Dimensional Expressivity Analysis, best-approximation errors, and automated design of parametric quantum circuits

TL;DR

This paper introduces a dimensional expressivity analysis method to optimize parametric quantum circuits by identifying redundant parameters, enabling efficient circuit design and potentially improving variational quantum simulations.

Contribution

It presents a hybrid quantum-classical algorithm for analyzing and removing redundant parameters in PQCs, enhancing their expressivity and efficiency.

Findings

Efficient identification of independent and redundant parameters in PQCs.

Ability to compute the dimension of the generated state space.

Potential for on-the-fly circuit optimization in variational quantum algorithms.

Abstract

The design of parametric quantum circuits (PQCs) for efficient use in variational quantum simulations (VQS) is subject to two competing factors. On one hand, the set of states that can be generated by the PQC has to be large enough to contain the solution state. Otherwise, one may at best find the best approximation of the solution restricted to the states generated by the chosen PQC. On the other hand, the PQC should contain as few parametric quantum gates as possible to minimize noise from the quantum device. Thus, when designing a PQC one needs to ensure that there are no redundant parameters. The dimensional expressivity analysis discussed in these proceedings is a means of addressing these counteracting effects. Its main objective is to identify independent and redundant parameters in the PQC. Using this information, superfluous parameters can be removed and the dimension of the space of states that are generated by the PQC can be computed. Knowing the dimension of the physical state space then allows us to deduce whether or not the PQC can reach all physical states. Furthermore, the dimensional expressivity analysis can be implemented efficiently using a hybrid quantum-classical algorithm. This implementation has relatively small overhead costs both for the classical and quantum part of the algorithm and could therefore be used in the future for on-the-fly circuit construction. This would allow for optimized circuits to be used in every loop of a VQS rather than the same PQC for the entire VQS. These proceedings review and extend work in [1, 2].