Improving Variational Quantum Optimization using CVaR

Understand

Hybrid quantum/classical variational algorithms can be implemented on noisy intermediate-scale quantum computers and can be used to find solutions for combinatorial optimization problems.

• Approaches discussed in the literature minimize the expectation of the problem Hamiltonian for a parameterized trial quantum state.
• The expectation is estimated as the sample mean of a set of measurement outcomes, while the parameters of the trial state are optimized classically.
• This procedure is fully justified for quantum mechanical observables such as molecular energies.