为量子计算加速做准备

By 2030, quantum machines will be capable ofperforming analytics that can create competitiveadvantage. Will companies be ready? By Laurent-Pierre Baculard, Velu Sinha, Eric Sheng, and PascalGautheron At a Glance. Quantum computing will outperform classical systems on complexproblems by 2029, with early advantages in healthcare, financial services,logistics, and energy.. Unlike generative AI, quantum capabilities take three to four years to build.Companies that wait may find themselves structurally behind before thetechnology stabilizes.. CEOs should set strategic posture now, launch targeted pilots over thenext three years, and industrialize what works. Readiness matters morethan hardware bets. For years, boards have held quantum at a familiar distance: eventuallyimportant, potentially transformative, and clearly disruptive but still “farenough away” to leave to research labs. That assumption is now much harderto defend. The technology is moving toward fault-tolerant systems by 2028 to 2029, withIBM’s roadmap targeting 200 logical qubits in 2029. At that threshold,quantum machines will begin to outperform classical systems on somehigh‑complexity optimization and simulation problems. For hardware players,this will mark a major step toward fault‑tolerant quantum computing; forenterprises, it will be the point at which quantum can start to generate acomputational competitive advantage. Industries where differentiation is rooted in rapid simulation models oroptimization of scenarios will be thefirst to realize this advantage, such asdesigning molecules and treatments (life sciences, healthcare), orchestratingglobal logistics networks, managingfinancial risk (banking and insurance),optimizing battery chemistry (chemicals), or modeling dynamic systems inaerospace, energy, manufacturing, and utilities. In these domains, smartercomputation directly translates into faster innovation, lower costs, higherresilience, and improved sustainability. But to realize the gains, companies need to be ready to master quantumcomputing (QC), adding it to other capabilities in their analytics arsenal alongwith machine learning and artificial intelligence. Move too slowly, and theyrisk slipping into a widening gap behind competitors that develop thisadvantage faster. Early movers that embed quantum into their analytics will be able to reset costbases, speed, and quality in ways that late adopters cannot easily match.Because building quantum capabilities takes three to four years, followers mayfind themselves structurally behind by the time the technology stabilizes. CEOs and executives shouldn’t still be asking whether quantum will matter,but rather how to prepare an adaptive three‑year roadmap. Once thetechnology stabilizes, companies need to be ready to turn quantum potentialinto business advantage. Why this second wave of analytics matters Quantum computing is one of four major quantum technology domains,alongside sensors, cryptography, and communications. Unlike AI and classicalanalytics, it is not only about processing more data but also about searchingthrough vast numbers of possible combinations to solve optimization problems and simulating complex systems that conventional tools cannothandle efficiently. Across multiple hardware approaches (superconducting, trapped ion,photonic, neutral atom, topological, quantum dots), progress in errorcorrection and device engineering suggests that systems with around 200usable logical qubits could be available by 2028 to 2029. Industrialization islikely to accelerate through the 2030s: Quantum processing units (QPUs) willemerge as standard resources, hardware will miniaturize and specialize, andthe software and algorithm stack will mature. As systems with thousands of logical qubits become available (mid 2030s) anderror correction scales, quantum computing is expected to move fromexperimental platforms to integration into enterprise architectures. It will sitalongside data platforms, AI models, and high‑performance computing (HPC),and in some cases may displace parts of today’s GPU‑based workflows. Thebroader technology stack will evolve rapidly, reshaping from algorithms andanalytics to data architecture, deployment models, and integrated softwareecosystems. This doesn’tmean quantum will replace AI. Instead, it will extend theanalytical arsenal, bringing a new capability precisely where classicalapproaches hit their limits. The most advanced organizations will be thefirstto treat quantum, AI, and HPC as a continuum of tools that can be activated tocover the full spectrum of business analytics performance. But organizational change occurs more slowly than technology progress.Building the skills and use‑case portfolio, operating model, and infrastructureto exploit quantum at scale can take three to four years. Use cases take six tonine months to develop, from problem framing to mathematical modeling,algorithm tuning, data preparation (including accommodating new formats),