Quantum tracking method and apparatus

A system useful for tracking one or more targets in a field of view, the system comprising:

• a classical computing system coupled to a quantum computing system, wherein
• (a) the classical processing system is configured for:
• i) receiving positioning data from a sensing system scanning a field of view containing one or more targets over successive sensing times (t0, . . . tk) thereby collecting over time a batch (Bk) of scans (S1, . . . , Sk) of sensed plots;
• ii) for a current batch (Bk) of scans (S1, . . . , Sk) of sensed plots:
• processing a graph (G) containing said sensed plots as N vertices (V) and possible connections between pairs of plots associated with consecutive scans as edges (E);
• defining a cost function of the edges so as to favor geometrically relevant edge patterns for the targets being tracked;
• associating in said cost function each edge with a quantum state encoded by log2 N binary variables;
• optimizing said cost function using a quantum processing system by determining the ground state of a Hamiltonian encoding the cost function using the Variational Quantum Eigensolver algorithm by converting said Hamiltonian to Pauli strings and measuring expectation values of said Pauli strings;
• processing one or more tracks based on a histogram built on statistics provided by the measured expectation value of said Pauli strings.

A computer program product facilitating tracking of one or more targets in a field of view of a sensing system, the computer program product comprising computer readable storage medium having program instructions embodied therewith, the program instructions executable by a classical processing system and a quantum processing system coupled therewith to:

• (a) receive, using the classical computing system, positioning data and/or doppler data from a sensing system scanning a field of view containing one or more targets over successive sensing times (t0, . . . tk) thereby collecting over time a batch (Bk) of scans (S1, . . . , Sk) of sensed plots;
• (b) using the classical computing system, for a current batch (Bk) of scans (S1, . . . , Sk) of sensed plots:
• i) processing a graph (G) containing said sensed plots as N vertices (V) and possible connections between pairs of plots associated with consecutive scans as edges (E);
• ii) defining a cost function of the edges so as to favor geometrically relevant edge patterns for the targets being tracked;
• iii) associating in said cost function each edge with a quantum state encoded by log2 N binary variables;
• (c) using the quantum computing system, implementing a variational quantum circuit for optimizing said cost function using a quantum processing system by determining the ground state of a Hamiltonian encoding the cost function using the Variational Quantum Eigensolver algorithm by converting said Hamiltonian to Pauli strings and measuring expectation values of said Pauli strings;
• (d) using the classical processing system, processing one or more tracks based on a histogram built on statistics provided by the measured expectation value of said Pauli strings.