The world is abuzz about quantum computing, and rightfully so. By exploiting the power of subatomic phenomena, quantum computing has the potential to solve some of humanity’s greatest challenges—and companies and governments want to be ready to take full advantage of these capabilities.
As a result, organizations are investing heavily in quantum computing. According to Precedence Research, the global quantum computing market size is projected to hit around USD 125 billion by 2030 and is poised to reach a CAGR of 36.89% from 2022 to 2030.
Quantum computing promises to be the solution to today’s most complex problems, and it’s expected to make an especially transformative impact in simulation problems—such as organic chemistry, materials science, and biochemistry—and security.
But optimization is the area where quantum computing is expected to create breakthrough performance first. This is of particular interest to business leaders, in particular, since optimization can solve common business problems—enabling organizations to do more with their limited resources.
Some common quantum optimization use cases include:
Even just a 1% improvement in these areas can translate into millions in annual cost savings for an organization.
Despite large investments and high expectations, quantum computing faces a steep uphill battle. Although it’s understood from a theoretical point of view—and it has been shown to work with working prototypes and machines—quantum computing has yet to scale to an industrial-size machine that can be used. Some of the specific reasons behind this challenge include the following:
As a result, real-world benefits could still be decades away. According to McKinsey & Company: “While quantum computing promises to help businesses solve problems that are beyond the reach and speed of conventional high-performance computers, use cases are largely experimental and hypothetical at this early stage. Indeed, experts are still debating the most foundational topics for the field.”
When it comes to quantum computing for solving optimization problems, the focus has been primarily on quantum annealing, which can only solve “quadratic unconstrained binary optimization” (QUBO) problems.
QUBOs are not always effective in practice, though. In particular, they cannot directly:
Especially in today’s economic climate, organizations are looking for ways to do more with their limited resources. And they can’t wait decades for the promises of quantum optimization to become a reality.
That’s why they’re turning to classical optimization for solving their complex, real-world business problems today—while also building a strong foundation for a quantum future. They’re becoming truly “quantum-ready,” with value to show for it. This involves two key steps:
Step 1: Identify Your Business Use Cases
Whether you’re preparing for a classical optimization project or a quantum optimization one, you will always start here. You start by taking a hard, quantitative look at your business, in order to:
Step 2: Learning to Leverage Advanced Analytics Software
By implementing and using optimization (or any other advanced analytics software, for that matter), your organization will be able to:
By leveraging optimization technology today, you can bring the promises of quantum optimization into your present-day business situations—while preparing for the quantum possibilities of tomorrow.
Gurobi customers are solving extremely complex, real-world business challenges every day. So why wait for quantum, when you can start optimizing now?
Plus, Gurobi customers achieve a 518% return on investment, with payback in under six months. (Forrester Total Economic Impact Report 2022)
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