Multi-state quantum optimization engine

A system of multiple computing resources, comprising:

• one or more classical processors;
• and
• quantum computing resources;
• wherein the one or more classical processors and the quantum computing resources are configured to perform operations comprising:
• receiving input data comprising (i) data specifying system parameters of the optimization task to be solved, and (ii) data specifying task objectives for solving the optimization task, comprising one or more local task objectives and one or more global task objectives;
• processing, by a local optimization engine, the received input data to obtain one or more initial solutions to the optimization task based on the local task objectives, comprising:
• transmitting, using a router, from the local optimization engine and to a first quantum computing resource, (i) the received data specifying the optimization task to be solved, and (ii) data representing one or more of the local task objectives, the router determining which computations to outsource to the first computing resource;
• and
• receiving, from the first quantum computing resource and at the local optimization engine, data representing an initial solution to the optimization task;
• and
• processing, by a global optimization engine, the generated one or more initial solutions using a second quantum computing resource to generate a global solution to the optimization task based on the global task objectives, comprising:
• transmitting, from the global optimization engine and to the second quantum computing resource, (i) data representing the one or more obtained initial solutions to the optimization task, and (ii) the received data specifying the optimization task to be solved, and (iii) data representing the one or more global task objectives;
• and
• receiving, from the second quantum computing resource and at the global optimization engine, data representing the global solution to the optimization task;
• comparing, using a comparison module, the data representing the global solution to the one or more global task objectives to determine whether the global solution satisfies the one or more global task objectives;
• and
• adjusting values of the system parameters using the generated global solution to the optimization task.