEP incorporates constant management for regression problems and lessens the constraints on the architecture of solutions since the user is no more required to determine the tree depth of solutions. Comparisons with standard Genetic Programming and with the CMA-ES algorithm showed that DE-based approach are well suited to automatic programming, being notably more robust than CMA-ES in this particular context.

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A review of procedures to evolve quantum algorithms

Cited by 29 publications

References 95 publications

Learning the quantum algorithm for state overlap

Learning the quantum algorithm for state overlap

Quantum-assisted quantum compiling

Linear imperative programming with Differential Evolution

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