Problem specific ion native ansatz for combinatorial optimization

TL;DR

This paper introduces a heuristic for selecting problem-specific ansatz configurations in ion native digital-analog quantum circuits, improving trainability and reducing circuit depth for combinatorial optimization problems.

Contribution

It proposes a new heuristic method for identifying problem-specific ansatz configurations that enhance trainability in ion native quantum algorithms.

Findings

The heuristic improves the cost landscape for random instances of the Sherrington-Kirkpatrick Hamiltonian.

The approach reduces the required circuit depth compared to standard QAOA.

It demonstrates better trainability for up to 15 qubits.

Abstract

Variational quantum algorithms have become a standard approach for solving a wide range of problems on near-term quantum computers. Identifying an appropriate ansatz configuration for variational algorithms, however, remains a challenging task, especially when taking into account restrictions imposed by real quantum platforms. This motivated the development of digital-analog quantum circuits, where sequences of quantum gates are alternated with natural Hamiltonian evolutions. A prominent example is the use of the controllable long-range Ising interaction induced in ion-based quantum computers. This interaction has recently been applied to develop an algorithm similar to the quantum approximate optimization algorithm (QAOA), but native to the ion hardware. The performance of this algorithm has demonstrated a strong dependence on the strengths of the individual ion-ion interactions, which serve as ansatz hyperparameters. In this work, we propose a heuristic for identifying a problem-specific ansatz configuration, which enhances the trainability of the ion native digital-analog circuit. The proposed approach is systematically applied to random instances of the Sherrington-Kirkpatrick Hamiltonian for up to 15 qubits, providing favorable cost landscapes. As the result, the developed approach identifies a well-trainable ion native ansatz, which requires a lower circuit depth to solve specific problems as compared to standard QAOA. This brings the algorithm one step closer to its large scale practical implementation.