CEO of D-Wave.
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.
Quantum and AI create an industrial-strength power duo.
How will these two technologies join forces? 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.
The two technologies are complementary. 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.
There are many compelling reasons why leaders need to start thinking now about a quantum computing strategy alongside enterprise AI. What’s more, they can take concrete steps to build their quantum and AI future now.
Quantum computers could help develop more accurate AI models.
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.
For example, in the field of drug discovery, quantum-powered AI models could identify potential new drug candidates by analyzing molecular structures. It could also accelerate the development of new materials by simulating and analyzing their properties at the molecular level—fueling potential breakthroughs in electronics, renewable energy technologies and more sustainable products.
Using quantum computing to train AI models will consume much less power.
AI has made remarkable strides in recent years. However, the technology is limited by the massive amount of energy required to develop and run its algorithms. Estimates from the International Energy Agency indicate that global electricity consumption from AI, data centers and cryptocurrency could double by 2026.
Annealing quantum computing technology has been shown to be more energy-efficient than classical computing for developing advanced AI models. This means quantum computing could help boost AI advancements in areas such as deep learning, natural language processing and computer vision without consuming enormous amounts of energy.
Complex supply chain logistics and manufacturing scenarios can benefit.
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. With sustainability now an added business imperative, quantum plus AI could help manufacturing organizations forecast demand more precisely and align production—minimizing the creation of excess goods and reducing waste through the supply chain.
By using quantum to optimize routing, production and inventory management based on AI-powered forecasting, quantum and AI together could help organizations reduce the need for transportation and storage—reducing costs and carbon emissions.
What steps can leaders take now to bring quantum AI into their enterprise?
1. Incorporate quantum now to maximize AI impact.
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. In addition, leaders should invest in quantum AI now to facilitate rapid development and deployment of applications.
2. Engage teams to identify near-term quantum AI use cases.
We’re starting to see near-term use cases emerging that are well-suited for quantum AI, most notably in the areas of supply chain logistics, manufacturing and life sciences. Leaders should engage their internal teams to identify where quantum AI can support their specific business challenges. Engaging cross-functional teams to identify problems that are suitable for quantum AI will help not only expedite quantum AI’s enterprise impact but also embed a culture of innovation and adaptability.
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.
As author and quantum computing expert Arthur Herman wrote, “What AI can do, AI/quantum will do better.” 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.
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