In this TechVoices conversation, D-Wave Vice President Murray Thom demystifies quantum computing and explains why elements of this technology are already practical today. He contrasts annealing and gate-model approaches, shows how superconducting quantum systems are tackling high-dimensional optimization, and points to production deployments in workforce, manufacturing, and telecom.
Thom also outlines a hybrid “quantum-as-a-service” model with enterprise-grade reliability, shares adoption signals from CIOs, and maps how quantum complements artificial intelligence—from better decisions off AI forecasts to accelerating model training.
Core Takeaways
Why quantum matters now—and where it’s working
- Quantum in plain English: Think of it as energy-efficient computing for very hard problems—especially combinatorial optimization—where the “speed limit” of exploring solutions can be higher than with classical machines.
- Two models, two strengths: Annealing (a type of quantum computing) helps jump between candidate solutions quickly for scheduling, routing, and logistics; gate-model (also a type of quantum computing) aims to store and manipulate far more information for simulations (e.g., molecules). D-Wave’s systems use superconducting circuits you can literally see on thumbnail-sized chips.
- Real-world impact today: Patterson Food Group cut schedule-creation time for delivery fleets by ~80% and expects ~50,000 hours saved annually in store staffing; Ford Otosan scheduled ~1,000 vehicles in ~5 minutes instead of ~30; NTT DOCOMO reduced peak paging signals ~15% across ~250,000 base stations.
- Hybrid is the path—and it’s enterprise-ready: Quantum is delivered via APIs alongside classical solvers (QCaaS) with sub-second responses, ~99.9% cloud availability, and SOC 2 compliance; a recent survey shows ~80% of CIOs are planning for quantum use cases, often tied to AI-driven forecasting and optimization.
Key Quotes
Quantum, in everyday terms
“In practical terms, quantum computing is energy-efficient computing for hard problems. We use quantum effects to accelerate the ‘search’ through huge spaces of possibilities so businesses can move from a bad starting plan to a much better one faster—whether that’s scheduling, routing, or other complex decisions with many interdependencies.”
“There are multiple ways to treat quantum mechanics as a computing resource. With annealing, the machine moves efficiently between solutions. With gate-model systems, quantum effects let you represent and manipulate far more information, which is vital for things like molecular simulation. Our approach today uses superconducting circuits—loops where current can flow one way, the other way, or be in a quantum mixture—printed on chips about the size of your thumbnail.”
Why real deployments are happening now
“A lot of industry noise makes quantum sound ‘years away,’ but that’s because many players focus only on gate-model systems for chemistry or fluid dynamics. Those are incredibly important—and still several years out for large-scale commercial impact. Annealing, by contrast, is already driving results in production for optimization problems, which is why our customers are using quantum today.”
“We’ve seen head-to-head comparisons where a classical supercomputer would need as much electricity as the world uses in a year to perform a certain magnetic-materials calculation—while our quantum system did it for under a dollar of electricity. Pair that with real customer outcomes—like grocery delivery and in-store staffing optimization, or automotive line scheduling—and you see why the conversation has shifted from ‘if’ to ‘where first.’”
The power of hybrid architecture
“Quantum won’t replace classical—period. The winning pattern is hybrid. Let classical algorithms do what they do best, and call out to the quantum computer when you need to make big coordinated moves in the solution space. That’s why we deliver quantum through an API and a platform that orchestrates classical and quantum resources together.”
“From an enterprise lens, you need reliability and governance. Our cloud service runs with sub-second responses, ~99.9% availability, and SOC 2 compliance. That means teams can plug quantum into existing workflows and CI/CD just like any other high-availability service—no reinvention required.”
Quantum + AI: better decisions, faster learning
“AI and quantum are complementary. AI can predict or recommend—say, a promo strategy across products and regions—but turning that into the best set of coordinated business decisions under constraints is a classic high-dimensional optimization problem. That’s where quantum shines, effectively ‘polishing’ AI’s recommendations into executable, higher-quality plans.”
“We’re also seeing early work where quantum can make machine-learning training more efficient. Pharma teams have used quantum-hybrid methods to surface a higher share of valid drug candidates, and researchers at large physics labs are exploring quantum-assisted learning for particle detection with big gains in energy efficiency. AI drives the insight; quantum drives the decision and, increasingly, the learning loop.”
