AiThority Interview with Trevor Lanting, Chief Development Officer D-Wave

Trevor Lanting, Chief Development Officer D-Wave shares the distinct advantages of annealing and gate-model quantum computing for various industries, emphasizing their roles in optimization, materials science, and AI. In this interview he talks about the potential for quantum computing to alleviate the computing demands in AI and ML across multiple sectors.

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Please share your journey to becoming Chief Development Officer (CDO) at D-Wave and what inspired your passion for quantum computing.

I have a background in physics, and I have always been interested in technology development. I am trained as an experimental physicist and my graduate work involved building superconducting instrumentation for microwave astronomy.

Through that training, I realized my passion really centered on developing technology and building tools. When D-Wave was recruiting for an experimental physicist in 2008, I jumped at the chance to join the team. Over the last 15 years, I have been involved with many aspects of our technology development, contributing directly to our annealing quantum computing development, our performance research program, and helping lead our software and algorithms teams. I was involved with work that was instrumental in demonstrating quantum entanglement in the fabric of our annealing processors, a major step in validating our technology approach.

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Several months ago, I stepped into a leadership role helping direct our overall research and product development efforts across software and hardware systems. I am incredibly excited about quantum computing. We are building technology that harnesses quantum mechanics to produce fundamentally new computational tools. As the technology rapidly matures, we are seeing a growing set of use cases that span from the acceleration of scientific discovery to optimization of complex business processes, and emerging machine learning applications.

Can you talk about the primary differences between D-Wave’s annealing and gate-model quantum computers, and how do these technologies benefit industries like AI, logistics, and materials sciences?

Annealing and gate-model quantum computing are two of the leading approaches to building practical large-scale quantum computing technology. These approaches offer distinct and complementary advantages for different use cases and applications, and we are developing both technology platforms.  

Annealing systems are uniquely suited for solving optimization problems. These problems, like tour scheduling, resource scheduling, and cargo loading, occur across many industries, like supply-chain logistics and manufacturing, and solving these problems leads to more efficient operations and direct cost savings.

 For materials sciences, gate-model systems have the potential to simulate the behavior of novel molecules and interactions between these molecules, promising to accelerate material discovery and drug design.

 For AI, annealing quantum computing can enhance machine learning algorithms, particularly in feature selection, model optimization, and providing rich quantum distributions that can be directly harnessed in generative AI architectures.  

Annealing quantum computing does have several advantages over current gate model systems: annealing protocols do not require significant pre-processing overheads associated with many gate-based protocols; annealing processor controls are continuously applied making the processors more resilient to errors and noise; and annealing processors are scaling to enterprise-level problem sizes more quickly. These characteristics make annealing quantum computing ideal for addressing today’s real-world challenges across industries.

How do you see the integration of quantum computing with AI and machine learning evolving, and what challenges and opportunities do you foresee?

It’s becoming apparent that the broader AI industry is facing a severe computing crunch. The amount of compute and the related energy costs needed to keep up with an increasing set of use cases is rapidly escalating. The industry should recognize that quantum computing technology might offer real opportunities to allow the industry to meet the growing demand for larger, more performant, and more energy efficient AI and ML architectures and workloads.

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 At the same time, we are in the early part of exploring how best to harness the power of quantum computing for AI. There is currently work in development here at D-Wave on using quantum distributions for designing modern generative AI architectures. This is an emerging field that involves directly using quantum processing unit samples that are not easily generated by classical computers, all of which could potentially improve how generative AI models are built.

 Customers such as TRIUMF, a Canadian physics lab, Honda Innovation Lab and Tohoku University, are already exploring D-Wave technology to address a variety of AI/ML workloads including pre-training optimization and more accurate and efficient model training.

 D-Wave has introduced a hybrid-quantum approach to optimizing feature selection in AI/ML model training and prediction. This approach is designed to help improve models’ accuracy by employing quantum systems to select the most representative dataset characteristics. Our partnership with Zapata AI continues to explore how the combination of quantum computing and generative AI could accelerate the development of new pharmaceuticals.

What are your predictions for the future of quantum computing, particularly in scalability, practical applications, and mainstream adoption across industries?

 For us at D-Wave, the future of quantum computing is firmly anchored in its practical applications. We’re already witnessing real-world impact across various industries, solving problems to directly improve people’s daily lives. Examples include quantum-optimized routes for grocery deliveries and more efficient supply-chain management. Overall, I expect quantum solutions to impact business operations by improving efficiencies in supply chain management, financial modeling, and resource allocation.

 Scalability is an important factor: the underlying quantum computing systems need to be designed for scalability from the beginning, and this a key reason why we focused our initial technology development effort on superconducting quantum annealing systems. As quantum computers become more powerful, growing in qubit count and quality, their ability to tackle larger and more complex problems will also increase.

 I believe quantum computing will play a meaningful role in drug discovery, accelerating the development of new medications and materials, and will be more broadly adopted across industries that face optimization challenges.

How do you think AI advancements will influence the evolution of quantum computing hardware and software solutions?

 We’ve talked about AI advancements making things faster and more scalable, and of course, this will allow for new discoveries. AI tools could also make quantum computing more accessible by automating some of the complex processes involved in quantum computations, problem formulation, solver parameter selection, and adding more user-friendly interfaces. This aligns with our goal at D-Wave, which is to make quantum computing a practical tool for solving real-world problems across various industries.

Could you share your thoughts on where you see AI, machine learning, and other smart technologies heading beyond 2024?

 In the future, I think we will see quantum-enhanced AI models outperform purely classical AI in many domains. Like any new emerging general-purpose technology, if we can put quantum computing, AI, and machine learning technologies in the hands of a broad and diverse set of users as fast as possible, unexpected and powerful use cases will quickly emerge and we will see these technologies embedded into our daily lives. And as domain experts in a wide range of fields adopt these powerful tools, progress on drug design, materials innovation and simulation, business optimization, and scientific discovery will accelerate. 

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