Quantum multi-programming for maximum likelihood amplitude estimation

Pooja Rao; Sua Choi; Kwangmin Yu

doi:10.1117/12.3002854

13 March 2024 Quantum multi-programming for maximum likelihood amplitude estimation

Pooja Rao, Sua Choi, Kwangmin Yu

Proceedings Volume 12911, Quantum Computing, Communication, and Simulation IV; 129110E (2024) https://doi.org/10.1117/12.3002854
Event: SPIE Quantum West, 2024, San Francisco, California, United States

Abstract

A variant of QAE algorithm by Suzuki et al. called maximum likelihood amplitude estimation (MLAE) achieves the amplitude estimation by varying depths of Grover operators and post-processing for maximum likelihood estimation without the additional controlled operations and QFT. However, MLAE requires running multiple circuits of different depths of Grover operators. On the other hand, quantum multi-programming (QMP) is a computing method that executes multiple quantum circuits concurrently on a quantum computer. The quantum circuits executed concurrently can be different and even have different circuit depths. The main motivation of the QMP is that the number of qubits of NISQ computers is much greater than their quantum volume. In this work, using QMP in conjunction with MLAE makes it possible to run MLAE using a single circuit, thus requiring sampling much fewer times. We validate this algorithm for a numerical integration problem using NVIDIA’s open-source platform CUDA Quantum (simulator), Qiskit (simulator) and Quantinuum H2 device.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Pooja Rao, Sua Choi, and Kwangmin Yu "Quantum multi-programming for maximum likelihood amplitude estimation", Proc. SPIE 12911, Quantum Computing, Communication, and Simulation IV, 129110E (13 March 2024); https://doi.org/10.1117/12.3002854

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