D-Wave launches new quantum roadmap geared to AI/ML

Analyst reaction to D-Wave Quantum’s announcement today of an extended product development roadmap aimed at helping organizations address a variety of artificial intelligence/machine learning (AI/ML) workloads was decidedly positive, however, one common sentiment was that much needs to happen for quantum computing to experience widespread adoption.

The Palo-Alto, California-based company said it is “strengthening the connection between quantum optimization, AI, and machine learning” with enhancements to its Leap quantum cloud service, a move, it said, “comes at a time when the broader AI industry is confronting a computing crunch.”

The cost of the amount of compute, and the associated energy, needed to satisfy a growing set of use cases, it said, is rapidly escalating. Its new offering, D-Wave added in a release, is designed to leverage annealing quantum computing’s “unique capability in solving optimization problems to help customers discover better, faster, and more energy efficient AI and ML workloads.”

Roadmap focus

According to the release, the new roadmap will focus on three areas:

Quantum distributions for generative AI: Development in this area, the company said, is focused on “designing novel, modern generative AI architectures that use quantum processing unit (QPU) samples from quantum distributions that cannot be generated classically.” They were initially focused on use cases involving molecular discovery.

Restricted Boltzmann Machine (RBM) architectures that leverage D-Wave’s QPU for applications that it said range from “cybersecurity and drug discovery to high-energy physics data analysis, which could potentially lead to reduced energy consumption in training and running AI models.”

GPU (graphics processing unit) integration with Leap: D-Wave said it will incorporate additional GPU resources for the training and support of AI models alongside optimization workloads. In addition, it said, “efforts are underway to further reduce latency between QPUs and  classical computing resources, a critical step in enabling hybrid-quantum technology for AI/ML.”

Potential impact

Bill Wong, research fellow at Info-Tech Research Group, said, “D-Wave’s advancements in quantum computing for AI are intriguing, but it’s still very early to assess the impact and value of quantum computing for real-world AI use cases.”

Today, he said, “most companies are not preparing for or anticipating breakthroughs in AI from the use of quantum computing. This will likely continue to be one of the key challenges for quantum computing, which is to find those AI use cases that can significantly benefit from this accelerated compute platform when traditional (i.e., GPU-based) can address the compute requirements at a much lower cost.”

Possible use cases, said Wong, “may focus on developing quantum-resistant cryptography, where traditional computing platforms cannot address the resources required. While D-Wave is at the cutting edge of research, I, too, am seeking those use cases that can drive the adoption of this unique platform.”

Heather West, quantum computing analyst at IDC, said, “there has always been a thought about how AI and quantum will work synergistically, but at this point in time, it is more about AI influencing quantum.”

A key piece of the announcement, she said, revolves around annealing quantum computing, due to the fact it has been designed specifically to solve optimization problems, and, in order for quantum to “really have an impact, you have to have a larger customer base.”

Asked if today’s announcement by D-Wave puts the quantum discussion out into the mainstream, she replied, “I think that is fair. D-Wave has taken a customer-centric approach to developing their quantum system.”

Many quantum hardware vendors, she said, “talk about their qubits, they are going to talk about the different components of the quantum system, they are going to talk about potential use cases. But D-Wave talks about use cases that are being explored and gaining value now. They are really driving this customer-centric focus of quantum, which differentiates them.”

Gartner VP Analyst Sid Nag, who specializes in scalable computing, said the announcement represents an “alternative to GPUs, although D-Wave is not saying that explicitly.”

He warned, however, that there is “a whole bunch of specialty cloud providers springing up and competing with hyperscalers, and that is an artifact of the trend we are seeing in terms of AI getting bigger and bigger and bigger. I do not know how big it is going to get in terms of the actual growth. At some point the trough of disillusionment [a Gartner term referring to a phase ‘where, after initial hype and inflated expectations, interest begins to wane.’] is going to set in.”

Nag added that, in the case of D-Wave, “they are going after a very special market.”