A Hybrid Quantum-Classical Paradigm to Mitigate Embedding Costs in Quantum Annealing

by   Alastair A. Abbott, et al.

Despite rapid recent progress towards the development of quantum computers capable of providing computational advantages over classical computers, it seems likely that such computers will, initially at least, be required to run in a hybrid quantum-classical regime. This realisation has led to interest in hybrid quantum-classical algorithms allowing, for example, quantum computers to solve large problems despite having very limited numbers of qubits. Here we propose a hybrid paradigm for quantum annealers with the goal of mitigating a different limitation of such devices: the need to embed problem instances within the (often highly restricted) connectivity graph of the annealer. This embedding process can be costly to perform and may destroy any computational speedup. We will show how a raw speedup that is negated by the embedding time can nonetheless be exploited to give a practical speedup to certain computational problems. Our approach is applicable to problems in which embeddings can be used multiple times to solve related problems, and may allow quantum speedups to be more readily exploited. As a proof-of-concept we present an in-depth case study of a problem based on the maximum weight independent set problem. Although we do not observe a quantum speedup experimentally, the advantage of the hybrid approach is robustly verified, showing how a potential quantum speedup may be exploited.


page 1

page 2

page 3

page 4


A Hybrid Quantum-Classical Paradigm to Mitigate Embedding Costs in Quantum Annealing---Abridged Version

Quantum annealing has shown significant potential as an approach to near...

Quantum speedup in stoquastic adiabatic quantum computation

Quantum computation provides exponential speedup for solving certain mat...

Computational speedups using small quantum devices

Suppose we have a small quantum computer with only M qubits. Can such a ...

Coreset Clustering on Small Quantum Computers

Many quantum algorithms for machine learning require access to classical...

A hybrid algorithm framework for small quantum computers with application to finding Hamiltonian cycles

Recent works have shown that quantum computers can polynomially speed up...

Towards Feature Selection for Ranking and Classification Exploiting Quantum Annealers

Feature selection is a common step in many ranking, classification, or p...

Iterative Qubits Management for Quantum Index Searching in a Hybrid System

Recent advances in quantum computing systems attract tremendous attentio...

Please sign up or login with your details

Forgot password? Click here to reset