Optimality Study of Existing Quantum Computing Layout Synthesis Tools

Tan, Bochen; Cong, Jason

doi:10.1109/tc.2020.3009140

Cited by 62 publications

(67 citation statements)

References 31 publications

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“…The evaluations were run on an Ubuntu 16.04 server with two Intel Xeon E5-2699v3 CPUs and 128GB memory. We selected a comprehensive set of benchmarks from various sources including [2,11,16,23]. We used the fidelity profile of IBM QX2, Figure 2a, and IBM Melbourne 2d, from [15].…”

Section: Discussionmentioning

confidence: 99%

“…The total search space is then exponential to and . This is expected from the NP-completeness of the problem [21,23]. However, as shown in Table 1, this formulation still has exponentially fewer variables compared to Wille et al [25] because they have a variable for each permutation of qubits at each time.…”

Section: Complexity Analysismentioning

confidence: 98%

“…Benchmarks: To evaluate these approaches to LSQC, benchmarks such as reversible functions in RevLib [26], gate optimization results of useful logic functions in Amy et al [2] and Nam et al [16], or certain circuits with known optimal, QUEKO [23], can be used. The QUEKO evaluations show t|ket⟩ [22] to be a leading industry LSQC tool, but still with large optimality gaps.…”

Section: Related Workmentioning

confidence: 99%

“…(Note that our notion of logical qubit is different from that in the quantum error correction.) The task of deriving such information is called layout synthesis for quantum computing (LSQC) [23]. Other names have also been used to refer to this task, e.g., quantum circuit compilation [1,8], transpilation (in Qiskit 1 ), placement [14,19], mapping [6,13,15,25,28], qubit allocation [21], and routing [22].…”

Section: Introductionmentioning

confidence: 99%

“…A recent study [23] revealed large optimality gaps of several leading LSQC tools with a set of benchmarks named QUEKO that have known optimal solutions. Therefore, despite the NP-completeness of the problem [14,21,23], optimal approaches to LSQC are still worthy of research because they provide the baseline of measuring the available LSQC solutions. There have been a few previous works on exact or optimal LSQC [6,19,21,25,27].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Optimal layout synthesis for quantum computing

Tan

Cong

2020

Proceedings of the 39th International Conference on Computer-Aided Design

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Section: Discussionmentioning

confidence: 99%

Section: Complexity Analysismentioning

confidence: 98%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal layout synthesis for quantum computing

Tan

Cong

2020

Proceedings of the 39th International Conference on Computer-Aided Design

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Layout Synthesis for Near-Term Quantum Computing: Gap Analysis and Optimal Solution

Tan

Cong

2022

Design Automation of Quantum Computers

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Improving Quantum Computation by Optimized Qubit Routing

Wagner

Bärmann

Liers

et al. 2023

J Optim Theory Appl

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In this work we propose a high-quality decomposition approach for qubit routing by swap insertion. This optimization problem arises in the context of compiling quantum algorithms formulated in the circuit model of computation onto specific quantum hardware. Our approach decomposes the routing problem into an allocation subproblem and a set of token swapping problems. This allows us to tackle the allocation part and the token swapping part separately. Extracting the allocation part from the qubit routing model of Nannicini et al. (Optimal qubit assignment and routing via integer programming, 2021, http://arxiv.org/abs/2106.06446), we formulate the allocation subproblem as a binary linear program. Herein, we employ a cost function that is a lower bound on the overall routing problem objective. We strengthen the linear relaxation by novel valid inequalities. For the token swapping part we develop an exact branch-and-bound algorithm. In this context, we improve upon known lower bounds on the token swapping problem. Furthermore, we enhance an existing approximation algorithm which runs much faster than the exact approach and typically is able to determine solutions close to the optimum. We present numerical results for the fully integrated allocation and token swapping problem. Obtained solutions may not be globally optimal due to the decomposition and the usage of an approximation algorithm. However, the solutions are obtained fast and are typically close to optimal. In addition, there is a significant reduction in the number of artificial gates and output circuit depth when compared to various state-of-the-art heuristics. Reducing these figures is crucial for minimizing noise when running quantum algorithms on near-term hardware. As a consequence, using the novel decomposition approach leads to compiled algorithms with improved quality. Indeed, when compiled with the novel routing procedure and executed on real hardware, our experimental results for quantum approximate optimization algorithms show an significant increase in solution quality in comparison to standard routing methods.

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Optimality Study of Existing Quantum Computing Layout Synthesis Tools

Cited by 62 publications

References 31 publications

Optimal layout synthesis for quantum computing

Optimal layout synthesis for quantum computing

Layout Synthesis for Near-Term Quantum Computing: Gap Analysis and Optimal Solution

Improving Quantum Computation by Optimized Qubit Routing

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