It's no secret that AI is being adopted and developed at an astonishing rate. According to Stanford University's 2024 AI Index Report, funding for generative AI experienced a significant increase in 2023, jumping to $25.2 billion, marking an eightfold increase from the previous year.
By now, every company (hopefully) has an AI plan in place, but it's time to start thinking about where quantum computing fits into that plan. Companies that can envision how quantum computing and AI could be used together—and invest in both—will be well-positioned to gain a competitive edge by drawing on the technologies' combined advantages while addressing energy demands.
AI excels in automating tasks and streamlining processes, aiding businesses in making data-driven decisions by uncovering patterns, trends and insights. Meanwhile, quantum computing shines in solving complex optimization problems such as supply chain management, manufacturing efficiency, employee scheduling and emissions reduction.
Quantum computing is demonstrating how it could enhance AI by enabling more accurate and more efficient model training and leveraging the predictive capabilities of AI to deliver better-optimized business processes. Soon, quantum computing could supercharge AI for certain use cases in a manner that is sustainable in terms of cost and energy.
As AI continues to evolve, the computational resources required to train and deploy the models are growing exponentially. Quantum computing can mitigate some of the challenges posed by the increasing complexity and size of AI models. Imagine being able to use quantum computing to build more accurate and complete models, helping businesses get more out of AI without being constrained by the limitations of classical computing.
Industries such as logistics and manufacturing deal with complex challenges that involve numerous variables and constraints when considering things like inventory management, warehouse capacity and delivery route planning. AI can make highly accurate sales predictions by analyzing vast amounts of data, considering factors such as historical sales, market trends and consumer behavior.
Quantum computing is not a distant reality but a present opportunity, and leaders should spearhead initiatives that prioritize the exploration and adoption of today's annealing quantum computing alongside efforts to scale AI. Teams can start identifying areas where quantum AI could make an impact today, especially to build more accurate models and more energy-efficient model training.
As quantum hardware scales up and algorithms become more refined, we'll soon live in an era where quantum-powered AI is the norm, offering scalable and energy-efficient solutions to tackle other complex problems like financial modeling and risk assessment or climate modeling and weather prediction.
Quantum computing is on track to take AI to the next level when it comes to both performance and sustainability. It's time for organizations to start thinking about where quantum computing fits into their AI plan before the competition does.
