Quantum Architecture Search for Solving Quantum Machine Learning Tasks
This work addresses the challenge of designing efficient quantum circuit architectures for researchers in quantum computing, though it is incremental as it applies an existing RL method to a new context.
The paper tackled the problem of automating Quantum Architecture Search (QAS) for quantum machine learning tasks by introducing RL-QAS, a reinforcement learning framework that discovered low-complexity circuit designs achieving high test accuracy on Iris and binary MNIST datasets.
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.