He is a Cambridge and ETH Zurich graduate. Over the course of his career, Michał completed internships in Quantum AI groups at CERN and Los Alamos National Laboratory in the United States. In his work, he aims to connect the worlds of quantum computing and business.
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Quantum annealing and quantum inspired optimisation (QIO) are two of the most promising technologies for generating business value with quantum computing right now. Their potential comes from the fact that they’re extremely well suited to solve several pressing Industry problems.
Quantum annealing and QIO can be used to support logistics. The problem of dealing with the costs and challenges of moving things from one place to another, whether it’s a warehouse or a delivery centre, can become very complex for large organisations. These technologies can also be used to improve scheduling. They can support the decision-making process of what things need to be done before others and in what order, which applies, for example, to employees and resources. Finally, quantum annealing and QIO can be used to deal with the problem of feature selection, which is a crucial problem in data science. It’s deciding which data features are important and which features aren’t for further processing and informed decision making based on machine learning and artificial intelligence algorithms.
In optimisation problems, the goal is to find the best solution from many possible ones. Because these kinds of problems are ubiquitous, you will inevitably deal with them in areas such as scheduling, logistics, or feature selection. The high processing power and unique capabilities of quantum devices make these three areas perfect use cases for quantum computing.
Logistics
In many industries, but especially in retail, having a well-oiled supply chain is a vital part of maintaining a competitive edge. Whether it’s an e-commerce business that needs to get a package to a customer as quickly as possible or a traditional brick-and-mortar retail outlet that needs to optimise the flow of goods through its distribution centres, supply chains are at the core of most businesses.
Achieving the best possible logistics is critical to the success of these supply chains, but it’s a complex problem that cannot be solved with a “one-size-fits-all” approach. Especially when it comes to optimising the flow of goods between distribution centres. That’s because the sheer number of possible scenarios is simply too large for classical computers to handle. So how can businesses improve their logistics and streamline their supply chains in the face of this complexity? The answer may lie in quantum computing.
Imagine that you’re in charge of operating a warehouse and improving its efficiency. Quantum computing solutions can help you increase your terminal capacity and achieve faster truck turn times. Moreover, they allow for continuous feed analysis of container availability forecasts, creating a beneficial feedback loop for making further enhancements.
Scheduling
Scheduling is an extremely broad topic that’s relevant to multiple business areas. It covers different industries, such as finance, healthcare, manufacturing, and others. Particularly in the manufacturing sector, scheduling is a crucial part of a company's success. From planning and forecasting, through scheduling and dispatching to production allocation, scheduling plays a vital role in every aspect of a manufacturing facility's operation.
Technically speaking, scheduling is the selection of items (job, event, meeting) for a specific time, so that the requirements of all concerned parties are met. In a business context, scheduling is essential to ensure that the most important tasks are completed on time.
This activity supports businesses in allocating their workforce to meet the demands of their customers through the provision of appropriate staff, equipment, and materials. When an enterprise scales up, these problems quickly grow in size and complexity, and this is where quantum can provide invaluable support.
For example, let’s look at daily operations of a medium to large size clinic or hospital that is continuously faced with the challenge of scheduling shifts for nurses and doctors. An optimal schedule has to consider a sizeable number of factors. Including the demand for employees throughout the day, their availability, minimum and maximum working hours, and others. These constraints are directly transferrable to the quantum optimisation domain and, as a result, you can get a flexible model that allows for considering interconnections and complex dependencies within scheduling.
Feature Selection
Feature selection is one of the most important problems in data-driven machine learning today. The goal of this process is to identify a subset of relevant features from a large set of the available ones. Personalised product recommendations can be a simple example. To determine what item to recommend to a potential customer, the most relevant factors would be previous purchase and search history, while data such as their location or age might play a much less pivotal role.
Narrowing the selection down to relevant features helps improve the prediction accuracy and reduce computational costs. A feature is relevant when it’s deeply interconnected with the goal you want to achieve and it can do so without a bias. The feature selection methods based on classical computing typically suffer from high complexity, which makes it difficult for the machines to handle.
Quantum-based methods can provide a very efficient mechanism to solve this problem with an improved trade-off between the computation and the accuracy. This technique, when applied to a recommender system for e-commerce business, allowed for 70% reduction in the input size of the model without any decrease in the quality of produced recommendations. As a result, the accuracy remained at the same level, while the speed of the calculations has improved significantly.
Summary
Quantum machines are the most promising emerging technology in computing and they can help businesses solve complex problems in areas such as scheduling, logistics, or feature selection. The number of quantum computing use cases continues to grow as this technology advances, and it’s gradually becoming a welcome addition to any business.
As Objectivity, we aim to promote the use of quantum-based technology and educate businesses about its availability, effectiveness and feasibility. We’re proud to be a part of a global outreach effort, and we’ll continue to assist our clients in achieving competitive advantage with this cutting-edge technology.
He is a Cambridge and ETH Zurich graduate. Over the course of his career, Michał completed internships in Quantum AI groups at CERN and Los Alamos National Laboratory in the United States. In his work, he aims to connect the worlds of quantum computing and business.
