{"id":164352,"date":"2025-09-23T17:57:09","date_gmt":"2025-09-23T17:57:09","guid":{"rendered":"https:\/\/www.newsbeep.com\/ca\/164352\/"},"modified":"2025-09-23T17:57:09","modified_gmt":"2025-09-23T17:57:09","slug":"quantum-computing-moves-from-theoretical-to-inevitable","status":"publish","type":"post","link":"https:\/\/www.newsbeep.com\/ca\/164352\/","title":{"rendered":"Quantum Computing Moves from Theoretical to Inevitable"},"content":{"rendered":"


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This article is part of Bain\u2019s Technology Report 2025<\/p>\n


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Over the past two years, quantum computing has moved closer to practical, real-world applications. Breakthroughs in fidelity, error correction, and scaling\u00a0qubits (the basic units of quantum computing, like the 0\u2019s and 1\u2019s bits in classical computing) across platforms signal that it\u2019s not a question of if but when.<\/p>\n

Investment is following suit. Tech giants like Alphabet, IBM, and Microsoft are doubling down, while governments are scaling national quantum strategies. And it\u2019s not just computing: Quantum sensing, communication, and annealing (a technique for solving optimization problems) are already at work.<\/p>\n

Given the early state of commercialization, expansive and open-minded approaches are critical for the development of specific types of qubits; the infrastructure necessary to scale and manage quantum components that will run alongside the host classical systems; and algorithms and middleware tools for connecting with data sets and sharing results.<\/p>\n

IBM has taken this broad view, developing several generations of quantum-related technologies over the past 20 to 30 years. The company has generated interest in its quantum computing systems among academic and industrial users, in some cases by supporting solution providers as they explore the market.<\/p>\n

But the field remains open. No single technology or vendor has pulled ahead, and many technical hurdles remain. Experimentation costs have fallen, and companies can now embark on exploring quantum with relatively modest entry costs. The opportunities and uncertainties make preparation and agility key.<\/p>\n

Quantum\u2019s big market potential: Big but uncertain<\/p>\n

Quantum could unlock as much as $250 billion of market value across industries like pharmaceuticals, finance, logistics, and materials science (see Figure 1). While the full potential is immense, the pace of progress and extent of realization are uncertain, and many advances will need to be made beyond\u00a0qubit scaling. To reach full market potential, a fully capable, fault tolerant computer at scale will be needed\u2014and that\u2019s still years away.<\/p>\n


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\n Quantum computing\u2019s market potential could be between $100 billion and $250 billion
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\nSource: Bain analysis
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At least four major barriers stand in the way:<\/p>\n

Hardware maturity: Quantum computing faces steep technological hurdles before it can reach full potential, many having to do with the need to convert or hold information in the fragile quantum state. These include physical scaling, fidelity and error correction, coherence times (that is, how long a\u00a0qubit can retain its quantum state), quantum memory (similarly, the ability to store quantum information reliably over time), data loading (the process of converting traditional data to quantum information), and qubit control bottlenecks (the ability to manipulate qubits without losing fidelity or picking up cross-talk). Although some might hope for a Moore\u2019s law relative to qubit scaling, the nature of quantum devices and the challenges of scaling here are quite different, and their difficulty increases exponentially with qubit count.
\nAlgorithm maturity: While quantum computing hardware garners most of the headlines, for many use cases, practical application will require major advances in quantum algorithms (QA). Research is ongoing and major progress has been made in optimizing existing quantum algorithms, but the pace of new QA development has slowed.
\nQuantum machine learning (QML): Over half the projected market value (about $150 billion) sits here, but it\u2019s still mostly theoretical. Key algorithmic and data-loading bottlenecks suggest this could be among the later use cases realized, and the applications for the highest-value machine-learning cases (generative AI, LLMs) remain even more speculative.
\nPractical ROI: Many current quantum targets, including simulation and optimization, are already being tackled with \u201cgood enough\u201d classical computing. To justify using quantum computing instead, it would need to deliver real, sustained performance and cost advantages in places where classical computing approaches fall short, even as classical computing continues to advance.\u00a0<\/p>\n

Some expect a single \u201cquantum breakthrough,\u201d but we anticipate more of a gradual curve: early wins in narrow domains within five to ten years with broader adoption unfolding over time.<\/p>\n

