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SUMMARY:Quantum Generative Adversarial Networks for Learning and Loading R
 andom Distrubitions - Christa Zoufal\, IBM Research/ETH
DTSTART:20191121T141500Z
DTEND:20191121T151500Z
UID:TALK131593@talks.cam.ac.uk
CONTACT:Johannes Bausch
DESCRIPTION:Quantum algorithms have the potential to outperform their clas
 sical counterparts in a variety of tasks. The realization of the advantage
  often requires the ability to load classical data efficiently into quantu
 m states. However\, the best known methods require O(2^n) gates to load an
  exact representation of a generic data structure into an n-qubit state. T
 his scaling can easily predominate the complexity of a quantum algorithm a
 nd\, thereby\, impair potential quantum advantage.\n\nOur work presents a 
 hybrid quantum-classical algorithm for efficient\, approximate quantum sta
 te loading. More precisely\, we use quantum Generative Adversarial Network
 s (qGANs) to facilitate efficient learning and loading of generic probabil
 ity distributions – implicitly given by data samples – into quantum st
 ates. Through the interplay of a quantum channel\, such as a variational q
 uantum circuit\, and a classical neural network\, the qGAN can learn a rep
 resentation of the probability distribution underlying the data samples an
 d load it into a quantum state.\n\nThe loading requires O(poly (n)) gates 
 and can thus enable the use of potentially advantageous quantum algorithms
 \, such as Quantum Amplitude Estimation.\nWe implement the qGAN distributi
 on learning and loading method with Qiskit and test it using a quantum sim
 ulation as well as actual quantum processors provided by the IBM Q Experie
 nce. Furthermore\, we employ quantum simulation to demonstrate the use of 
 the trained quantum channel in a quantum finance application.
LOCATION:MR9\,  Centre for Mathematical Sciences\, Wilberforce Road\, Camb
 ridge
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