All you need is spin: SU(2) equivariant variational quantum circuits based on spin networks

TL;DR

This paper introduces SU(2) equivariant variational quantum circuits based on spin networks, leveraging group symmetry to improve quantum algorithm efficiency and performance in solving symmetric many-body problems.

Contribution

It proposes a novel construction of SU(2) equivariant quantum circuits using spin networks, providing a more hardware-friendly approach and demonstrating improved performance on Heisenberg models.

Findings

Circuits effectively solve SU(2) symmetric Heisenberg models.

Equivariant circuits outperform non-symmetric counterparts.

Construction is mathematically equivalent to existing methods but more practical.

Abstract

Variational algorithms require architectures that naturally constrain the optimization space to run efficiently. Geometric quantum machine learning achieves this goal by encoding group structure into parameterized quantum circuits to include the symmetries of a problem as an inductive bias. However, constructing such circuits is challenging as a concrete guiding principle has yet to emerge. In this paper, we propose the use of spin networks, a form of directed tensor network invariant under a group transformation, to devise SU(2) equivariant quantum circuit ans\"atze $\unicode{x2013}$ circuits possessing spin-rotation symmetry. By changing to the basis that block diagonalizes the SU(2) group action, these networks provide a natural building block for constructing parameterized equivariant quantum circuits. We prove that our construction is mathematically equivalent to other known constructions, such as those based on twirling and generalized permutations, but more direct to implement on quantum hardware. The efficacy of our constructed circuits is tested by solving the ground state problem of SU(2) symmetric Heisenberg models on the one-dimensional triangular lattice and the Kagome lattice. Our results highlight that our equivariant circuits boost the performance of quantum variational algorithms, indicating broader applicability to other real-world problems.