Abstract: The optimization of circuit parameters of variational quantum algorithms is a challenge for the practical deployment of near-term quantum computing algorithms. Embodiments relate to a hybrid quantum-classical optimization methods. In a first stage, analytical tomography fittings are performed for a local cluster of circuit parameters via sampling of the observable objective function at quadrature points in the circuit parameters. Optimization may be used to determine the optimal circuit parameters within the cluster, with the other circuit parameters frozen. In a second stage, different clusters of circuit parameters are then optimized in “Jacobi sweeps,” leading to a monotonically convergent fixed-point procedure. In a third stage, the iterative history of the fixed-point Jacobi procedure may be used to accelerate the convergence by applying Anderson acceleration/Pulay's direct inversion of the iterative subspace (DIIS).

Abstract: The optimization of circuit parameters of variational quantum algorithms is a challenge for the practical deployment of near-term quantum computing algorithms. Embodiments relate to a hybrid quantum-classical optimization methods. In a first stage, analytical tomography fittings are performed for a local cluster of circuit parameters via sampling of the observable objective function at quadrature points in the circuit parameters. Optimization may be used to determine the optimal circuit parameters within the cluster, with the other circuit parameters frozen. In a second stage, different clusters of circuit parameters are then optimized in “Jacobi sweeps,” leading to a monotonically convergent fixed-point procedure. In a third stage, the iterative history of the fixed-point Jacobi procedure may be used to accelerate the convergence by applying Anderson acceleration/Pulay's direct inversion of the iterative subspace (DIIS).

Abstract: The optimization of circuit parameters of variational quantum algorithms is a challenge for the practical deployment of near-term quantum computing algorithms. Embodiments relate to a hybrid quantum-classical optimization methods. In a first stage, analytical tomography fittings are performed for a local cluster of circuit parameters via sampling of the observable objective function at quadrature points in the circuit parameters. Optimization may be used to determine the optimal circuit parameters within the cluster, with the other circuit parameters frozen. In a second stage, different clusters of circuit parameters are then optimized in “Jacobi sweeps,” leading to a monotonically convergent fixed-point procedure. In a third stage, the iterative history of the fixed-point Jacobi procedure may be used to accelerate the convergence by applying Anderson acceleration/Pulay's direct inversion of the iterative subspace (DIIS).

Abstract: A solid inductor for forming an inner wall of a coolant box for an electromagnetic continuous casting system has a deflector integrally formed on the exterior surface thereof to deflect coolant passing through passages in the solid inductor. In one embodiment, the deflector is formed by an indentation on the exterior surface of the deflector, and in another embodiment, it is formed by a protrusion on the exterior surface of the solid inductor.