| CPC G06N 3/08 (2013.01) [G06N 10/00 (2019.01)] | 19 Claims |
|
1. A computer implemented method for designing a quantum control trajectory for implementing a quantum gate using quantum hardware, the method comprising:
representing, by a classical processor, the quantum gate as a sequence of control actions;
applying, by a classical processor, a reinforcement learning model to iteratively adjust each control action in the sequence of control actions to determine a quantum control trajectory that implements the quantum gate and reduces leakage, infidelity and total runtime of the quantum gate during the iterative adjustments, comprising, for each iteration:
determining, by an agent, a control action for the iteration based on a current state of a quantum system included in the quantum hardware;
updating, by a training environment, the current state of the quantum system to a subsequent state of the quantum system using the determined control action and sample control noise;
determining, by the agent, a discounted future reward using i) a universal control cost function that penalizes leakage, infidelity and total gate runtime as a reinforcement learning discounted future reward function and ii) the updated state of the quantum system; and
adjusting, by the agent and based on the determined discounted future reward, values of one or more control trajectory parameters for the iteration; and
implementing, by the quantum hardware, the quantum gate using the quantum control trajectory.
|