Method and apparatus for machine learning using a quantum system

A method for machine learning using a quantum system, the method comprising:

• providing a quantum system comprising a set of primary qubits and a set of ancillary qubits,
• wherein a neural network is associated with the quantum system, the neural network including:
• a first set of nodes;
• a second set of nodes;
• a plurality of connections between the first set of nodes and the second set of nodes, each connection having a weight;
• a training data set comprising a plurality of configurations for the first set of nodes,
• wherein each connection of the plurality of connections is both associated with a primary qubit and with an ancillary qubit, the method further comprising:
• initializing the quantum system in an initial quantum state;
• for each configuration in a set sampled from the training data set,
• encoding the configuration in input qubits that form a subset of the set of primary qubits;
• performing a plurality of first rounds of operations, wherein each first round comprises:
• (a1) applying a sequence of unitary operators selected from a first family of unitary operators;
• and
• (a2) performing a measurement of at least a portion of the quantum system;
• and
• performing a plurality of second rounds of operations, wherein each second round comprises:
• (b1) applying a sequence of unitary operators selected from a second family of unitary operators, wherein the second family of unitary operators is different from the first family of unitary operators;
• and
• (b2) performing a measurement of at least a portion of the quantum system;
• the method further comprising:
• performing a measurement of one or more ancillary qubits;
• and
• based at least on the measurement of the one or more ancillary qubits, determining at least one weight of a connection of the plurality of connections.

An apparatus for performing machine learning using a quantum system, the apparatus comprising:

• a quantum system comprising a set of primary qubits and a set of ancillary qubits,
• wherein a neural network is associated with the quantum system, the neural network including:
• a first set of nodes;
• a second set of nodes;
• a plurality of connections between the first set of nodes and the second set of nodes, each connection having a weight;
• and
• a training data set comprising a plurality of configurations for the first set of nodes,
• wherein each connection of the plurality of connections is both associated with a primary qubit and with an ancillary qubit;
• a quantum processing device for evolving the quantum system according to one or more short-range unitary operators;
• an encoding device for interacting with input qubits forming a subset of the set of primary qubits;
• and
• a measurement device for measuring at least a portion of the quantum system,
• wherein the apparatus is configured for:
• initializing the quantum system in an initial quantum state;
• for each configuration of a number of configurations taken from the training data set,
• encoding, by the encoding device, the configuration in the input qubits by interacting with the input qubits;
• and
• performing N rounds of operations, wherein each round comprises:
• applying, by the quantum processing device, a sequence of unitary operators acting on at least a portion of the quantum system;
• and
• performing, by the measurement device, a measurement of at least a portion of the quantum system;
• wherein the apparatus is further configured for:
• performing, by the measurement device, a measurement of one or more ancillary qubits;
• and
• determining at least one weight of a connection of the plurality of connections based at least on the measurement of the one or more ancillary qubits.