2020/08/31 by Bryan Dury, Dury, Bryan, Olivia Di Matteo +1 · 9 citations
Computer Science · Engineering · Physics and Astronomy · #Benchmark (surveying) #Computer science #FOS: Physical sciences #Low-power high-performance VLSI design #Parallel Computing and Optimization Techniques #Physics #Quadratic unconstrained binary optimization #Quantum #Quantum Computing Algorithms and Architecture #Quantum Physics (quant-ph) #Quantum annealing #Quantum computer #Quantum gate #Quantum mechanics #Qubit #Theoretical computer science #quant-ph
paper · pdf · doi:10.48550/arxiv.2009.00140
published in arXiv (Cornell University) (Cornell University) · 17 pages, 15 figures; updated some figures for clarity
openalex publication_date 2020/08/31 · arxiv created 2020/11/28 · arxiv updated 2020/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
To run an algorithm on a quantum computer, one must choose an assignment from logical qubits in a circuit to physical qubits on quantum hardware. This task of initial qubit placement, or qubit allocation, is especially important on present-day quantum computers which have a limited number of qubits, connectivity constraints, and varying gate fidelities. In this work we formulate and implement the qubit placement problem as a quadratic, unconstrained binary optimization (QUBO) problem and solve it using simulated annealing to obtain a spectrum of initial placements. Compared to contemporary allocation methods available in t|ket⟩ and Qiskit, the QUBO method yields allocations with improved circuit depth for >50% of a large set of benchmark circuits, with many also requiring fewer CX gates.