Quantum circuit architecture search for variational quantum algorithms
This work addresses the challenge of optimizing quantum circuit designs for VQAs to achieve better performance on noisy quantum devices, which is an incremental improvement in quantum machine learning.
The authors tackled the problem of variational quantum algorithms (VQAs) being hindered by noise and poor trainability due to suboptimal circuit architectures, and they developed a quantum architecture search (QAS) scheme that automatically finds near-optimal ansatze, which outperformed pre-selected ansatze in data classification and quantum chemistry tasks on both simulators and real hardware.
Variational quantum algorithms (VQAs) are expected to be a path to quantum advantages on noisy intermediate-scale quantum devices. However, both empirical and theoretical results exhibit that the deployed ansatz heavily affects the performance of VQAs such that an ansatz with a larger number of quantum gates enables a stronger expressivity, while the accumulated noise may render a poor trainability. To maximally improve the robustness and trainability of VQAs, here we devise a resource and runtime efficient scheme termed quantum architecture search (QAS). In particular, given a learning task, QAS automatically seeks a near-optimal ansatz (i.e., circuit architecture) to balance benefits and side-effects brought by adding more noisy quantum gates to achieve a good performance. We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks. In the problems studied, numerical and experimental results show that QAS can not only alleviate the influence of quantum noise and barren plateaus, but also outperforms VQAs with pre-selected ansatze.