Classification using quantum neural networks

A method of classification performed using a quantum computer, the method comprising:

• applying one or more layers of parameterised unitary operators to transform a plurality of qubits from an input state to an output state, wherein
• the plurality of qubits comprise one or more data qubits that represent the input state to be classified and one or more readout qubits;
• and
• each layer of parameterised unitary operators comprises a plurality of two-qubit quantum gates, each of the two-qubit quantum gates acting between a readout qubit and a different one of the data qubits, wherein parameters of the one or more parameterised unitary operators comprise trained parameters;
• determining a readout state from measurements on the one or more readout qubits in the output state;
• and
• classifying the input state in dependence on the readout state.

A quantum computing system comprising:

• a plurality of qubits;
• and
• one or more parameterised quantum gates,
• wherein the system is configured to:
• prepare the plurality of qubits in an input state with a known classification, said plurality of qubits comprising one or more data qubits that represent a state to be classified and one or more readout qubits;
• apply one or more layers of the one or more parameterised quantum gates to the plurality of qubits to transform the input state to an output state, wherein each layer of parameterised quantum gates comprises a plurality of two-qubit quantum gates, each of the two-qubit quantum gates acting between a readout qubit and a different one of the data qubits;
• determine, using a readout state of the one or more readout qubits in the output state, a predicted classification of the input state;
• compare the predicted classification with the known classification;
• and
• update, during a training process, one or more trained parameters of the parameterised quantum gates in dependence on the comparison of the predicted classification with the known classification.

A quantum computing system comprising:

• a plurality of qubits;
• and
• one or more parameterised quantum gates,
• wherein the system is configured to:
• apply the one or more parameterised quantum gates to transform the plurality of qubits from an unclassified input state to an classifying output state, trained parameters of the one or more parameterised quantum gates determined using a classifier trained using a method comprising:
• preparing a plurality of qubits in an input state with a known classification, said plurality of qubits comprising one or more data qubits that represent a state to be classified and one or more readout qubits;
• applying one or more layers of the one or more parameterised quantum gates to the plurality of qubits to transform the input state to an output state, wherein each layer of parameterised quantum gates comprises a plurality of two-qubit quantum gates, each of the two-qubit quantum gates acting between a readout qubit and a different one of the data qubits;
• determining, using a readout state of the one or more readout qubits in the output state, a predicted classification of the input state;
• comparing the predicted classification with the known classification;
• and
• updating, during a training process, one or more trained parameters of the parameterised quantum gates in dependence on the comparison of the predicted classification with the known classification;
• determine a readout state from measurements on one or more readout qubits in the classifying output state;
• and
• classify the unclassified input state in dependence on the readout state.