Neural Architecture Search Algorithms for Quantum Autoencoders
This work addresses the scalability issue in quantum algorithm design for researchers, though it is incremental as it adapts existing NAS methods to quantum circuits.
The authors tackled the manual effort in quantum circuit design by proposing two Quantum-NAS algorithms, which automated the process and found efficient autoencoder designs that outperformed baselines on tasks like quantum data denoising, classical data compression, and pure quantum data compression.
The design of quantum circuits is currently driven by the specific objectives of the quantum algorithm in question. This approach thus relies on a significant manual effort by the quantum algorithm designer to design an appropriate circuit for the task. However this approach cannot scale to more complex quantum algorithms in the future without exponentially increasing the circuit design effort and introducing unwanted inductive biases. Motivated by this observation, we propose to automate the process of cicuit design by drawing inspiration from Neural Architecture Search (NAS). In this work, we propose two Quantum-NAS algorithms that aim to find efficient circuits given a particular quantum task. We choose quantum data compression as our driver quantum task and demonstrate the performance of our algorithms by finding efficient autoencoder designs that outperform baselines on three different tasks - quantum data denoising, classical data compression and pure quantum data compression. Our results indicate that quantum NAS algorithms can significantly alleviate the manual effort while delivering performant quantum circuits for any given task.