Hybrid Quantum Computation Architecture for Solving Quadratic Unconstrained Binary Optimization Problems

A method for driving a quantum computational network for determining an extremal value of a cost function for solutions of a quadratic unconstrained binary optimization problem includes initializing qubits, sequentially applying layers of quantum gates to the qubits, determining an output state of the quantum computational network for obtaining a solution associated with the set of variational parameters {right arrow over (θ)}, and determining an output state for shifted variational parameters {right arrow over (θ)}* to evaluate a partial derivative with respect to the subset of the variational parameters {right arrow over (θ)} for determining a gradient of the cost function based on the output state for the shifted variational parameters {right arrow over (θ)}*, and by updating the variational parameters {right arrow over (θ)} based on an update function of a moving average over the gradient of the cost function and of a moving average over the squared gradient of the cost function.