The market today for quantum computing hardware and services is less than $1 billion a year. Barring a major breakthrough, roadmaps suggest that over the next five to ten years we\u2019ll see initial examples of quantum supremacy\u2014quantum computers outperforming classical approaches in practical and useful applications\u2014although the scope of application will be limited. These earliest practical applications in simulation (for example, metallodrug- and metalloprotein-binding affinity, battery and solar material research, or credit derivative pricing) and optimization (logistics, portfolio analysis) will boost the quantum computing market to between $5 billion and $15 billion by 2035\u2014still a far cry from the $250 billion a fully capable, full-potential quantum computing could unlock.<\/p>\n

Getting ahead means choosing the right pilot use cases and investing in talent and technical readiness now.\u00a0<\/p>\n

Cybersecurity: Quantum\u2019s immediate implication\u00a0<\/p>\n

The potential for quantum computing to overcome today\u2019s best encryption is real. Although today\u2019s quantum computing can\u2019t break state-of-the-art encryption yet, some malicious organizations have embarked on a strategy of \u201charvest now, decrypt later,\u201d intending to store sensitive data for five years or more, until the evolution of quantum empowers them to break the encryption. <\/p>\n

Bain\u2019s recent survey on the implications of post-quantum cryptography (PQC) on cybersecurity found that 73% of IT security professionals expect this to be a material risk within five years, and 32% expect it within three years, though some expect it will take longer (see Figure 2). However, even if it takes years for quantum to crack today\u2019s levels of encryption, PQC is becoming a necessity. Still, only 9% of tech leaders surveyed said they have a roadmap in place for dealing with it.<\/p>\n


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\n Most tech leaders recognize quantum\u2019s cybersecurity risk, but very few have a plan to address it
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Note: The survey population varies by question because respondents were screened out based on certain criteria, ranging from 182 to 226 participants <\/p>\n


\nSource: Bain Post Quantum Cryptography (PQC) Survey, May 2025
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The transition isn\u2019t easy; it requires mapping the company\u2019s\u00a0cryptographic landscape, updating protocols, and ensuring compliance. Companies will need to update their IT and cyber technology stacks with PQC-enabled solutions. For large or legacy-heavy organizations, this process could take years.<\/p>\n

A future of hybrid solutions<\/p>\n

Quantum computing will not replace classical computing\u2014it will complement it, becoming an important part of a broad mosaic of solutions. Quantum computing will play a targeted role, solving specific problems where classical systems fall short. Quantum computing is likely to replace supercomputing tasks in initial applications, where it won\u2019t compete with high-performance data centers. As it evolves, it\u2019s likely to take on a wider range of activity, but still in a hybrid fashion where it will work with classical computing to form complete solutions. Already, much of the focus is on developing architectures in which quantum and classical computing work well together.<\/p>\n

The future compute stack will be a mosaic, with quantum processors alongside CPUs, GPUs, and other accelerators optimized for specific functions. Winning companies will be those who can knit these together into a unified, high-performing system.<\/p>\n

Symmetrically, the future of analytics will be a mosaic, too: with capabilities ranging from simple regressions to AI, agentic, high-performance compute capabilities and quantum algorithms, each evolving in complementary fashion and directed at specific business problems.<\/p>\n

Mobilize today; lead tomorrow\u00a0<\/p>\n

For many companies, the most pressing need is securing data for a post-quantum world. In sectors where quantum is likely to have a near-term impact, developing readiness is more operational than technical. That means defining target use cases, building capabilities, forming partnerships, and scanning for signals in a fast-moving space.<\/p>\n

Working with companies to assess their quantum readiness, we\u2019ve seen it takes three to four years on average to go from awareness to a structured approach that includes a strategic roadmap, an ecosystem of partnerships, and pilot programs. Moving quantum use cases from R&D out to business units and functions, including the time needed to experiment and climb learning curves, can take between six and nine months. This reflects the time needed for mathematical modeling, algorithm tuning, formatting incoming and outgoing data, deployment, interpretation, impact on competitiveness, as well as recruiting and training the right people. The challenge today is navigating between moving too quickly (including overinvesting) in a not-yet-mature technology and moving too slowly, which could leave a company struggling to keep pace with competitors.<\/p>\n

Most companies are still in the early stages. With talent scarce and the learning curve steep, those who move now will shape the quantum landscape later.<\/p>\n


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\n More from the report
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\n Read our Technology Report 2025 \n <\/p>\n


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