Quantum Circuit Transformation Based on Tabu Search

Jiang, Hui; Deng, Yuxin; Xu, Ming

doi:10.48550/arxiv.2104.05214

Cited by 2 publications

(3 citation statements)

References 19 publications

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“…In some cases, a known circuit for the desired quantum algorithm may be incompatible with the connectivity constraints of the hardware. This task is often called qubit routing or circuit transformation, and several methods have been applied to efficiently find the necessary SWAP operations between gates, including simulated annealing [30], tabu search [31], artificial neural networks [32,33], and specialised heuristics [34][35][36][37]. Furthermore, sometimes SWAPs can be avoided altogether [38].…”

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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“…However, solving the quantum circuit mapping problem for a large number of qubits can be computationally unfeasible, even for contemporary single-core devices. To tackle it, diverse mapping algorithms have been proposed, including heuristic or brute-force strategies, graph-theoretical techniques, dynamic programming algorithms, and machine learning-based solutions [2,7,10,[17][18][19][20][21][22][23][24].…”

Section: Background and Related Workmentioning

confidence: 99%

“…In order to improve their initial approach in terms of circuit and graph partitioning with an overly-restricting local lookahead function, we rely on previous subgraph isomorphism- [21,24,[30][31][32] and QUBO optimization-based single-core solutions [33,34] where now instead of mapping logical qubits to optimal subsets of coupling graph representing the physical qubits and their connections (Fig. 1) we map qubits to different cores.…”

Section: Background and Related Workmentioning

confidence: 99%

Mapping Quantum Circuits to Modular Architectures with QUBO

Bandic,

Prielinger,

Nüßlein

et al. 2023

2023 IEEE International Conference on Quantum Computing and Engineering (QCE)

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Modular quantum computing architectures are a promising alternative to monolithic QPU (Quantum Processing Unit) designs for scaling up quantum devices. They refer to a set of interconnected QPUs or cores consisting of tightly coupled quantum bits that can communicate via quantum-coherent and classical links. In multi-core architectures, it is crucial to minimize the amount of communication between cores when executing an algorithm. Therefore, mapping a quantum circuit onto a modular architecture involves finding an optimal assignment of logical qubits (qubits in the quantum circuit) to different cores with the aim to minimize the number of expensive inter-core operations while adhering to given hardware constraints. In this paper, we propose for the first time a Quadratic Unconstrained Binary Optimization (QUBO) technique to encode the problem and the solution for both qubit allocation and inter-core communication costs in binary decision variables. To this end, the quantum circuit is split into slices, and qubit assignment is formulated as a graph partitioning problem for each circuit slice. The costly inter-core communication is reduced by penalizing inter-core qubit communications. The final solution is obtained by minimizing the overall cost across all circuit slices. To evaluate the effectiveness of our approach, we conduct a detailed analysis using a representative set of benchmarks having a high number of qubits on two different multi-core architectures. Our method showed promising results and performed exceptionally well with very dense and highly-parallelized circuits that require on average 0.78 inter-core communications per two-qubit gate.

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Quantum Circuit Transformation Based on Tabu Search

Cited by 2 publications

References 19 publications

Exploring ab initio machine synthesis of quantum circuits

Exploring ab initio machine synthesis of quantum circuits

Mapping Quantum Circuits to Modular Architectures with QUBO

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