Engineering the Cost Function of a Variational Quantum Algorithm for Implementation on Near-Term Devices

Understand

Variational hybrid quantum-classical algorithms are some of the most promising workloads for near-term quantum computers without error correction.

• The aim of these variational algorithms is to guide the quantum system to a target state that minimizes a cost function, by varying certain parameters in a quantum circuit.
• This paper proposes a new approach for engineering cost functions to improve the performance of a certain class of these variational algorithms on today's small qubit systems.
• We apply this approach to a variational algorithm that generates thermofield double states of the transverse field Ising model, which are relevant when studying phase transitions in condensed matter systems.