Avoiding Barren Plateaus with Entanglement

TL;DR

This paper introduces a method to mitigate barren plateaus in quantum circuit optimization by using auxiliary control qubits to shift the circuit's design, enhancing gradient stability and training efficiency.

Contribution

It proposes a novel approach to overcome barren plateaus without altering the original quantum circuit architecture by temporarily adding and removing auxiliary qubits.

Findings

Broader gradient distribution with increasing qubits and layers

Effective mitigation of barren plateaus demonstrated experimentally

Enhanced stability for training larger quantum systems

Abstract

In the search for quantum advantage with near-term quantum devices, navigating the optimization landscape is significantly hampered by the barren plateaus phenomenon. This study presents a strategy to overcome this obstacle without changing the quantum circuit architecture. We propose incorporating auxiliary control qubits to shift the circuit from a unitary $2$-design to a unitary $1$-design, mitigating the prevalence of barren plateaus. We then remove these auxiliary qubits to return to the original circuit structure while preserving the unitary $1$-design properties. Our experiment suggests that the proposed structure effectively mitigates the barren plateaus phenomenon. A significant experimental finding is that the gradient of $\theta_{1,1}$, the first parameter in the quantum circuit, displays a broader distribution as the number of qubits and layers increases. This suggests a higher probability of obtaining effective gradients. This stability is critical for the efficient training of quantum circuits, especially for larger and more complex systems. The results of this study represent a significant advance in the optimization of quantum circuits and offer a promising avenue for the scalable and practical implementation of quantum computing technologies. This approach opens up new opportunities in quantum learning and other applications that require robust quantum computing power.