Unlocking the power of global quantum gates with machine learning

TL;DR

This paper introduces a machine learning-based variational method to efficiently utilize global quantum gates, enabling more practical quantum circuit implementations for complex models like the Heisenberg and toric code Hamiltonians.

Contribution

It presents a novel variational approach with parameterized global gates, addressing the challenge of compiling local gates into global operations in quantum computing.

Findings

Demonstrates expressibility of the proposed ansatz

Successfully applies method to ground state preparation

Highlights potential advantages in quantum circuit complexity

Abstract

In conventional circuit-based quantum computing architectures, the standard gate set includes arbitrary single-qubit rotations and two-qubit entangling gates. This choice is not always aligned with the native operations available in certain hardware, where the natural entangling gates are not restricted to two qubits but can act on multiple, or even all, qubits simultaneously. However, leveraging the capabilities of global quantum operations for algorithm implementations is highly challenging, as directly compiling local gate sequences into global gates usually gives rise to a quantum circuit that is more complex than the original one. Here, we circumvent this difficulty using a variational approach. Specifically, we study parameterized circuit ansatze composed of a finite number of global gates and layers of single-qubit unitaries. We demonstrate the expressibility of these ansatze and apply them to the problem of ground state preparation for the Heisenberg model and the toric code Hamiltonian, highlighting their potential for offering practical advantages.