2008
DOI: 10.26421/qic8.1-2-2
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Quantum algorithm design using dynamic learning

Abstract: We present a dynamic learning paradigm for ``programming'' a general quantum computer. A learning algorithm is used to find the control parameters for a coupled qubit system, such that the system at an initial time evolves to a state in which a given measurement corresponds to the desired operation. This can be thought of as a quantum neural network. We first apply the method to a system of two coupled superconducting quantum interference devices (SQUIDs), and demonstrate learning of both the classical gates X… Show more

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Cited by 22 publications
(63 citation statements)
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“…We use a quantum machine learning paradigm using quantum backpropagation [25] in time [26] to find these parameter functions that produce desired quantum states. In previous work [14], via machine learning, we successfully mapped an entanglement witness of the system's initial state, to a measurement at a final time t f . Here, we wish instead to direct the time evolution while at the same time performing quantum annealing by lowering the temperature and/or reducing the tunneling amplitudes.…”
Section: Machine Learning Of Annealingmentioning
confidence: 99%
See 4 more Smart Citations
“…We use a quantum machine learning paradigm using quantum backpropagation [25] in time [26] to find these parameter functions that produce desired quantum states. In previous work [14], via machine learning, we successfully mapped an entanglement witness of the system's initial state, to a measurement at a final time t f . Here, we wish instead to direct the time evolution while at the same time performing quantum annealing by lowering the temperature and/or reducing the tunneling amplitudes.…”
Section: Machine Learning Of Annealingmentioning
confidence: 99%
“…In addition, the two "parts" of the Hamiltonian commute (as is not usually the case with the interaction representation!) The derivative of the Lagrangian can also be written in terms of our earlier result [14] for zero β as…”
Section: Machine Learning Of Annealingmentioning
confidence: 99%
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