Abstract: A method is provided. The method includes: determining L+1 parameterized quantum circuits and L data encoding circuits; obtaining a plurality of training data pairs including independent variable data and dependent variable data. The method further includes, for each of the training data pairs: cascading the parameterized quantum circuits and the data encoding circuits alternately to form a quantum neural network, where the data encoding circuits code the independent variable data in the training data pair; and operating the quantum neural network from an initial quantum state and performing measurement on the output of the quantum neural network, to obtain a measurement result. The method further includes, computing a loss function based on measurement results corresponding to all the training data pairs and corresponding dependent variable data; and adjusting parameters to be trained of the parameterized quantum circuits and the data encoding circuits to minimize the loss function.

Abstract: A method is provided, including: initializing a first quantum neural network to be trained and at least two second quantum neural networks to be trained, and obtaining a quantum state training set; identifying one or more qubit pairs in an entangled state shared by the two parties; for each of a plurality of quantum state combinations: inputting quantum states of the quantum state combination into the respectively corresponding first quantum neural network, and measuring qubits output by the first quantum neutral network and not input into each of the at least two second quantum neural networks of each party so as to obtain a corresponding quantum state; selectively running a second quantum neural network respectively according to a measuring result so as to obtain quantum state output by the two parties, to compute a loss function; and adjusting a parameter value to make the loss function reach a minimum value.