Gates for Adiabatic Quantum Computing

TL;DR

This paper introduces fundamental gates for adiabatic quantum computing, adapting key quantum gates to the Ising model to improve discrete optimization on D-Wave systems.

Contribution

It presents methods to implement and adapt essential quantum gates within the adiabatic framework, specifically tailored for the Ising objective function.

Findings

Gates adapted for the Ising model on D-Wave systems

Enhanced quantum algorithms using these building blocks

Potential improvements in quantum optimization performance

Abstract

The goal of this paper is to introduce building blocks for adiabatic quantum algorithms. Adiabatic quantum computing uses the principle of quantum annealing, which implies that a carefully controlled energy solution is optimal and corresponds to minimizing a discrete function. The input function can be influenced by rewards and penalties to favor a solution that meets restrictions that are imposed by the problem. We show how to accomplish this influence for gates in adiabatic quantum computing, particularly the controlled-NOT gate (CNOT gate) which is fundamental to all quantum gates on two or more qubits. In addition, we adapt the Toffoli gate, the Fredkin gate, and the Hadamard gate to the Ising objective function which is a foundation for discrete optimization on D-Wave System machines. The quantum work in this paper encompasses Boolean operations, some of which are used to construct gates. We think the possible advantages of the building blocks in this paper will enhance quantum algorithms on D-Wave System computers.