Quantum Matters: Where Quantum Computing Gets Real

For more than 30 years, Dr. Robert Schoelkopf has been working on one of quantum computing's biggest challenges: how to build a better qubit. His answer is the dual-rail qubit, a first-of-its-kind superconducting qubit that embeds error detection directly at the hardware level.In this episode of Quantum Matters, host Murray Thom sits down with Rob, a pioneer of gate-model quantum computing and now Chief Scientist at D-Wave. Rob traces the evolution of qubit design, from the transmon to the dual-rail, and explains why the first qubit you build isn't necessarily the one that scales, and how a design that can flag its own errors offers a faster, more efficient path to fault-tolerant gate quantum computing.Along the way, Rob shares how gate-model systems differ from annealing quantum computers, his take on quantum hype, and why fault-tolerant gate systems may make their first big impact in scientific discovery.Learn More: https://www.dwavequantum.com/solutions-and-products/systems/gate-model-quantum-computing/Glossary Cat qubit A superconducting qubit that encodes information in special resonator states designed to suppress certain types of errors. One of several qubit designs Dr. Schoelkopf helped develop. Coherence / coherence time How long a qubit maintains its fragile quantum state before noise degrades it. Longer coherence means more operations can be completed before errors accumulate. In the ice sculpture analogy, it's how long the blocks last before melting. Cooper pair box An early superconducting qubit design based on pairs of electrons (Cooper pairs) on a tiny superconducting island. A precursor to the transmon, and part of the story of Dr. Schoelkopf's early work. Dual-rail qubit (DRQ) A first-of-its-kind superconducting cavity-based qubit architecture, invented by Dr. Schoelkopf and colleagues, that embeds error detection directly in the device design. Two cavities encode a quantum bit of information in a single, shared photon. The dominant error mode, photon loss, produces an invalid state that the qubit itself can detect and flag, enabling highly efficient error correction.Entanglement A quantum phenomenon in which two or more qubits become correlated so strongly that the state of one cannot be described independently of the others; measuring or operating on one affects its partners. Entangling gates are the operations that create this connection, and they're a core building block of gate-model computation.Error correction Methods for protecting quantum information so a computation can continue reliably despite errors. Error correction requires redundancy, typically many physical qubits working together to protect each logical qubit, and this overhead is one of the biggest costs in quantum computing. Because the dual-rail qubit detects errors on its own at the hardware level, it is designed to reduce that overhead by a factor of 10. In the ice sculpture analogy, it's refreezing the blocks as you build.Error detection The dual-rail qubit's built-in ability to recognize when it has experienced an error. When the qubit's photon is lost, the result is an invalid state the hardware itself identifies and flags. Conventional qubits fail silently, and errors must be inferred indirectly by measuring many additional qubits; the dual-rail qubit identifies the error itself, at the individual qubit, as it happens. Error awareness What error detection makes possible for programmers: knowing when and where errors occur during a computation and being able to use that information, in real time, within an algorithm. Error awareness turns errors from silent failures into usable data.Fault tolerance The ability of a quantum system to keep computing reliably despite errors, achieved in gate quantum computing through error correction. The key requirement for commercial-scale gate-model applications.Noise Unwanted disturbance from the environment, such as heat, vibration, or stray electromagnetic fields, that corrupts fragile quantum states and causes errors. In the ice sculpture analogy, noise is what melts the blocks.Photon A single particle of light. In the dual-rail qubit, one photon shared between two cavities carries the quantum information. Physical qubit vs. logical qubit A physical qubit is an actual hardware device. A logical qubit is an error-protected unit of information built from many physical qubits working together. Conventional error-correction approaches can require roughly 1,000 physical qubits per logical qubit; D-Wave's dual-rail approach is designed to reduce that to roughly 100. Quantum gate A basic operation applied to one or more qubits, such as a bit flip or an entangling gate. The building blocks of gate-model programs, analogous to logic gates in classical computing. Quantum simulation Using a quantum computer to model quantum-mechanical systems such as molecules and materials, which are hard for classical computers precisely because they are intrinsically quantum. As Dr. Schoelkopf puts it in the episode, it's "fighting quantum with quantum."Shor's algorithm A quantum algorithm, discovered by Peter Shor in 1994, for factoring large numbers exponentially faster than known classical methods. A landmark result that sparked serious interest in building quantum computers, and a turning point in Dr. Schoelkopf's own career. Superconductivity The property of certain materials, when cooled to extremely low temperatures, to conduct electricity with zero resistance. The physical foundation of D-Wave's qubits, both annealing and dual-rail. Superposition A qubit's ability to exist in a combination of 0 and 1 at the same time, rather than one or the other. Part of what gives quantum computers their power. Transmon A widely used type of superconducting qubit developed by Dr. Schoelkopf and colleagues at Yale. It greatly improved qubit stability and reproducibility and became the dominant design across the industry, and the starting point of the arc that led to the dual-rail qubit.  Highlights:02:33 - The Early Days of Superconducting Qubits06:20 - Lessons from the Quantum Computing Industry 23:09 - The Future Impact of Quantum Computing

