Dynamic parameterized quantum circuits: expressive and barren-plateau free

TL;DR

This paper introduces dynamic parameterized quantum circuits with measurements and feedforward, which are free from barren plateaus, highly expressive, and effective for state preparation and quantum machine learning tasks.

Contribution

The paper proposes a new class of dynamic quantum circuits with measurements and feedforward that avoid barren plateaus and match the expressiveness of deep unitaries.

Findings

They do not suffer from barren plateaus.

They can represent arbitrarily deep quantum circuits.

They are effective for ground state and thermal state preparation.

Abstract

Classical optimization of parameterized quantum circuits is a widely studied methodology for the preparation of complex quantum states, as well as the solution of machine learning and optimization problems. However, it is well known that many proposed parameterized quantum circuit architectures suffer from drawbacks which limit their utility, such as their classical simulability or the hardness of optimization due to a problem known as "barren plateaus". We propose and study a class of dynamic parameterized quantum circuit architectures. These are parameterized circuits containing intermediate measurements and feedforward operations. In particular, we show that these architectures: 1. Provably do not suffer from barren plateaus. 2. Are expressive enough to describe arbitrarily deep unitary quantum circuits. 3. Are competitive with state of the art methods for preparing ground states and facilitating the representation of nontrivial thermal states. These features make the proposed architectures promising candidates for a variety of applications.