Switching optimization

1. A computer-implemented method for allocating a plurality of transactions to a plurality of networks, the method comprising:

receiving from a router at a classical computer a plurality of first information associated with a respective one of the plurality of transactions;

receiving from the router at the classical computer a plurality of second information associated with a respective one of the plurality of networks;

determining in the classical computer a switching characteristic to be optimized by the allocation of the plurality of transactions to the plurality of networks;

expressing by the classical computer in at least one expression to a quantum computer, the plurality of first information, the plurality of second information and the switching characteristic;

providing to the quantum computer the at least one expression to determine the allocation of the plurality of transactions to the plurality of networks;

receiving from the quantum computer the allocation; and

providing the allocation to the router.

2. The method of claim 1 , wherein the expressing at least one expression by the classical computer comprises:

extracting a first plurality of variables from the plurality of first information and a second plurality variables from the plurality of second information;

determining a base expression with the first and second plurality of variables;

determining a conditional expression from the first and second plurality of variables.

3. The method of claim 2 , further comprising combining the base expression and the conditional expression.

4. The method of claim 2 , wherein the determination of the base expression further uses a least one network constraint parameter.

5. The method of claim 2 , wherein the determination of the conditional expression further uses at least one network constraint parameter.

6. The method of claim 2 , wherein determination of the conditional expression uses further conditional information.

7. The method of claim 2 , wherein the base expression is a base cost expression.

8. The method of claim 2 wherein the conditional expression is a conditional discount expression.

9. The method of claim 1 wherein the expressions are determined to minimize one of a transaction cost, a transaction latency and/or a network loading.

10. The method of claim 1 , further comprising resolving in the quantum computer the at least one expression to determine the allocation of the plurality of transactions to the plurality of networks.

11. A system for allocating a plurality of transactions to a plurality of networks, the system comprising:

a classical computer configured to:

receive a plurality of first information associated with a respective one of the plurality of transactions;

receive a plurality of second information associated with a respective one of the plurality of networks;

determine a switching characteristic to be optimized by the allocation of the plurality of transactions to the plurality of networks; and

express in at least one expression to a quantum computer, the plurality of first information, the plurality of second information and the switching characteristic; and

a quantum computer configured to:

resolve the at least one expression to determine the allocation of the plurality of transactions to the plurality of networks;

the system being further configured to:

provide the allocation of the transactions to a router.

12. A switching optimization computer implemented method comprising the steps:

(a) determining, by a classical computer, a non-convex sub-problem and a convex sub-problem of a constrained optimization problem, the constrained optimization problem comprising:

a binary variable indicative of a discount being activated for a respective network; and

a continuous variable representative of a first portion of a first volume of transactions to be switched to a first network, and a second portion of the first volume of transactions to be switched to a second network;

(b) solving, by a quantum computer, the non-convex sub-problem, thereby generating a binary solution;

wherein the continuous variable is fixed for the step of solving the non-convex sub-problem;

(c) solving, by a quantum computer, the convex sub-problem, thereby generating a continuous solution;

wherein the convex sub-problem is solved using the binary solution, said binary solution being fixed;

(d) repeating steps (b) and (c), using the continuous solution as the continuous variable, until a threshold is met; and

(e) mapping the binary solution and the continuous solution to the constrained optimization problem, thereby providing an optimized switching.

13. The method of claim 12 , wherein the non-convex sub-problem is a quadratic unconstrained binary optimization (QUBO) sub-problem.

14. The method of claim 12 , wherein the constrained optimization problem is solved according to an alternating direction method of multipliers (ADMM) method.

15. The method of claim 12 , wherein continuous solution is determined according to a sequential convex programming method.

16. The method of claim 12 , further comprising determining the constrained optimization problem based on a set of switching details, wherein the constrained optimization problem comprises a plurality of constraints.

17. The method of claim 12 , wherein the plurality of constraints comprises:

a discount amount constraint;

a discount volume constraint;

a transaction allocation constraint; and

a binary constraint.

18. The method of claim 12 , wherein the threshold is an iteration threshold.

19. The method of claim 12 , wherein the threshold is a solution plateau threshold.