Methods and system for using a quantum computer to generate a graph neural network

A system for generating a Graph Neural Network, GNN, wherein:

• i) the GNN comprises a plurality of layers wherein:
• one of the said layers is a first layer;
• the first layer being the initial layer or a hidden layer in the GNN;
• and,
• another one of the said layers is a second layer;
• the second layer being subsequent to the first layer;
• ii) the GNN being associated with a graph comprising a plurality of graph nodes and edges;
• iii) for the first layer:
• at least a first graph node of the plurality of graph nodes is represented by a first set of one or more first node features;
• at least a second graph node of the plurality of graph nodes is represented by a first set of one or more second node features;
• the first graph node being connected to the second graph node by one of the edges;
• the system comprising a computer processor configured to:
• I) generate, for the second layer, a second set of one or more first node features by:
• outputting one or more control signals to an electromagnetic, EM, source for inputting electromagnetic radiation to at least a first qubit and a second qubit of a quantum computer;
• the first graph node represented by at least the first qubit;
• the second graph node represented by at least the second qubit;
• the EM radiation interacting the first qubit with the second qubit;
• receiving measured quantum states of the first and second qubits after the start of the inputting of the EM radiation;
• determining an aggregation factor from the measured quantum states;
• and,
• using the aggregation factor and the first set of one or more second node features to generate the second set of one or more features for the first graph node for the second layer;
• and,
• II) generate the GNN at least based upon the generated second set of one or more first node features.