**Editorial Summary :**

This article aims to overview quantum computing and explore quantum annealer, a specialised machine for solving combinatorial optimisation problems . We go through the basics of QC and two types of computing systems . Then, we focus on problem-solving practices with an example of portfolio optimisation using Python programming . In October 2019, **Google** announced that they achieved quantum supremacy, i.e., their QC machine with quantum processing unit (QPU) outperformed the state-of-the-art “classical” supercomputer by 158 million times . A miracle is realised by a quantum bit (qubit) that can represent the dual state of 0 and 1 simultaneously, while a classic binary bit can only take either 0 or 1 at a time . A quantum takes advantage of its wave nature so that the wave interference increases the probability of the desired state and decreases the other to reach a correct solution effectively all at once . The concept of QC dates to the 1980s, but it was in the early 2010s when more and more people talked about it being the next big thing . We can classify quantum computers into “gate model” and “quantum annealing” Quantum annealer is relatively noise-tolerant compared to the gate model [15]. At the time of this writing, the most considerable number of qubits on a quantum processing unit (QPU) is at least five thousand provided by **D-Wave**’s Advantage solver . Simulated annealing (SA) is a probabilistic technique whose name and idea are derived from annealed in material science . SA is a meta-heuristic in which algorithms approximate the global minimum or maximum of a function . It uses a parameter called temperature that is initialised with a high value and decreases as the algorithm proceeds . Quantum annealers require an objective function to be defined in the Ising model or Quadratic Unconstrained Binary Optimisation (QUBO) QuBO is a minimisation objective function expressed as follows: where x_i and x_j {i,j =0,1,…,N} takes 0 or 1 . In this article, we would like to code and solve a simple combinatorial optimisation problem in Python and the real Quantum Annealer by **D-Wave** . This article explores the current state of quantum computing and then shifted our focus to quantum annealing . We coded a simplified portfolio optimisation problem in Python to solve the problem using **D-Wave** Quantum Annealers: Advantage_system4.1 and hybrid_binary_quadratic_model_version2.1 . We also saw helpful resources that support researchers and developers in building an application powered by quantum technologies . It would be great if this article motivated some readers to know more about the technologies . A. Mcgeoch and **P. Farré**, “Advantage Processor Overview TECHNICAL REPORT” “Problem Formulation Guide WHITEPAPER” **D-Wave** Systems Inc., 2022, . Available: https://arxiv.org/abs/1710.11056[16] “Annealing (materials science),” **Wikipedia**, https://en.wikwikimedia/org/wiki/Annealing_(materials_science) “Traffic Flow Optimization Using a Quantum Annealer”

**Key Highlights :**

**This article aims to overview quantum computing and explore quantum annealer .****We go through the basic concepts behind quantum computing .****A miracle is realised by a quantum bit (qubit) that can represent the dual state of 0 and 1 simultaneously .****A classic binary bit can only take either 0 or 1 at a time .****This phenomenon is called superposition .****Quantum annealer is relatively noise-tolerant compared to the gate model .****Simulated annealing (SA) is a probabilistic technique whose name and idea are derived from annealed in material science .****We would like to code and solve a simple combinatorial optimisation problem in Python and the real Quantum Annealer by D-Wave .****The notebook includes sample codes to solve the problem using D-Wave Quantum Annealers: Advantage_system4.1 and hybrid_binary_quadratic_model_version2.1 .****The author of this article is credited with the work of Towards Data Science .****We do not endorse each author’s contribution .****We offer an overview of the work done by D-Wave Systems Inc.**

The editorial is based on the content sourced from towardsdatascience.com