Computer implemented method for real time quantum compiling based on artificial intelligence

A computer implemented method for real time quantum compiling, comprising:

• providing a unitary matrix, representing a single-qubit or multi-qubit quantum operation to be carried out by a quantum computer, to a machine-learning trained algorithm;
• providing to said machine-learning trained algorithm an information representing a base of quantum gates to be used as basic elements to build a quantum circuit corresponding to operation of said unitary matrix, within a given tolerance;
• providing said machine-learning trained algorithm with a tolerance parameter, representative of said given tolerance, and with processing termination information, representative of a criterion to determine an end of processing by the algorithm;
• determining a quantum circuit corresponding to the operation of said unitary matrix, within the given tolerance and according to said processing termination information, said quantum circuit comprising a combination of said base quantum gates;
• wherein the step of determining is carried out by said machine-learning trained algorithm, based on a policy encoded in the machine-learning trained algorithm by a reinforced learning training phase, performed before and independently of the real time quantum compiling;
• providing, as a result of the real time quantum compiling, a quantum circuit description information describing the determined quantum circuit in terms of said combination of base quantum gates;
• wherein said reinforced learning training phase is carried out based on a Reinforcement Learning procedure and comprises:
• defining an unbiased set of training target unitary matrices, which randomly represent target points in a unitary matrices space defined by the special unitary group SU(2N), where N is the number of the qubits involved in the quantum operation to be performed;
• defining one or more training base sets of quantum gates;
• executing multiple episodes of said Reinforcement Learning procedure, each episode being executed having as a target a respective one of the unitary matrices of said training target set of unitary matrices, and using a respective one of said training base sets of quantum gates;
• wherein each episode of said Reinforced Learning procedure, in the training phase, comprises:
• applying a reinforcement learning model to iteratively adjust a current approximating quantum circuit by actions decided by an agent, based on a matrix representing the operation of the current approximating quantum circuit based on a reward function.