In this episode, we take you behind the curtain for a close look at D-Wave's full quantum computing technology stack. Host Murray Thom sits down with longtime colleague Dr. Trevor Lanting, Chief Development Officer at D-Wave, to discuss how the hardware, software, and applications all fit together.From cryogenic hardware and processors, to the Leap™ cloud platform and Ocean™ open-source tools, to the real-world applications customers are running now, Murray and Trevor give you the story behind the technology, D-Wave's culture of innovation, key milestones, and where the product roadmap is heading. Trevor breaks down D-Wave's dual-platform strategy across annealing and gate-model systems, why each has its own sweet spot, and how the lessons learned scaling annealing position D-Wave to drive the commercialization of gate-model quantum computing.Learn More: https://www.dwavequantum.com/solutions-and-products/public-sector/Highlights:4:38- Why Build Quantum Computers? 24:24- What Gives D-Wave a Head Start in Quantum?28:06- How Quantum and AI Could Work Together

What does quantum advantage actually mean? How do you prove a quantum computer can outperform the world’s most powerful supercomputers? And why is its energy efficiency arriving at such an important time for the world?In this episode of Quantum Matters, host Murray Thom sits down with Dr. Andrew King, Senior Distinguished Scientist at D-Wave, to discuss the company’s landmark peer-reviewed research demonstrating quantum computational advantage on aproblem relevant to materials discovery.Together, they unpack the result behind the headlines, including a calculation completed in minutes on a quantum processor that could take classical supercomputers nearly a million years. They explore what it took to validate that claim, why energy efficiency is becoming a critical part of the quantum computing story, and how these advances could impact materials science, blockchain, and AI.Join us for an inside look at one of the most significant milestones in quantum computing and what it could mean for the future of computation.Learn more about the Beyond Classical research:https://www.dwavequantum.com/beyond-classical/ Explore the Blockchain research: https://www.dwavequantum.com/blockchain/ Highlights: 03:41 — The Most Exciting Quantum Breakthrough in Years11:49 — Why Quantum Computing Could Revolutionize Energy Efficiency29:12 — What’s Next for Quantum Computing: New Controls and CapabilitiesShow GlossaryQuantum Phase Transition: A change in the state of a quantum system driven by quantum effects rather than changes in temperature.Programmable Quantum Magnet: A controllable quantum system designed to mimic the behavior of magnetic materials for experiments and simulations.Constraint Satisfaction Problem (CSP): A problem where a solution must satisfy a specified set of constraints or rules.Spin Glass: A disordered magnetic system with competing interactions that make finding its lowest-energy state difficult.Polynomial Speedup: An improvement where a quantum algorithm scales more favorably than a classical algorithm as problem size increases.Matrix Product State (MPS): A mathematical representation used to efficiently simulate certain quantum systems on classical computers.Projected Entangled Pair States (PEPS): An advanced tensor-network method used to model higher-dimensional quantum systems.Thermal Bath: The surrounding environment that exchanges heat with a physical system and can influence its behavior.Topological Phase Transition: A phase transition characterized by changes in a system’s global structure rather than conventional ordering.Order by Disorder: A phenomenon where fluctuations create an ordered state from a set of equally possible disordered configurations.Degenerate Ground States: Multiple lowest-energy states of a system that all have exactly the same energy.Hamiltonian: The mathematical description of the total energy and evolution of a physical system.Non-Ising Hamiltonian: A Hamiltonian that includes interactions beyond those found in the standard Ising model of magnetism.Multicolor Annealing: A quantum annealing technique that applies different control schedules to different groups of qubits.State Preparation: The process of initializing a quantum system into a desired starting state before computation or simulation.Doping Parameter: A variable describing how impurities are intentionally added to a material to alter its properties.Hopfield Network: A type of recurrent neural network that stores and retrieves patterns using an energy-based framework.Tensor Network: A mathematical framework used to represent and compute properties of complex quantum systems.

