Performing quantum error mitigation at runtime using trained machine learning model

Abstract

A method, system, and computer program product for runtime quantum error mitigation. Training data, which includes noisy expectation values and target expectation values (noiseless expectation values), is generated. A machine learning model is then trained using the training data to perform quantum error mitigation based on learning the relationships between target and noisy expectation values. That is, such a machine learning model is trained to generate target expectation values based on inputted noisy expectation values. Upon executing a quantum circuit on a quantum computer creating quantum results, quantum error mitigation is performed on the quantum results at runtime using the trained machine learning model. In this manner, there are significant savings in quantum execution time while improving the accuracy of the results in performing quantum error mitigation on quantum results at runtime without additional mitigation circuits.