Finding optimal qubit permutations for IBM's quantum computer architectures

Almeida, Alexandre A. A. de; Dueck, Gerhard W.; Silva, Alexandre C. R. da

doi:10.1145/3338852.3339829

Cited by 12 publications

(6 citation statements)

References 7 publications

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“…'53Q gate Sycamore', '53Q gate Rochester', '20Q gate Tokyo', and three QUEKO sets '54Q bntf Sycamore', '20Q bss Tokyo', and '16Q bntf Aspen-4'. As no '20Q bntf Tokyo' benchmark set is provided in the GitHub website of QUEKO, 8 we replace it with '20Q bss Tokyo', which are benchmarks with depth from 100 to 900 for scaling study. We also run benchmarks in the '16Q bntf Aspen-4' on the 20-qubit IBM Q Tokyo.…”

Section: Summary Of Evaluation Resultsmentioning

confidence: 99%

“…With the rapid development in quantum hardware, the qubit mapping problem has attracted fast-growing interest, and many QCT algorithms have been developed. A few of these algorithms aim to find transformations with the exact optimal cost [21], [23], [28], [3], [15], [8], [17], which are, however, only applicable to circuits with less than or around 10 qubits. Most algorithms turn to heuristic search techniques [34], [11], [4], [7], [33], [22], [12], [31].…”

Section: Related Workmentioning

confidence: 99%

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On constructing benchmark quantum circuits with known near-optimal transformation cost

Li¹,

Zhou²,

Feng³

2023

Preprint

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Current quantum devices impose strict connectivity constraints on quantum circuits, making circuit transformation necessary before running logical circuits on real quantum devices. Many quantum circuit transformation (QCT) algorithms have been proposed in the past several years. This paper proposes a novel method for constructing benchmark circuits and uses these benchmark circuits to evaluate state-of-the-art QCT algorithms, including t|ket from Cambridge Quantum Computing, Qiskit from IBM, and three academic algorithms SABRE, SAHS, and MCTS. These benchmarks have known near-optimal transformation costs and thus are called QUEKNO (for quantum examples with known near-optimality). Compared with QUEKO benchmarks designed by Tan and Cong ( 2021), which all have zero optimal transformation costs, QUEKNO benchmarks are more general and can provide a more faithful evaluation for QCT algorithms (like t|ket ) which use subgraph isomorphism to find the initial mapping. Our evaluation results show that SABRE can generate transformations with conspicuously low average costs on the 53-qubit IBM Q Rochester and Google's Sycamore in both gate size and depth objectives.

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Section: Summary Of Evaluation Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

On constructing benchmark quantum circuits with known near-optimal transformation cost

Li¹,

Zhou²,

Feng³

2023

Preprint

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“…Integer Linear Programming (ILP) [14], or Satisfiability Modulo Theories (SMT) [15]. Existing tools for those problems are then used to find acceptable results.…”

Section: Related Workmentioning

confidence: 99%

Quantum Circuit Transformation Based on Tabu Search

Jiang,

Deng,

2021

Preprint

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The goal of quantum circuit transformation is to construct mappings from logical quantum circuits to physical ones in an acceptable amount of time, and in the meantime to introduce as few auxiliary gates as possible. We present an effective approach to constructing the mappings. It consists of two key steps: one makes use of a combined subgraph isomorphism and complement (CSIC) to initialize a mapping, the other dynamically adjusts the mapping by using Tabu search-based adjustment (TSA). Our experiments show that, compared with the very recent method wghtgraph considered in the literature, CSIC can save 22.43% of auxiliary gates and reduce the depth of output circuits by 8.46% on average in the initialization of the mapping, and TSA has a better scalability than many stateof-the-art algorithms for adjusting mappings.

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“…These algorithms can be roughly classiied into two broad categories (see also [Kusyk et al 2021] for a similar classiication). The irst category consists of algorithms that try to reformulate QCT as a planning or optimisation problem and solve it by applying of-the-shelf tools [Booth et al 2018;de Almeida et al 2019;Murali et al 2019;Rasconi and Oddi 2019;Saeedi et al 2011;Siraichi et al 2018;Venturelli et al , 2017Wille et al 2019;Zhang et al 2021;Zhu et al 2020]. However, as shown in [Childs et al 2019;Siraichi et al 2018], QCT is NP-complete in general.…”

Section: Introductionmentioning

confidence: 99%

Quantum Circuit Transformation: A Monte Carlo Tree Search Framework

Zhou

Feng

2022

ACM Trans. Des. Autom. Electron. Syst.

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In Noisy Intermediate-Scale Quantum (NISQ) era, quantum processing units (QPUs) suffer from, among others, highly limited connectivity between physical qubits. To make a quantum circuit effectively executable, a circuit transformation process is necessary to transform it, with overhead cost the smaller the better, into a functionally equivalent one so that the connectivity constraints imposed by the QPU are satisfied. While several algorithms have been proposed for this goal, the overhead costs are often very high, which degenerates the fidelity of the obtained circuits sharply. One major reason for this lies in that, due to the high branching factor and vast search space, almost all these algorithms only search very shallowly and thus, very often, only (at most) locally optimal solutions can be reached. In this paper, we propose a Monte Carlo Tree Search (MCTS) framework to tackle the circuit transformation problem, which enables the search process to go much deeper. The general framework supports implementations aiming to reduce either the size or depth of the output circuit through introducing SWAP or remote CNOT gates. The algorithms, called MCTS-Size and MCTS-Depth, are polynomial in all relevant parameters. Empirical results on extensive realistic circuits and IBM Q Tokyo show that the MCTS-based algorithms can reduce the size (depth, resp.) overhead by, on average, 66% (84%, resp.) when compared with t|ket PLX − right 〉., an industrial level compiler.

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Finding optimal qubit permutations for IBM's quantum computer architectures

Cited by 12 publications

References 7 publications

On constructing benchmark quantum circuits with known near-optimal transformation cost

On constructing benchmark quantum circuits with known near-optimal transformation cost

Quantum Circuit Transformation Based on Tabu Search

Quantum Circuit Transformation: A Monte Carlo Tree Search Framework

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