Training quantum generative networks based on marginal and joint distributions

A non-transitory computer-readable storage medium configured to store instructions that, in response to being executed, causes a system to perform operations, the operations comprising:

• initializing, on a quantum computer, a parameterized quantum circuit that implements a Quantum Neural Network (QNN) with trainable parameters to learn a first conditional distribution of a training dataset comprising input data and a first output data;
• loading a marginal distribution associated with the input data on a first set of qubits of a first set of registers of the quantum computer;
• operating the QNN on the marginal distribution to predict the first conditional distribution based on the input data and the first output;
• loading a first joint distribution associated with the input data and the first output data on a second set of qubits of a second set of registers of the quantum computer;
• operating the QNN on the marginal distribution associated with the input data and the first conditional distribution to generate a second conditional distribution associated with the input data and second output data;
• generating a second joint distribution based on the second conditional distribution;
• loading the second joint distribution on a third set of qubits of a third set of registers of the quantum computer;
• extracting joint quantum measurements over the second joint distribution loaded on the third set of qubits of the third set of registers and the first joint distribution loaded of the second set of qubits of the second set of registers;
• and
• training the QNN based on the joint quantum measurements to configure the QNN to learn the first conditional distribution.

An electronic device, comprising:

• a memory configured to store instructions;
• and
• a processor, coupled to the memory, configured to execute the instructions to perform a process comprising:
• initializing, on a quantum computer, a parameterized quantum circuit that implements a Quantum Neural Network (QNN) with trainable parameters to learn a first conditional distribution of a training dataset comprising input data and a first output data;
• loading a marginal distribution associated with the input data on a first set of qubits of a first set of registers of the quantum computer;
• operating the QNN on the marginal distribution to predict the first conditional distribution based on the input data and the first output;
• loading a first joint distribution associated with the input data and the first output data on a second set of qubits of a second set of registers of the quantum computer;
• operating the QNN on the marginal distribution associated with the input data and the first conditional distribution to generate a second conditional distribution associated with the input data and second output data;
• generating a second joint distribution based on the second conditional distribution;
• loading the second joint distribution on a third set of qubits of a third set of registers of the quantum computer;
• extracting joint quantum measurements over the second joint distribution loaded on the third set of qubits of the third set of registers and the first joint distribution loaded of the second set of qubits of the second set of registers;
• and
• training the QNN based on the joint quantum measurements to configure the QNN to learn the first conditional distribution.