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Qubit Allocation for Noisy Intermediate-Scale Quantum Computers

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arxiv 1810.08291 v1 pith:BQE2MICF submitted 2018-10-18 quant-ph

classification quant-ph
keywords quantumalgorithmqubitsallocationdevicesprogramscomputersconnectivity
verification ladder T0 review T1 audit T2 compute T3 formal

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In the era of noisy-intermediate-scale quantum computers, we expect to see quantum devices with increasing numbers of qubits emerge in the foreseeable future. To practically run quantum programs, logical qubits have to be mapped to the physical qubits by a qubit allocation algorithm. However, on present day devices, qubits differ by their error rate and connectivity. Here, we establish and demonstrate on current experimental devices a new allocation algorithm that combines the simulated annealing method with local search of the solution space using Dijkstra's algorithm. Our algorithm takes into account the weighted connectivity constraints of both the quantum hardware and the quantum program being compiled. New quantum programs will enable unprecedented developments in physics, chemistry, and materials science and our work offers an important new pathway toward optimizing compilers for quantum programs.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Quantum Circuit Transformation Based on Simulated Annealing and Heuristic Search

    quant-ph 2019-08 conditional novelty 6.0 of 10

    A simulated-annealing plus two-level look-ahead heuristic reduces the added-gate overhead of mapping quantum circuits onto IBM QX5 and Q20 hardware compared with prior algorithms.

  2. Timing and resource-aware mapping of quantum circuits to superconducting processors

    quant-ph 2019-08 conditional novelty 6.0 of 10

    A timing- and resource-aware quantum-circuit mapper reduces circuit latency overhead by up to 47.3% and operation overhead by up to 28.6% compared with a baseline mapper on the Surface-17 processor.

  3. Improving and benchmarking NISQ qubit routers

    quant-ph 2025-02 conditional novelty 4.0 of 10

    A SABRE heuristic that keeps only the basic and decay terms outperforms lookahead-based routers in fidelity for larger NISQ devices under a thermal relaxation noise model.

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