Evaluating Ising Processing Units with Integer Programming

Abstract

The recent emergence of novel computational devices, such as adiabatic quantum computers, CMOS annealers, and optical parametric oscillators, present new opportunities for hybrid-optimization algorithms that are hardware accelerated by these devices. In this work, we propose the idea of an Ising processing unit as a computational abstraction for reasoning about these emerging devices. The challenges involved in using and benchmarking these devices are presented and commercial mixed integer programming solvers are proposed as a valuable tool for the validation of these disparate hardware platforms. The proposed validation methodology is demonstrated on a D-Wave 2X adiabatic quantum computer, one example of an Ising processing unit. The computational results demonstrate that the D-Wave hardware consistently produces high-quality solutions and suggests that as IPU technology matures it could become a valuable co-processor in hybrid-optimization algorithms.

Quantum computing based hybrid solution strategies for large-scale discrete-continuous optimization problems

“…Constraints (10) - (12) and (17) form the constraints of the hybrid model described below. These timing and assignment constraints together form a new set of constraints (19) for the relaxed MILP model. Constraints…”

“…A number of technological limitations face commercially available quantum computers, such as relatively small number of qubits with limited connectivity, and lack of quantum memory. Therefore, harnessing the complementary strengths of classical and quantum computers to solve complex large-scale optimization problems has become the main strategy for near-term and mid-term solution [18,19].There are several research challenges towards developing hybrid QC-based solution strategies for large-scale mixed-integer optimization problems. The first challenge is to develop a hybrid algorithmic framework that leverages both QC and classical computers, by integrating exact solution techniques with QC-based solution techniques.…”

“…A number of technological limitations face commercially available quantum computers, such as relatively small number of qubits with limited connectivity, and lack of quantum memory. Therefore, harnessing the complementary strengths of classical and quantum computers to solve complex large-scale optimization problems has become the main strategy for near-term and mid-term solution [18,19].…”

Unified Clustering and Outlier Detection on Specialized Hardware

“…In recent years, we have seen the emergence of specialized computational platforms, e.g., adiabatic quantum computers, CMOS annealers, memristive circuits, and optical parametric oscillators, that use QUBO models (or, equivalently, Ising models) as their core mathematical abstraction (see [4] for a detailed description). Consequently, there is a growing interest in formulating and evaluating QUBO models for key computational problems.…”

Ising-Based Consensus Clustering on Specialized Hardware

“…The increasingly challenging task of scaling the traditional Central Processing Unit (CPU) has lead to the exploration of new computational platforms such as quantum computers, CMOS annealers, neuromorphic computers, and so on (see [3] for a detailed exposition). Although their physical implementations differ significantly, adiabatic quantum computers, CMOS annealers, memristive circuits, and optical parametric oscillators all share Ising models as their core mathematical abstraction [3]. This has lead to a growing interest in the formulation of computational problems as Ising models and in the empirical evaluation of these models on such novel computational platforms.…”