AI Control Signals for Analogue Quantum Computer Optimization

The patent applies preliminary action by pre-training machine learning models using data from quantum simulations or emulations before actual quantum computation. The model learns optimal electromagnetic signal parameters in advance through supervised or reinforcement learning, so that during actual operation, parameters can be determined quickly without time-consuming trial and error. This resolves the contradiction by performing the time-consuming optimization work beforehand.

The patent uses copying by creating virtual copies of the quantum system through classical emulations or quantum simulations to generate training data. Instead of repeatedly running physical quantum experiments to find optimal parameters, the system copies the quantum behavior in silico to train ML models, which then guide actual quantum computer operation. This eliminates the need for repeated physical trial and error experiments.

The patent applies dynamics by implementing adaptive machine learning models that can dynamically adjust to varying problem parameters. The reinforcement learning component allows the system to learn optimal strategies through interaction with the quantum system, adapting to different combinatorial problems and their parameters. This provides both reliability through learned optimal solutions and productivity through efficient adaptation without retraining from scratch.

The patent uses feedback through reinforcement learning mechanisms where the system receives feedback from quantum computation results to improve future parameter selections. The model learns from the outcomes of previous computations and adjusts electromagnetic signal parameters accordingly, improving both solution reliability and computational efficiency over time through iterative optimization.