Approximate supervised learning of quantum gates via ancillary qubits
2018 ◽
Vol 16
(08)
◽
pp. 1840004
Keyword(s):
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 nontrivial three qubit operations, including a QFT and a half-adder gate.
2019 ◽
Vol 7
(4)
◽
pp. 360-363
Keyword(s):
Keyword(s):
Keyword(s):