Equality-Constrained Quadratic Programming with Quantum Computing

Xiaojin Zheng, Xiaoling Sun, Duan Li +1 more

We investigate in this paper the Lagrangian duality properties of linear equality constrained binary quadratic programming. We derive an underestimation of the duality gap between the primal problem and its Lagrangian dual or SDP relaxation, using the distance from the set of binary integer points to certain affine subspace, while the computation of this distance can be achieved by the cell enumeration of hyperplane arrangement. Alternative Lagrangian dual schemes via the exact penalty and the squared norm constraint reformulations are also discussed.

Kyle E. C. Booth, Bryan O’Gorman, Jeffrey Marshall +2 more

Sergio Boixo, Rolando D. Somma

Pooya Ronagh, Brad Woods, Ehsan Iranmanesh

Quantum adiabatic evolution is perceived as useful for binary quadratic programming problems that are a priori unconstrained. For constrained problems, it is a common practice to relax linear equality constraints as penalty terms in the objective function. However, there has not yet been proposed a method for efficiently dealing with inequality constraints using the quantum adiabatic approach. In this paper, we give a method for solving the Lagrangian dual of a binary quadratic programming (BQP) problem in the presence of inequality constraints and employ this procedure within a branch-and-bound framework for constrained BQP (CBQP) problems.

Pooya Ronagh

Kishor Bharti

We propose a hybrid quantum-classical algorithm for approximating the ground state and ground state energy of a Hamiltonian. Once the Ansatz has been decided, the quantum part of the algorithm involves the calculation of two overlap matrices. The output from the quantum part of the algorithm is utilized as input for the classical computer. The classical part of the algorithm is a quadratically constrained quadratic program with a single quadratic equality constraint. Unlike the variational quantum eigensolver algorithm, our algorithm does not have any classical-quantum feedback loop. Using convex relaxation techniques, we provide an efficiently computable lower bound to the classical optimization program. Furthermore, using results from Bar-On et al. (Journal of Optimization Theory and Applications, 82(2):379--386, 1994), we provide a sufficient condition for a local minimum to be a global minimum. A solver can use such a condition as a stopping criterion.