The world is on the cusp of another technological revolution, this time powered by quantum computing. After a decade dominated by artificial intelligence, attention is shifting to quantum computers and their promise to solve problems beyond the reach of today’s fastest supercomputers. Businesses, governments, and investors around the globe are pouring resources into quantum technology, betting that it will propel the next wave of innovation and economic growth. This article explores what quantum computing is, why it matters, who is leading the race, the policies enabling it, early real-world applications, and how it might converge with AI to transform industries in the years ahead.
The Promise of Quantum Computing
A simple definition of a not so simple subject is – quantum computing harnesses the counterintuitive physics of quantum mechanics, notably superposition, a qubit’s ability to be in multiple states at once, and entanglement, meaning strong correlations between qubits, to process information in ways impossible for classical computers. In a classical computer, bits are strictly 0 or 1. A quantum bit or qubit can exist in a superposition of 0 and 1 simultaneously, enabling a quantum processor to explore many possible solutions in parallel. Moreover, entangled qubits can coordinate their states instantly, allowing certain computations to scale exponentially with qubit count. The promise of quantum computing lies in its potential to solve select problems dramatically faster than classical machines. A sufficiently large quantum computer could factor large numbers exponentially quicker, breaking current encryption, search unsorted data faster, or simulate complex molecules and materials with atomic accuracy. These capabilities translate into transformative applications, optimising global supply chains and traffic systems, designing more efficient solar cells and fertilisers, discovering new drugs and materials, and advancing artificial intelligence. In short, quantum computers could attack “problems that we can’t easily do now” by leveraging nature’s quantum rules. However, the field is still in its infancy. Today’s devices have only tens or hundreds of noisy qubits, and their “quantum advantage” over classical computing has been proven only for a few specialised tasks in theory. Building a large-scale, fault-tolerant quantum computer is an immense scientific and engineering challenge. Qubits are extremely sensitive, the same quantum effects that give them power also make them error-prone, requiring ultracold temperatures and isolation from noise. Despite these hurdles, progress in the last decade has been staggering. Researchers and startups worldwide are experimenting with myriad qubit technologies, hoping one will yield a breakthrough. The promise that keeps excitement high, if those challenges are overcome, a “large quantum computer could be transformational to society”, enabling solutions to problems previously deemed intractable. A superconducting quantum device held in hand. Quantum computers use specialised hardware operating at extreme conditions such as millikelvin temperatures to manipulate qubits that leverage quantum physics for computation. Such devices could eventually tackle convoluted tasks like molecular simulation, optimised logistics, and cryptography far beyond the capacity of today’s silicon chips.
How Consequential is Quantum Computing to the Future?
Many technologists view quantum computing as the next frontier that could redefine computing and industry, much as classical computing did over the past half-century. Its long-term consequences could be profound. A fully capable quantum computer would effectively be a “spell-breaker” for certain hard problems, breaking current internet encryption by factoring large primes, or simulating quantum chemistry to discover new materials and pharmaceuticals. These advances could revolutionise sectors from finance to healthcare. In finance, quantum algorithms might optimise investment portfolios or risk management in ways classical algorithms can’t. In healthcare and materials science, quantum simulations could unlock new drugs, catalysts, or batteries by accurately modelling molecular interactions . Logistics and manufacturing could benefit from quantum optimisation of complex scheduling and routing problems, leading to leaner, more efficient operations. Quantum computing is often likened to a “second quantum revolution,” building on the first that gave us semiconductors and lasers a century ago. As one analyst put it, “quantum computers would surpass the problem-solving capacities of current computers by vast orders of magnitude, revolutionising industries from communications to drug development.” The United Nations has even declared 2025 the International Year of Quantum Science and Technology, underscoring expectations that quantum breakthroughs can “make the world a better place for everyone”. That said, experts caution that realising quantum computing’s promise will take time. We are currently in the NISQ, “noisy intermediate-scale quantum”, era machines with dozens of qubits that suffer frequent errors. Useful, generalpurpose quantum computers may be “still some decades off,” and even then, quantum advantage is proven for only a handful of significant problems so far. The impact of quantum computing will likely unfold gradually, initial commercial uses in niche areas, including material design, as these are inherently quantum problems, then broader applications as hardware scales and stabilises. Nonetheless, the high-risk, high-reward nature of the field hasn’t deterred massive investment, because the potential payoff, a computational tool that can solve otherwise unsolvable problems, is so consequential. In the long run, quantum computing could become as fundamental to technology as digital computing is today, complementing classical computers and working in tandem with AI to accelerate innovation. As one report summarised, governments and companies are “plowing billions into the quantum wave” precisely because early movers could dominate the next generation of high-tech markets.
