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Complex optimization problems exist across every industry, such as vehicle routing, supply chain management, portfolio optimization, power grid management, and many others. Optimization algorithms are also at the core of many machine learning methods. These real-world problems are very valuable to solve in order to reduce costs, accelerate processes, or reduce risk.
Quantum computers might be able help solve complex optimization problems, from combinatorial optimization to partial differential equations. Quantum computers can naturally represent random distributions as quantum states, and therefore have the potential to provide better solutions than today’s classical optimization algorithms.
By viewing problems of transportation and logistics through a quantum lens, we see a profound impact on how goods, services, and people move through cities and around the world.
Many real-world optimization problems remain unsolvable despite the remarkable advancement in both algorithms and computing power over the past decades. In addition, because of the difficulty of simulating the characteristics of complex molecules, the development of new materials with specific properties—like next-gen batteries—poses huge computational challenges as well. And with quantum breakthroughs in material science, lower emissions and better efficiencies begin to come into focus.
Quantum computing, with its ability to solve remarkably complex problems, is expected to answer some of the most challenging questions in the financial services industry. The financial sector is powered by data, with millions of decisions being made every day across all customer segments. Quantum computing is expected to be a strong driver for innovations, ultimately revolutionizing trading, credit scoring, underwriting, risk management and cybersecurity.
Because of quantum computing’s ability to more efficiently explore thousands of scenarios, optimization applications in the energy and utilities sector are ideal since they are complex systems that require compute power beyond what even the most powerful classical computers can currently provide.
In addition to solving complex optimization problems, quantum computers may be able to aid in chemistry and materials development far beyond the capacity of present-day supercomputers. Such simulations could lead to breakthroughs in materials science such as batteries with greater capacity and longer life spans, high temperature superconductors, and new catalysts for converting and optimizing alternative fuel sources.