On the representation of Boolean and real functions as Hamiltonians for quantum computing

TL;DR

This paper presents a method to represent Boolean and real functions as Hamiltonians using sums of Pauli Z operators, providing a toolkit for quantum optimization and algorithm design.

Contribution

It introduces composition rules for constructing Hamiltonians of complex functions from simpler components, facilitating quantum algorithm development.

Findings

Hamiltonians for Boolean functions are linked to Fourier expansions.

Constructing Hamiltonians for real functions is generally computationally easier.

Application to controlled-unitary operators and function value computation in ancilla registers.

Abstract

Mapping functions on bits to Hamiltonians acting on qubits has many applications in quantum computing. In particular, Hamiltonians representing Boolean functions are required for applications of quantum annealing or the quantum approximate optimization algorithm to combinatorial optimization problems. We show how such functions are naturally represented by Hamiltonians given as sums of Pauli $Z$ operators (Ising spin operators) with the terms of the sum corresponding to the function's Fourier expansion. For many classes of functions which are given by a compact description, such as a Boolean formula in conjunctive normal form that gives an instance of the satisfiability problem, it is #P-hard to compute its Hamiltonian representation. On the other hand, no such difficulty exists generally for constructing Hamiltonians representing a real function such as a sum of local Boolean clauses. We give composition rules for explicitly constructing Hamiltonians representing a wide variety of Boolean and real functions by combining Hamiltonians representing simpler clauses as building blocks. We apply our results to the construction of controlled-unitary operators, and to the special case of operators that compute function values in an ancilla qubit register. Finally, we outline several additional applications and extensions of our results. A primary goal of this paper is to provide a $\textit{design toolkit for quantum optimization}$ which may be utilized by experts and practitioners alike in the construction and analysis of new quantum algorithms, and at the same time to demystify the various constructions appearing in the literature.