The Quantum Race: Key Players, Geopolitics, and Geoeconomics
Quantum computing has ignited a global race reminiscent of the space race, a competition for technological leadership with economic and security implications. The United States and China are widely seen as front-runners, each investing heavily to ensure dominance in quantum technologies. Europe, too, is vying not to be left behind, while other countries from Canada to Japan, Australia, and India are carving out niches. The outcome of this race could reshape geopolitics and geoeconomics, countries that lead in quantum computing may gain significant advantages in cybersecurity, economic growth, and scientific prowess. The U.S. currently captures over 50% of global private quantum investment and leads in key metrics like top-cited research and patents, U.S. entities account for a large share of highly cited quantum research papers and intellectual property. American tech giants including IBM, Google, Microsoft, and Amazon are at the forefront of hardware development, while a vibrant startup ecosystem including IonQ, Rigetti, and Quantinuum are driving innovation. The U.S. government has treated quantum as a strategic priority, akin to AI or semiconductors, for both economic and national security reasons. In 2018, it enacted the National Quantum Initiative Act, a $1.2 billion program coordinating research across federal agencies, academia, and industry. This was followed by additional billions in funding through the National Science Foundation, Department of Energy, and defence agencies. By 2024, total U.S. public investment in quantum R&D was reported at around $4 billion, with a pending reauthorisation targeting another $2.7 billion. U.S. leadership is motivated partly by security, anticipating threats to encryption, and a desire to maintain tech supremacy. Indeed, Washington has already imposed export controls on certain quantum technologies to prevent them from falling into rival hands. China has pursued quantum tech with long-term, state-driven strategy and reportedly leads in overall government spending, estimated ¥100 billion+ or ~$15 billion in public funding across multiple programs. It stunned the world with quantum communication feats, launching the Micius satellite for quantum-encrypted communications and building a 2,000 km QKD network, and achieved experimental “quantum supremacy” in the lab including the Jiuzhang photonic computer’s randomised calculation beyond classical reach. Chinese universities and companies including giants such as Alibaba, Baidu, and Huawei, are rapidly advancing superconducting and photonic qubit prototypes. By one estimate, China accounts for roughly 40% of global private quantum funding, much through state-influenced capital. Geopolitically, Beijing frames quantum tech as part of strategic competition with the West – their 14th Five-Year Plan explicitly underlines quantum computing and cryptography as critical areas. The concern in Western capitals is that if China wins the quantum race, it could undermine others’ cybersecurity, through code-breaking, and gain an edge in military technology, through superior simulation and optimisation. This has led to a techno-nationalist dynamic, the U.S. and China increasingly view quantum leadership as a matter of national pride and security, akin to the nuclear or space races of the Cold War. Meanwhile, Europe boasts world-class quantum science, EU-based researchers produce the largest share of quantum scientific publications globally, but it has historically struggled to commercialise breakthroughs. Determined not to repeat the mistakes of the AI and semiconductor eras, the EU launched a €1 billion Quantum Flagship initiative in 2018 to “kick-start a European industry in quantum technology”. This funds academic-industry consortia across computing, communications, sensing, and simulation. European governments collectively have committed billions more – Germany with €2 billion (2020) and an additional €3 billion action plan (2023) to build a universal quantum computer by 2026, France with a €1.8 billion quantum plan, announced by President Macron in 2021, aiming to put France among the world’s top 3 quantum powers, and