DeepQPrep: Neural Network Augmented Search for Quantum State Preparation

Abstract

There is an increasing interest in the area of quantum computing but developing quantum algorithms is difficult. Neural Network augmented search algorithms have proven quite successful for general search problems (like program generation) but current approaches to quantum program generation make very restricted use of them. In this paper we present DeepQPrep, a Neural Network based approach to generate quantum circuits for state preparation; a common yet expensive task needed in many applications of quantum computing. We illustrate that Neural Network augmented search algorithms have significant promise for automated quantum program generation; DeepQPrep generated programs were able to solve 99% and 76.9% of 20000 previously unseen state prepartion tasks in a supervised machine learning context within two different application scenarios. The circuits produced by DeepQPrep are also shallower (on average) than their ground truth counterparts. We also compare DeepQPrep to IBM Qiskit’s approach to state preparation and illustrate that even when constrained, DeepQPrep generates significantly shallower circuits despite Qiskit solving more of the state preparation tasks. Based on our results, we argue that neural network augmented search approaches exhibit significant promise for generalised approaches to quantum program induction warranting further study in more complex scenarios.

Keywords

• Quantum state preparation
• quantum program synthesis
• graph neural networks
• gate-based circuits
• supervised machine learning