Christopher K. Long, Nikola Yanakiev, Crispin H. W. Barnes, Normann Mertig, and David R. M. Arvidsson-Shukur
Date & time: 6 March 2024 08:36–08:48 CST
Location: Minneapolis, Minnesota, United States of America
Conference: 2024 APS March Meeting [1]
I presented work from Ref. [2]. This was part II/II following Good and bad news for noisy variational quantum algorithms—Part I.
Download slide deck: PPTX1, PDF1
Abstract
From Ref. [3]:
The quantum approximate optimization algorithm (QAOA) is an appealing proposal to solve NP problems on noisy intermediate-scale quantum (NISQ) hardware. Making NISQ implementations of the QAOA resilient to noise requires short ansatz circuits with as few CNOT gates as possible. In this talk, we present Dynamic-ADAPT-QAOA. Our algorithm significantly reduces the circuit depth and the CNOT count of standard ADAPT-QAOA, a leading proposal for near-term implementations of the QAOA. Throughout our algorithm, the decision to apply CNOT-intensive operations is made dynamically, based on algorithmic benefits. Using density-matrix simulations, we benchmark the noise resilience of ADAPT-QAOA and Dynamic-ADAPT-QAOA. We compute the gate-error probability p below which these algorithms provide, on average, more accurate solutions than the classical, polynomial-time approximation algorithm by Goemans and Williamson. For small systems with 6–10 qubits, we show that p > 0.001 for Dynamic-ADAPT-QAOA. Compared to standard ADAPT-QAOA, this constitutes an order-of-magnitude improvement in noise resilience. This improvement should make Dynamic-ADAPT-QAOA viable for implementations on superconducting NISQ hardware, even in the absence of error mitigation.
References
Footnotes
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