TY - GEN
T1 - Design-Space Exploration of Quantum Approximate Optimization Algorithm under Noise
AU - Alam, Mahabubul
AU - Ash-Saki, Abdullah
AU - Ghosh, Swaroop
N1 - Funding Information:
IV. CONCLUSION We analyzed the performance of QAOA-MaxCut under noise for NISQ qubits. We noted that, (i) QAOA performance is highly sensitive to noise; (ii) monotonic performance improvement with higher p-values is impractical; (iii) the p-value of QAOA should be chosen based on the noise values to maximize performance. Finally, we also provided guidelines for desired qubit quality to meet certain algorithmic performance. Acknowledgement: This work is supported by SRC (2847.001), and NSF (CNS-1722557, CCF-1718474, CNS-1814710, DGE-1723687 and DGE-1821766).
Publisher Copyright:
© 2020 IEEE.
PY - 2020/3
Y1 - 2020/3
N2 - Quantum approximate optimization algorithm (QAOA) is a promising quantum-classical hybrid technique to solve NP-hard problems in near-term gate-based noisy quantum devices. In QAOA, the gate parameters of a parameterized quantum circuit (PQC) are varied by a classical optimizer to generate a quantum state with a significant support to the optimal solution. The existing analysis fails to consider nonidealities in the qubit quality i.e., short lifetime and imperfect gate operations in a realistic quantum hardware. In this article, we study the impact of various noise sources on the performance of QAOA both in simulation and on a real quantum computer from IBM. Our analysis indicates that QAOA performance is noise-sensitive (especially higher-depth QAOA instances). Therefore, the optimal number of stages (p-value) for any QAOA instance is limited by the noise in the target hardware as opposed to the current perception that QAOA will provide monotonically better performance with higher-depth. We show that the two-qubit gate error has to be decreased by more than 75% of the current state-of-the-art levels to attain a performance within 10% of the maximum value for the lowest-depth QAOA.
AB - Quantum approximate optimization algorithm (QAOA) is a promising quantum-classical hybrid technique to solve NP-hard problems in near-term gate-based noisy quantum devices. In QAOA, the gate parameters of a parameterized quantum circuit (PQC) are varied by a classical optimizer to generate a quantum state with a significant support to the optimal solution. The existing analysis fails to consider nonidealities in the qubit quality i.e., short lifetime and imperfect gate operations in a realistic quantum hardware. In this article, we study the impact of various noise sources on the performance of QAOA both in simulation and on a real quantum computer from IBM. Our analysis indicates that QAOA performance is noise-sensitive (especially higher-depth QAOA instances). Therefore, the optimal number of stages (p-value) for any QAOA instance is limited by the noise in the target hardware as opposed to the current perception that QAOA will provide monotonically better performance with higher-depth. We show that the two-qubit gate error has to be decreased by more than 75% of the current state-of-the-art levels to attain a performance within 10% of the maximum value for the lowest-depth QAOA.
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U2 - 10.1109/CICC48029.2020.9075903
DO - 10.1109/CICC48029.2020.9075903
M3 - Conference contribution
AN - SCOPUS:85084478020
T3 - Proceedings of the Custom Integrated Circuits Conference
BT - 2020 IEEE Custom Integrated Circuits Conference, CICC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE Custom Integrated Circuits Conference, CICC 2020
Y2 - 22 March 2020 through 25 March 2020
ER -