Transpiling quantum circuits by a transformers-based algorithm

• URL: http://arxiv.org/abs/2512.09834v1
• Date: Wed, 10 Dec 2025 17:13:34 GMT
• Title: Transpiling quantum circuits by a transformers-based algorithm
• Authors: Michele Banfi, Paolo Zentilini, Sebastiano Corli, Enrico Prati,
• Abstract summary: 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.<n>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%.
• Score: 0.5249805590164902
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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