Generative quantum eigensolver with constrained circuit-cutting overhead

Understand

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.