Generative quantum eigensolver with constrained circuit-cutting overhead

Abstract

Generative quantum eigensolver (GQE) is a hybrid quantum-classical algorithm that iteratively trains a classical generative machine learning model such that the model can generate quantum circuits with desired properties such as approximating molecular ground states. It offers as many potential applications and as much flexibility as variational quantum eigensolvers, while avoiding the problem of barren plateaus. Quantum circuit cutting (QCC) is a technique to perform quantum computations that require more qubits than available on single quantum devices. It comes with considerable sampling overhead depending on the structure of the circuit to be cut and how the circuit is cut. To make QCC practical, therefore, the circuits to be cut must be designed such that their execution is meaningful and QCC overhead is kept small. In this work, we extend GQE such that the generative model only produces circuits whose overhead by QCC is upper-bounded, while retaining the original purpose of GQE. Consequently, our proposal not only enhances the applicability of GQE through the use of QCC, but also provides a practical application for QCC. Using a transformer decoder implementation of GQE, we evaluate our method through simulated ground state search experiments on the BeH_2 molecule. A new loss function and a hybrid online/offline training strategy are also introduced and it is observed that these tools improve convergence and final energy values.