Modern warehouses process many thousands, sometimes millions, of items in constantly changing conditions, where each decision affects the flow of goods in the warehouse, and across the supply chain.In this episode of Quantum Matters, Murray Thom speaks with Gabriel Fernandes of the Wernher von Braun Advanced Research Center about a warehouse challenge faced by an automotive manufacturer. With warehouse capacity limits looming and the challenge of maintaining product flow, Gabriel shares how he turned to quantum-powered optimization to help rethink operations at scale.Using real operational data, Gabriel explores how products could be assigned to gravity flow racks, demonstrating in simulation a 10x reduction in product re-insertions.The result reflects a new way of thinking about coordination across modern supply chains, a practical, eye-opening look at quantum computing’s potential in action.Highlights include:6:30 - Programming a quantum computer for the first time 15:53 - Quantum Solving the Warehouse “Tetris Problem”21:00 -The 90% Cost Reduction Result

Quantum computing can feel abstract… Until you see the kinds of business problems it’s already being applied to. On this episode of Quantum Matters, host Murray Thom is joined by Mayowa Ayodele, Manager, Solutions Architect on D-Wave’s Professional Services team, to explore how organizations are using hybrid-quantum computing approaches to help deliver measurable results today. Drawing on real-world client engagements across industries like logistics, manufacturing, energy, and finance, Ayodele breaks down what makes a problem well- suited for quantum-powered optimization: challenges that are large, complex, and driven by discrete decisions. She also unpacks why classical systems can begin to struggle as constraints multiply, how hybrid solvers can, in some cases, reduce time to solution from hours to seconds, and what it can take to move from proof of concept to production. The conversation also highlights the importance of clean data, strong collaboration with domain experts, and practical pathways for teams that are new to quantum computing to get started with confidence.Highlights:8:00 - The 3 Things You Need to Solve Problems with Quantum13:21 - Good Data Determines Good Results23:36 - What Makes a Quantum Project Successful

Introducing Quantum Matters, a podcast from D-Wave, the world’s first commercialquantum computing company. Every episode will explore where quantum is making atangible difference today. You’ll hear from industry leaders, researchers, and academicswho are applying quantum technology to find answers to their most challengingcomputational problems. You’ll also learn about real-world case studies to help you cutthrough the hype, separate fact from fiction, and develop an informed position on whatthis incredible technology could mean for you today.Because quantum isn’t coming someday—it’s here, it’s scaling, and it’s creatingopportunities and delivering real ROI right now. So whether you’re a quantum skeptic,enthusiast, or undecided, keep an eye out for Quantum Matters from D-Wave, comingsoon to YouTube, Spotify, Apple, and everywhere you get your podcasts.