the UK with a new £2.5 billion program through 2033. Despite this, the EU noted in 2025 that Europe attracted just 5% of global private quantum funding, versus ~50% in the U.S. and 40% in China. Fragmented capital markets and fewer big tech players are challenges for Europe. Nonetheless, Europe has strengths in collaboration and infrastructure, the EU is integrating quantum computers into its supercomputing centres, EuroHPC, and rolling out a pan-European quantum-secure communication network, EuroQCI. European startups like IQM of Finland, Pasqal of France, and Oxford Quantum Circuits of the UK are gaining traction, and the bloc sees an opportunity to lead in setting standards and ethical frameworks for quantum tech, just as it did for data privacy with GDPR. European officials also balance openness with caution, several countries have imposed export controls on sensitive quantum knowledge to prevent its misuse. Canada punches above its weight in quantum. It was home to D-Wave, the first company to sell quantum systems, and has strong research hubs in Waterloo and Toronto. Canada unveiled a National Quantum Strategy in 2023 with CA$360 million initial funding and has invested in quantum institutes for years. Similarly, Japan has longstanding quantum research, Toshiba’s quantum cryptography, and NTT’s quantum labs being prominent examples, and a program called Q-LEAP focusing on quantum simulation, sensing, and computing since 2018. The Japanese government committed ¥1.05 trillion or $7 billion in next-gen tech including quantum and aims for a fault-tolerant quantum computer by 2050. Australia has world-renowned quantum scientists and startups like Silicon Quantum Computing and Q-CTRL, and recently released a National Quantum Strategy in 2023. Australia’s government and universities have attracted significant investments; in fact, Australian funds invested nearly $100 million into PsiQuantum, a U.S. photonic quantum startup, and the country sees quantum as a chance to “punch above its weight” scientifically. India only recently launched a major initiative, the National Quantum Mission, approved in 2023, with a budget of ₹6004 crore ($730 million) to advance quantum technologies by 2031. India aims to build indigenous quantum computers, targeting 50-100 qubits by 2026, and develop a skilled workforce, viewing quantum tech as critical for future economic growth and security. Dozens of other countries have joined the quantum race, in 2023, over 30 nations had national quantum programs and committed more than $40 billion in public funding collectively. South Korea, for example, announced a national quantum strategy in 2023, and Israel launched a $1.2 billion program including building a 30–40 qubit computer domestically. From Russia, which has stated plans for 50–100 qubit machines and quantum networks, to Brazil, South Africa, and beyond, many governments are eager not to miss out on what is seen as a general-purpose technology of the future. The race for quantum supremacy is about more than bragging rights. It could determine who controls next-generation encryption and secure communications, as quantum computers threaten current cryptography but also enable quantumsafe encryption and networks. It will influence military capabilities, faster logistics optimisation, new materials for defence, stealthier communications. Economically, countries leading in quantum could dominate high-tech industries, patent vital algorithms, and attract the best talent, similar to how Silicon Valley’s leadership in classical computing fuelled U.S. economic strength. Policymakers speak of avoiding a “quantum gap” akin to past “missed races.” Europe’s tech commissioner recently noted that quantum is “a matter of national security”, urging joint investment so Europe doesn’t fall behind the U.S. and China. Alliances are also forming, the U.S., EU, and allies have begun collaborating on quantum R&D and setting norms for responsible use. In summary, quantum computing is now a key element of geotechnological strategy, those who lead could “reap huge economic benefits, dominating next-generation industries,” while laggards risk dependency
But, is Quantum Computing Useful Yet?
