Effective Embedding of Integer Linear Inequalities for Variational Quantum Algorithms

Understand

In variational quantum algorithms, constraints are usually added to the problem objective via penalty terms.

• For linear inequality constraints, this procedure requires additional slack qubits.
• Those extra qubits tend to blow up the search space and complicate the parameter landscapes to be navigated by the classical optimizers.
• In this work, we explore approaches to model linear inequalities for quantum algorithms without these drawbacks.