A semi-agnostic ansatz with variable structure for quantum machine learning
This work addresses trainability and noise issues in quantum machine learning for fields such as chemistry and data science, representing an incremental improvement in method design.
The authors tackled the challenge of training deep variational quantum algorithms (VQAs) by introducing VAns, a variable structure approach that dynamically grows and removes quantum gates during optimization to keep ansatzes shallow, which demonstrated successful results in applications like variational quantum eigensolver, quantum autoencoder, and unitary compilation.
Quantum machine learning -- and specifically Variational Quantum Algorithms (VQAs) -- offers a powerful, flexible paradigm for programming near-term quantum computers, with applications in chemistry, metrology, materials science, data science, and mathematics. Here, one trains an ansatz, in the form of a parameterized quantum circuit, to accomplish a task of interest. However, challenges have recently emerged suggesting that deep ansatzes are difficult to train, due to flat training landscapes caused by randomness or by hardware noise. This motivates our work, where we present a variable structure approach to build ansatzes for VQAs. Our approach, called VAns (Variable Ansatz), applies a set of rules to both grow and (crucially) remove quantum gates in an informed manner during the optimization. Consequently, VAns is ideally suited to mitigate trainability and noise-related issues by keeping the ansatz shallow. We employ VAns in the variational quantum eigensolver for condensed matter and quantum chemistry applications, in the quantum autoencoder for data compression and in unitary compilation problems showing successful results in all cases.