Abstract: A method may include obtaining a multi-dimensional training dataset that includes multiple datums. Each of the datums may correspond to a number of quantum bits (qubits) and may represent a quantum state. The method may also include generating, using a quantum computing device, a Gram matrix based on the multiple datums. In addition, the method may include determining, using a classical computing device, multiple operators according to a constraint defined by the Gram matrix. Each of the operators may be configured as a proxy for a corresponding datum. Further, the method may include assigning, using the classical computing device, each of the operators to a label.

Abstract: A method may include obtaining a graph dataset that represents a social network and includes a plurality of nodes. The method may include partitioning the graph dataset into a first node cluster and a second node cluster in which the first node cluster and the second node cluster each include one or more nodes from the graph dataset. The method may include identifying one or more first candidate influential nodes included in the first node cluster and one or more second candidate influential nodes included in the second node cluster. The method may include selecting one or more of the first candidate influential nodes and the second candidate influential nodes as influencer nodes and identifying a respective object included in the social network corresponding to each of the influencer nodes.

Abstract: A method may include obtaining a multi-dimensional training dataset that includes multiple datums. Each of the datums may correspond to a number of quantum bits (qubits) and may represent a quantum state. The method may also include generating, using a quantum computing device, a Gram matrix based on the multiple datums. In addition, the method may include determining, using a classical computing device, multiple operators according to a constraint defined by the Gram matrix. Each of the operators may be configured as a proxy for a corresponding datum. Further, the method may include assigning, using the classical computing device, each of the operators to a label.

Abstract: A method may include obtaining a graph dataset that represents a social network and includes a plurality of nodes. The method may include partitioning the graph dataset into a first node cluster and a second node cluster in which the first node cluster and the second node cluster each include one or more nodes from the graph dataset. The method may include identifying one or more first candidate influential nodes included in the first node cluster and one or more second candidate influential nodes included in the second node cluster. The method may include selecting one or more of the first candidate influential nodes and the second candidate influential nodes as influencer nodes and identifying a respective object included in the social network corresponding to each of the influencer nodes.