Entanglement Devised Barren Plateau Mitigation
Taylor L. Patti, Khadijeh Najafi, Xun Gao, Susanne F. Yelin

TL;DR
This paper identifies random entanglement as the cause of barren plateaus in quantum machine learning and introduces techniques to mitigate their effects, improving training accuracy in variational quantum algorithms.
Contribution
It provides a theoretical framework linking entanglement dynamics to barren plateaus and proposes practical methods to reduce entanglement for better quantum training performance.
Findings
Entanglement correlates with barren plateau formation.
Mitigation techniques improve training accuracy.
Entanglement control is key to effective quantum learning.
Abstract
Hybrid quantum-classical variational algorithms are one of the most propitious implementations of quantum computing on near-term devices, offering classical machine learning support to quantum scale solution spaces. However, numerous studies have demonstrated that the rate at which this space grows in qubit number could preclude learning in deep quantum circuits, a phenomenon known as barren plateaus. In this work, we implicate random entanglement as the source of barren plateaus and characterize them in terms of many-body entanglement dynamics, detailing their formation as a function of system size, circuit depth, and circuit connectivity. Using this comprehension of entanglement, we propose and demonstrate a number of barren plateau ameliorating techniques, including: initial partitioning of cost function and non-cost function registers, meta-learning of low-entanglement circuit…
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