What has all the investment in quantum computing yielded so far in practical terms? Today’s quantum computers are mostly research prototypes, with limited real-world applicability. They are generally too small and error-prone to outright surpass classical computers in business tasks. However, we are witnessing the first glimmers of real utility, pilot projects and experiments hinting at how quantum computers might be used in practice, as well as adjacent quantum technologies, like quantum communication, already making an impact. One notable demonstration of quantum computing’s potential occurred in transportation optimisation. In late 2019, Volkswagen teamed up with D-Wave to route public buses in Lisbon using a quantum annealer. This pilot, the world’s first live quantum computing application in traffic management, used D-Wave’s quantum processor to calculate optimal routes for 9 city buses in real time. The system successfully navigated buses to avoid congestion, reducing passenger travel times during rush hour. While a modest test, it showcased that even today’s noisy quantum machines can tackle certain complex optimisation problems like traffic flows when carefully tuned. Volkswagen’s CIO called it a meaningful step toward “understanding how this technology can be put to use within the company,” and D-Wave highlighted it as an example of “real-world impact” – one of the first instances a quantum computer delivered a benefit to everyday commuters. Another area seeing early quantum use is quantum chemistry simulation. Because quantum computers naturally emulate quantum systems, they’re well-suited to modelling molecular interactions. Researchers have used small quantum processors to calculate properties of simple molecules such as hydrogen chains, and lithium hydride more efficiently than basic classical methods. In 2020, a team used an IBM quantum computer to simulate the energy surface of a lithium hydride molecule, a step toward quantum-enabled drug discovery and materials design. Though classical computing can handle small molecules, these early experiments are building expertise for eventually tackling larger, industrially relevant chemistry problems such as enzymes or new battery materials, where classical computation struggles. Companies like Merck, Daimler, and BMW have partnered with quantum computing firms to explore how these simulations could eventually accelerate R&D, such as finding better catalysts or optimising chemical reactions. In one case, BMW is collaborating with Quantinuum to develop improved fuel cells, leveraging quantum simulations of electrochemical reactions . Financial services firms are also experimenting. HSBC is working with the startup Terra Quantum to apply quantum algorithms to asset allocation optimisation. JPMorgan, Goldman Sachs, and others have active quantum research teams looking at portfolio optimisation, option pricing, and risk analysis using quantum computing techniques. While no quantum solution yet beats classical algorithms for these problems, banks are building know-how so they’re ready when hardware can handle larger models. A recent report noted over 300 companies in various industries were exploring quantum use cases in 2023, double the number from the year prior. The most popular trial applications were “fasttracking drug discovery, building more cyber-secure systems, and improving portfolio optimisation and fraud detection” in finance . Perhaps the most mature quantum technology today is quantum cryptography/communication, rather than computing. Quantum Random Number Generators (QRNGs) are already commercially available, providing true randomness for cryptographic keys. More importantly, Quantum Key Distribution (QKD), a method to share encryption keys with security guaranteed by quantum physics, is in use. Banks in Switzerland and government agencies in China use QKD links for ultra-secure communication. China’s quantum satellite and national QKD network have carried secure video calls and data traffic. In October 2021, China demonstrated intercontinental QKD by integrating the satellite with fiber networks. The EU and U.S. are also deploying testbed quantum networks. While this is not computing per se, it’s part of the broader quantum tech ecosystem spurred by the same scientific advances and policy support. It’s worth noting that in 2019 and 2020, researchers at Google and USTC in China achieved “quantum supremacy,” performing specific contrived calculations that would take impractically long on a classical supercomputer. Google’s 53-qubit Sycamore processor randomly sampled quantum circuits in 200 seconds, a task they estimated a supercomputer would need 10,000 years for, though IBM argued it could be done in a few days with certain optimisations. By the same token, USTC’s photonic Jiuzhang processor solved a random boson sampling in seconds that would overwhelm classical algorithms. These feats were milestones in proving quantum devices can outperform classical at something. However, the tasks were highly specialised and not directly useful for industry. The significance