Quantum Architecture Search for Solving Quantum Machine Learning Tasks

• URL: http://arxiv.org/abs/2509.11198v1
• Date: Sun, 14 Sep 2025 09:55:38 GMT
• Title: Quantum Architecture Search for Solving Quantum Machine Learning Tasks
• Authors: Michael Kölle, Simon Salfer, Tobias Rohe, Philipp Altmann, Claudia Linnhoff-Popien,
• Abstract summary: This work introduces RL-QAS, a framework that applies RL to discover effective circuit architectures for classification tasks.<n>We evaluate RL-QAS using the Iris and binary MNIST datasets.
• Score: 3.515829683606796
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum computing leverages quantum mechanics to address computational problems in ways that differ fundamentally from classical approaches. While current quantum hardware remains error-prone and limited in scale, Variational Quantum Circuits offer a noise-resilient framework suitable for today's devices. The performance of these circuits strongly depends on the underlying architecture of their parameterized quantum components. Identifying efficient, hardware-compatible quantum circuit architectures -- known as Quantum Architecture Search (QAS) -- is therefore essential. Manual QAS is complex and error-prone, motivating efforts to automate it. Among various automated strategies, Reinforcement Learning (RL) remains underexplored, particularly in Quantum Machine Learning contexts. This work introduces RL-QAS, a framework that applies RL to discover effective circuit architectures for classification tasks. We evaluate RL-QAS using the Iris and binary MNIST datasets. The agent autonomously discovers low-complexity circuit designs that achieve high test accuracy. Our results show that RL is a viable approach for automated architecture search in quantum machine learning. However, applying RL-QAS to more complex tasks will require further refinement of the search strategy and performance evaluation mechanisms.

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