Transpiling quantum circuits by a transformers-based algorithm

TL;DR

This paper introduces a transformer-based model that efficiently transpiles quantum circuits from standard formats to hardware-specific gate sets, achieving high accuracy and scalable complexity for quantum computing applications.

Contribution

It develops the first transformer model for quantum circuit transpilation, demonstrating high accuracy and polynomial scalability for circuits up to five qubits.

Findings

Transpilation accuracy of 99.98% for circuits up to five qubits.

Model complexity scales polynomially with circuit depth and size.

Feasibility of training on HPC infrastructures for larger models.

Abstract

Transformers have gained popularity in machine learning due to their application in the field of natural language processing. They manipulate and process text efficiently, capturing long-range dependencies among data and performing the next word prediction. On the other hand, gate-based quantum computing is based on controlling the register of qubits in the quantum hardware by applying a sequence of gates, a process which can be interpreted as a low level text programming language. We develop a transformer model capable of transpiling quantum circuits from the qasm standard to other sets of gates native suited for a specific target quantum hardware, in our case the set for the trapped-ion quantum computers of IonQ. The feasibility of a translation up to five qubits is demonstrated with a percentage of correctly transpiled target circuits equal or superior to 99.98%. Regardless the depth of the register and the number of gates applied, we prove that the complexity of the transformer model scales, in the worst case scenario, with a polynomial trend by increasing the depth of the register and the length of the circuit, allowing models with a higher number of parameters to be efficiently trained on HPC infrastructures.