Modernizing Quantum Annealing II: Genetic algorithms with the Inference Primitive Formalism

TL;DR

This paper introduces the 'inference primitive' formalism for quantum annealing, enabling advanced control protocols and the integration of genetic algorithms to enhance the performance and versatility of quantum annealers.

Contribution

The paper develops a formalism for algorithmic design in quantum annealers, allowing for flexible control structures and the incorporation of genetic algorithms, advancing the field's capabilities.

Findings

Formalism enables control of initial conditions and annealing schedules.

Compatibility with non-stoquastic drivers and belief propagation.

Representation of genetic algorithms within quantum annealing.

Abstract

Quantum annealing allows for quantum fluctuations to be used used to assist in finding the solution to some of the worlds most challenging computational problems. Recently, this field has attracted much interest because of the construction of large-scale flux-qubit based quantum annealing devices. There has been recent work on [Chancellor NJP 19(2):023024, 2017] how the control protocols of these devices can be modified so that individual annealer calls on real devices can take initial conditions. Development is being undertaken to implement such protocols in the quantum annealing devices designed by D-Wave Systems Inc. and these features will be available to customers soon. In this paper, I develop a formalism for algorithmic design in quantum annealers, which I call the `inference primitive' formalism. This formalism allows for a natural description of calls to quantum annealers with a general control structure. This more generalized control structure includes not only the ability to include initial conditions in an annealer run, but also to control the annealing schedules of qubits or clusters of qubits independently, thereby representing relative certainty values of different parts of a candidate solution. I discuss the compatability of such controls with a wide variety of other current efforts to improve the performance of annealers, such as non-stoquatic drivers, synchronizing freeze times for the qubits, and belief propagation techniques. To demonstrate the power of the formalism I present here, I discuss how this new formalism can be used to represent annealer implementations of genetic algorithms, and can represent the addition of genetic components to currently used algorithms. The new tools I develop will allow a more complete understanding of the algorithmic space available to quantum annealers, and thereby make the field more competitive.