Curriculum reinforcement learning for quantum architecture search under hardware errors

The key challenge in the noisy intermediate-scale quantum era is findinguseful circuits compatible with current device limitations. Variational quantumalgorithms (VQAs) offer a potential solution by fixing the circuit architectureand optimizing individual gate parameters in an external loop. However,parameter optimization can become intractable, and the overall performance ofthe algorithm depends heavily on the initially chosen circuit architecture.Several quantum architecture search (QAS) algorithms have been developed todesign useful circuit architectures automatically. In the case of parameteroptimization alone, noise effects have been observed to dramatically influencethe performance of the optimizer and final outcomes, which is a key line ofstudy. However, the effects of noise on the architecture search, which could bejust as critical, are poorly understood. This work addresses this gap byintroducing a curriculum-based reinforcement learning QAS (CRLQAS) algorithmdesigned to tackle challenges in realistic VQA deployment. The algorithmincorporates (i) a 3D architecture encoding and restrictions on environmentdynamics to explore the search space of possible circuits efficiently, (ii) anepisode halting scheme to steer the agent to find shorter circuits, and (iii) anovel variant of simultaneous perturbation stochastic approximation as anoptimizer for faster convergence. To facilitate studies, we developed anoptimized simulator for our algorithm, significantly improving computationalefficiency in simulating noisy quantum circuits by employing the Pauli-transfermatrix formalism in the Pauli-Liouville basis. Numerical experiments focusingon quantum chemistry tasks demonstrate that CRLQAS outperforms existing QASalgorithms across several metrics in both noiseless and noisy environments.

Further reading#

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