Home » Quantum-enhanced deep learning: applications to optimization and complexity – 2020

Quantum-enhanced deep learning:
applications to optimization and complexity – 2020

General motivation

Even though any practical implementation of a universal quantum computer is hardly to happen in the short run, combining different aspects of algorithm development with quantum engineering nowadays is feasible to accelerate computations. As one practical example, variational quantum algorithms use quantum circuits as variational models, where the quantum computer is needed to prepare a sufficient variety of variational probe states. These states characterized by a polynomial number of parameters are further used to minimize the expectation value of a given cost function via classical optimization. The combination of quantum and classical computation has led to the name hybrid quantum-classical algorithms was coined for variational quantum algorithms.

Remarkably, these algorithms are proposed as a practically viable application of quantum computers with several dozens of qubits and short decoherence times that preclude the use of the more complicated quantum algorithms. The course is intended to provide students with basic skills to program existing quantum processors, including D-Wave’s quantum annealer and IBM’s quantum cloud experience. After completing the course, you are supposed to be familiar with the theory of ground state quantum computation as implemented based on quantum annealing and adiabatic quantum computing, quantum circuits and various quantum algorithms, including quantum-enhanced machine learning.


1 week pre-study exercises

  • Open source quantum toolboxes, e.g. Qiskit
  • Open source machine learning libraries, e.g. Keras

1 week lectures and hands-on at Uppsala University (t.b.a.)

  • Mathematical background
  • Annealing and adiabatic quantum computing
  • Tensor networks
  • Quantum-enhanced machine learning and quantum simulations
  • Homework assignment

1 week project assignment

  • Implement quantum chemistry or machine learning task on a quantum computer


Basic programming skills on Python, matrix algebra


Dmitry Yudin, Department of Physics and Astronomy, Uppsala University
e-mail: dmitry.yudin@physics.uu.se


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