{"id":3503,"date":"2024-08-20T17:18:15","date_gmt":"2024-08-20T17:18:15","guid":{"rendered":"https:\/\/towardsdatascience.com\/solving-a-constrained-project-scheduling-problem-with-quantum-annealing-d0640e657a3b\/"},"modified":"2025-03-05T03:38:22","modified_gmt":"2025-03-05T08:38:22","slug":"solving-a-constrained-project-scheduling-problem-with-quantum-annealing-d0640e657a3b","status":"publish","type":"post","link":"https:\/\/towardsdatascience.com\/solving-a-constrained-project-scheduling-problem-with-quantum-annealing-d0640e657a3b\/","title":{"rendered":"Solving a Constrained Project Scheduling Problem with Quantum Annealing"},"content":{"rendered":"
\"Why
Why did the dog fail quantum mechanics class? He couldn’t grasp the concept of super-paws-ition. Quantum superposition is the principle where a quantum system can exist in multiple states simultaneously until it is measured, at which point it collapses into one of the possible states. (Image generated by DALLE-3)<\/figcaption><\/figure>\n

I’m really excited to share this article with you because it’s closely tied to my current research: optimizing project schedules with quantum computing. My passion for this topic comes from my background as a project manager and my ongoing research into how quantum computing can help solving complex optimization problems. During my PhD, I specifically looked into how today’s quantum technology could be used to tackle complex scheduling challenges, that are specific for the field of project management.<\/p>\n

We’re living in an incredible era where quantum computers are no longer just a concept – they’re real and being used. Imagine what Richard Feynman would say about them! However, these machines aren’t fully ready yet. They’re often referred to as NISQ (Noisy Intermediate-Scale Quantum) devices, a term coined by quantum computing pioneer John Preskill. These machines are still considered early versions of the more advanced quantum computers we hope to see in the future.<\/p>\n

Currently, they’re small, with only a few qubits (quantum bits of information) available. For comparison, it’s not uncommon today to have a laptop with 16GB of RAM, which equates to around 128 billion bits of information, that we can work with. In contrast, the largest quantum computers have fewer than 6,000 qubits. Moreover, qubits are highly sensitive to noise, and the limited number of qubits makes it challenging to correct errors and perform complex calculations.<\/p>\n

Despite these challenges, the quantum computers we have today are powerful enough to test out algorithms and see their potential for solving tough problems. In broad terms there are two main types of quantum computers: universal quantum machines, like the ones from IBM and Google, which work on a circuit-gate model of computation, and quantum annealers, which use adiabatic evolution principles (Technically speaking there is a third type of quantum computer, which are measurement based quantum machines, but these ones are still in early development). In quantum computing, "universal" means that these machines can, in theory, run any quantum algorithm by implementing any quantum mechanical gate (including classical gates). While quantum annealers might not be universal, they’re still super promising for optimization tasks.<\/p>\n

In this article, I’ll walk you through how you can use a quantum annealer provided by a company called D-Wave<\/a> to optimize project schedules, this article is based on one of my recently published research papers [1]. If you’re interested in a deeper dive, I invite you to read my full research article, which you can access using the link down below.<\/p>\n

Solving the resource constrained project scheduling problem with quantum annealing – Scientific…<\/strong><\/a><\/p><\/blockquote>\n

Before we get into the coding, I’ll give you a quick intro to quantum annealing and how it’s used in optimization. Let’s get started!<\/p>\n

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