Corpus ID: 198179760

Analysis of Quantum Approximate Optimization Algorithm under Realistic Noise in Superconducting Qubits

@article{Alam2019AnalysisOQ,
  title={Analysis of Quantum Approximate Optimization Algorithm under Realistic Noise in Superconducting Qubits},
  author={Mahabubul Alam and Abdullah Ash- Saki and Swaroop Ghosh},
  journal={ArXiv},
  year={2019},
  volume={abs/1907.09631}
}
  • Mahabubul Alam, Abdullah Ash- Saki, Swaroop Ghosh
  • Published 2019
  • Mathematics, Physics, Computer Science
  • ArXiv
  • The quantum approximate optimization algorithm (QAOA) is a promising quantum-classical hybrid technique to solve combinatorial optimization problems in near-term gate-based noisy quantum devices. In QAOA, the objective is a function of the quantum state, which itself is a function of the gate parameters of a multi-level parameterized quantum circuit (PQC). A classical optimizer varies the continuous gate parameters to generate distributions (quantum state) with significant support to the… CONTINUE READING

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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 17 REFERENCES

    QAOA for Max-Cut requires hundreds of qubits for quantum speed-up

    VIEW 2 EXCERPTS

    A Quantum Approximate Optimization Algorithm

    VIEW 8 EXCERPTS
    HIGHLY INFLUENTIAL

    QURE: Qubit Re-allocation in Noisy Intermediate-Scale Quantum Computers

    VIEW 1 EXCERPT