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This is the first in a series of articles about Quantum Computing and Supply Chains.

Quantum Computing offers tremendous possibilities for supply chains, principally in the area of optimization and simulation. More specifically, quantum computing brings the ability to solve large complex problems that have huge numbers of variables and constraints. This includes optimization of inventory across many facilities, improved route planning, minimizing manufacturing costs, factory and truck scheduling. 

Our current computers, including PCs, cloud servers, other servers, tablets, phones, and even super computers, are based on technology that has been progressively developed since WWII. Despite the incredible improvements in all areas of computing, these machines are now called ‘classical computers’.

Supply chain software running on classical computing has been available for over 30 years. Mathematical algorithms for efficiently solving supply chain optimization problems are over 50 years old. However, there is a limit to the size of the problems we can solve on classical computers, or the problems take too long to run to completion. In other words, supply chain problems are some of the worlds’ most complicated challenges to solve and the speed and breadth needed for these problems is well suited for quantum computing. Similar problems, due to size or complexity, include modeling of quantum properties themselves, or discovering new molecules for better pharmaceuticals or materials. Most of supply chain professionals don’t think of their computing challenges as being on similar level of difficulty as quantum physics, but they are. There is a theoretical threshold where quantum computing is the only way to solve these problems, and this boundary is known as ‘quantum supremacy’. While quantum computing progresses, expect advances in high performance classical computing to continue and the ‘supremacy’ line to shift with developments in both platforms.  

In particular, supply chain problems such as truck routing or scheduling jobs in a complex factory can have hundreds and millions of variables and possible outcomes. As the number of variables and combinations grow, the ability of classical computers, even high-performance super computers, to solve these becomes either impossible, or the number of computations requires run-times of years and even decades. Quantum computing provides the outlet; these new machines can theoretically handle many more variables and run the computations in seconds or minutes. 

The purpose of these blogs is to help supply chain professionals begin to prepare for the quantum world that’s coming. Already very small quantum machines are available to access via cloud providers and there are academic and industry experts around the world developing the necessary software to run these new machines. Production-ready machines aren’t likely to be available for a few more years (progress is not linear), but there has been enough advancement to start to focus on real end-use applications, in this case supply chain.

For now, consider the hardest computational problems you have. For example, finding the best combination of inventory levels to satisfy demand while minimizing costs is often difficult to solve when all the known variables are input. Or, what is the best way to route a fleet of trucks to provide the best customer experience while keeping costs as low as possible. COVID has disrupted most of our demand models that use historical data to calculate future needs; with quantum computers its possible to simulate many many combinations to understand the true volatility in demand. 

Second, start to educate yourself about quantum computing. There are many articles, both in mainstream media and in technical journals, on various hardware, software, use cases, etc

I look forward to sharing more with you and welcome your thoughts.