# An initialization strategy for addressing barren plateaus in parametrized quantum circuits

## TL;DR

This paper proposes an initialization method for parametrized quantum circuits that prevents barren plateaus, enabling effective training of quantum neural networks and variational quantum eigensolvers.

## Contribution

The paper introduces a novel initialization strategy that ensures shallow circuit blocks evaluate to identity, addressing barren plateaus in quantum circuit training.

## Key findings

- The strategy empirically improves trainability of quantum neural networks.
- It enables the use of more compact ansätze in practical applications.
- The method is validated on variational quantum eigensolvers and quantum neural networks.

## Abstract

Parametrized quantum circuits initialized with random initial parameter values are characterized by barren plateaus where the gradient becomes exponentially small in the number of qubits. In this technical note we theoretically motivate and empirically validate an initialization strategy which can resolve the barren plateau problem for practical applications. The technique involves randomly selecting some of the initial parameter values, then choosing the remaining values so that the circuit is a sequence of shallow blocks that each evaluates to the identity. This initialization limits the effective depth of the circuits used to calculate the first parameter update so that they cannot be stuck in a barren plateau at the start of training. In turn, this makes some of the most compact ans\"atze usable in practice, which was not possible before even for rather basic problems. We show empirically that variational quantum eigensolvers and quantum neural networks initialized using this strategy can be trained using a gradient based method.