Quantum Matters: Where Quantum Computing Gets Real

D-Wave

Quantum computing has a “reputation.” To many, it sounds like pure science fiction: a mythical machine that can crack every code, outpace every supercomputer, and solve the world’s biggest problems overnight. But the truth is even more exciting. The Quantum Matters podcast reveals how quantum computing is already transforming the world around us — from automotive manufacturing to retail operations. Created by D-Wave, the world’s first commercial quantum company, each episode unpacks big ideas in plain English and shows where quantum is starting to make a difference today. You’ll hear from researchers, academics, and industry leaders who are walking the talk of applying quantum solutions to their most computationally complex problems. Quantum isn’t “someday.” It’s here. It’s scaling. It’s not afraid of its critics — and it’s creating opportunities faster than most people realize. Don’t get left behind. Follow D-Wave’s Quantum Matters to discover how this once-mythical technology is beginning to change lives, transform industries, and rewrite the future, today.

Episodes

  1. 1d ago

    Paving the Path to Fault-Tolerant Gate-Model Computing

    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 Qubits 06:20 - Lessons from the Quantum Computing Industry  23:09 - The Future Impact of Quantum Computing

  2. Jun 16

    Quantum Computing for Computational Advantage

    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 a problem 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 Years 11:49 — Why Quantum Computing Could Revolutionize Energy Efficiency 29:12 — What’s Next for Quantum Computing: New Controls and Capabilities Show Glossary Quantum 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.

Ratings & Reviews

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About

Quantum computing has a “reputation.” To many, it sounds like pure science fiction: a mythical machine that can crack every code, outpace every supercomputer, and solve the world’s biggest problems overnight. But the truth is even more exciting. The Quantum Matters podcast reveals how quantum computing is already transforming the world around us — from automotive manufacturing to retail operations. Created by D-Wave, the world’s first commercial quantum company, each episode unpacks big ideas in plain English and shows where quantum is starting to make a difference today. You’ll hear from researchers, academics, and industry leaders who are walking the talk of applying quantum solutions to their most computationally complex problems. Quantum isn’t “someday.” It’s here. It’s scaling. It’s not afraid of its critics — and it’s creating opportunities faster than most people realize. Don’t get left behind. Follow D-Wave’s Quantum Matters to discover how this once-mythical technology is beginning to change lives, transform industries, and rewrite the future, today.

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