Abstract: Technologies for a quantum/classical hybrid approach to solving optimization problems are disclosed. In the illustrative embodiment, an optimization problem is decomposed into two sub-problems. The first sub-problem is solved on a classical computer, and a result from the first sub-problem is provided to a quantum computer. The quantum computer then solves the second sub-problem based on the result of the first sub-problem from the classical computer. The quantum computer can then provide a result to the classical computer to re-solve the first problem. The iterative calculation is continued until an end condition is met.

Abstract: Technologies for a quantum/classical hybrid approach to solving optimization problems is disclosed. In the illustrative embodiment, an optimization problem is decomposed into two sub-problems. The first sub-problem is solved on a classical computer, and a result from the first sub-problem is provided to a quantum computer. The quantum computer then solves the second sub-problem based on the result of the first sub-problem from the classical computer. The quantum computer can then provide a result to the classical computer to re-solve the first problem. The iterative calculation is continued until an end condition is met.

Abstract: A method for quantum computing-assisted portfolio selection may include a classical computer program: (1) receiving a plurality of asset selection parameters for an asset portfolio; (2) initializing a current selection of assets from a plurality of available assets; (3) setting a risk upper bound value to a risk upper bound initial value, and a risk lower bound value to a risk lower bound initial value; (4) instructing a quantum computer to solve a first sub-problem; (5) calculating an objective functional value; (6) setting the risk upper bound value to the objective functional value; (7) instructing the quantum computer to determine a new selection of assets by solving a second sub-problem using a second quantum algorithm; and (8) returning an optimal portfolio selection.

Abstract: Technologies for a quantum/classical hybrid approach to solving optimization problems is disclosed. In the illustrative embodiment, an optimization problem is decomposed into two sub-problems. The first sub-problem is solved on a classical computer, and a result from the first sub-problem is provided to a quantum computer. The quantum computer then solves the second sub-problem based on the result of the first sub-problem from the classical computer. The quantum computer can then provide a result to the classical computer to re-solve the first problem. The iterative calculation is continued until an end condition is met.