Quantum circuit compilation by genetic algorithm for quantum approximate optimization algorithm applied to MaxCut problem

Arufe, Lis; González, Miguel A.; Oddi, Angelo; Rasconi, Riccardo; Varela, Ramiro

doi:10.1016/j.swevo.2022.101030

Cited by 14 publications

(2 citation statements)

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“…A different approach is to dynamically construct a circuit through machine learning, heuristics, or metaheuristics. Several techniques have been proposed, including pseudorandom walks [39], genetic algorithms [40], temporal planning [41], and deep learning [42]. Such methods can sometimes also be used to synthesise approximations to a desired unitary, rather than a perfect re-expression, resulting in shorter circuits than exact compilation.…”

Section: Introductionmentioning

confidence: 99%

Exploring ab initio machine synthesis of quantum circuits

2023

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Gate-level quantum circuits are often derived manually from higher level algorithms. While this suffices for small implementations and demonstrations, ultimately automatic circuit design will be required to realise complex algorithms using hardware-specific operations and connectivity. Therefore, ab initio creation of circuits within a machine, either a classical computer or a hybrid quantum-classical device, is of key importance. We explore a range of established and novel techniques for the synthesis of new circuit structures, the optimisation of parameterised circuits, and the efficient removal of low-value gates via the quantum geometric tensor. Using these techniques we tackle the tasks of automatic encoding of unitary processes and translation (recompilation) of a circuit from one form to another. Using emulated quantum computers with various noise-free gate sets we provide simple examples involving up to 10 qubits, corresponding to 20 qubits in the augmented space we use. Further applications of specific relevance to chemistry modelling are considered in a sister paper, 'Exploiting subspace constraints and ab initio variational methods for quantum chemistry'.

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Section: Introductionmentioning

confidence: 99%

Exploring ab initio machine synthesis of quantum circuits

2023

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“…The method was experimented on the 5-qubit IBM QX architecture, which had faster mapping time at the same mapping accuracy. In 2022, Arufe et al [26] modeled the 2DNN architecture mapping problem as a scheduling problem and proposed a quantum circuit mapping method based on genetic algorithm. The method was mainly applied to the 2DNN architecture mapping of the QAOA for the MaxCut problem.…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning for Mapping Quantum Circuits to 2D Nearest‐Neighbor Architectures

Li,

Liu,

2024

Adv Quantum Tech

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Recently, quantum computing is considered as a promising future computing paradigm. However, when implementing quantum circuits on a quantum device, it is necessary to ensure that quantum circuits satisfy nearest‐neighbor architecture constraints. When performing nearest‐neighbor architecture mapping for quantum circuits, it is inevitable to introduce SWAP gates, which will increase the overhead and reduce the fidelity. Therefore, it is crucial to complete the mapping with the minimum SWAP. In this paper, a 2D nearest‐neighbor architecture mapping method for quantum circuits is proposed based on deep reinforcement learning. In the initial mapping, an isomorphic graph initial mapping search algorithm is designed to quickly find the isomorphic graph between quantum circuit and architecture constraint graph. For quantum circuits without isomorphic graph mapping, the reordering algorithm of qubit impact factors is designed to obtain the initial placement position of qubits. In the SWAP gate addition, a qubit local reordering algorithm based on dueling deep‐Q‐network is designed to reduce SWAP gates. Experiments on the benchmark set B131 and IBM Q20 Tokyo verify that the proposed method can add fewer SWAP gates. Compared with the state‐of‐the‐art method, the average running time is accelerated by 63.1%, and the average SWAP gates added are reduced by 24.7%.

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