6533b851fe1ef96bd12a98f9

RESEARCH PRODUCT

Approximate supervised learning of quantum gates via ancillary qubits

Alessandro FerraroLuca InnocentiLeonardo BanchiSougato BoseMauro Paternostro

subject

Theoretical computer sciencePhysics and Astronomy (miscellaneous)Computer scienceSupervised learningQuantum Physicsquantum-computation01 natural sciencesSettore FIS/03 - Fisica Della Materia010305 fluids & plasmasSet (abstract data type)Quantum-informationComputer Science::Emerging TechnologiesQuantum gatemachine learningquantum informationQubit0103 physical sciences/dk/atira/pure/subjectarea/asjc/3100/3101Hardware_ARITHMETICANDLOGICSTRUCTURESQuantum informationquantum-gates010306 general physicsQuantum computer

description

We present strategies for the training of a qubit network aimed at the ancilla-assisted synthesis of multi-qubit gates based on a set of restricted resources. By assuming the availability of only time-independent single and two-qubit interactions, we introduce and describe a supervised learning strategy implemented through momentum-stochastic gradient descent with automatic differentiation methods. We demonstrate the effectiveness of the scheme by discussing the implementation of non-trivial three qubit operations, including a Quantum Fourier Transform (QFT) and a half-adder gate.

yearjournalcountryeditionlanguage
2018-12-01
10.1142/s021974991840004xhttp://hdl.handle.net/10447/533818