was mostly scientific, a proof of concept that quantum speedups are real. Now, the focus is shifting to achieving a practical quantum advantage on problems of business or scientific value. So, does quantum computing have applicability to show as of now? In a limited sense, yes, there are early pilot successes and research breakthroughs, live traffic optimisation in a city, quantum-enhanced machine learning research that compressed big data in ways classical methods couldn’t, and a doubling of companies experimenting with quantum for real use cases in the past year. But these are baby steps. Experts note that truly practical applications, where quantum computers deliver clear economic value or solve an everyday problem better than classical systems – likely require better hardware and could be years away. A 2024 industry report estimated that “practical use cases for quantum could still be up to 20 years away” due to current machines’ cost, error rates, and the shortage of quantum talent to build on them. In the interim, we will continue to see hybrid approaches, using today’s small quantum chips alongside classical computing in cloud services, offered by e.g. IBM, Amazon, Microsoft, to test algorithms on real quantum hardware, and using quantum-inspired algorithms to get partial benefits now. Some logistics firms use “quantum-inspired” optimisers on classical machines for scheduling, claiming improvements akin to what a future quantum annealer might provide. In summary, quantum computing is just beginning to show its first real-world applications. They are niche and experimental, a far cry from quantum computers transforming the world yet. But these early wins in traffic flow, materials simulation, and finance are important proof-of-concept signals. They demonstrate to businesses and governments that it’s worth investing in quantum readiness today, so that as the technology matures from a few dozen high-quality qubits to hundreds or thousands, organisations will be prepared to capitalise on quantum breakthroughs in solving real problems.
The Investment Landscape: Funding Levels and Major Players
The promise of quantum computing has spurred a surge of investment from a mix of actors, governments, venture capitalists, tech giants, and even some end-user industries. After a relatively quiet period in the 2000s, the 2015–2022 period saw quantum technology funding explode, as concerns about the end of Moore’s Law and the allure of quantum advantage grew. Understanding where the money is coming from, and who the key players are, gives insight into how the field might progress commercially. By 2023, annual venture capital funding for quantum startups had reached roughly $1–2 billion per year. In fact, 2022 was a record peak with over $2 billion raised globally by quantum tech companies, before a dip in 2023 which saw about $1.2 billion raised amid broader VC cooling. Notably, unlike many tech sectors, Europe took the lead in quantum VC funding in 2023, startups in EMEA raised $781 million, three times the amount raised in North America that year. This was a quirk of timing, U.S. quantum startups had a blockbuster 2022 such as Alphabet’s spin-off SandboxAQ raised $500 million, and Canada’s Xanadu $100 million, then saw an 80% drop in 2023 as some big players paused, whereas Europe’s funding grew slightly with multiple mid-sized rounds including for Pasqal’s €100 million, Oxford Quantum Circuits’ $100 million, Quandela’s €50 million, etc. Over the longer term, the U.S. still leads in cumulative private investment, about half of global, as noted. But the key point is that quantum remains a niche sector, total VC funding in 2023 was <1% of what went into, say, AI startups, and investors are cautious due to the high costs and long horizons involved. On the public funding side, as discussed, governments have announced over $40 billion in quantum tech funding globally. China’s public spend is thought to be highest, perhaps $10–15 billion so far, followed by the EU collectively, ~$7 billion across EU institutions and member states combined commitments, and the U.S., around $4–5 billion explicitly allocated, not counting defense classified investments. Countries like the UK and Germany have each pledged on the order of $4 billion over the coming decade. This public funding often complements private capital, e.g., governments may fund academic research and provide grants to startups, while VCs fund product development and scaling. A striking feature of quantum computing is the diverse range of organisations involved, from garage startups to multinational conglomerates, each with different technical approaches. Below we highlight some of the influential players and their particular strengths or strategies, grouped broadly by their technology approach. An early champion among them, indubitably is IBM (USA), Superconducting Qubits. IBM has arguably the most advanced quantum hardware program to date. It has steadily increased qubit counts on its superconducting quantum chips, using transmon qubits on quantum circuits. IBM’s latest processor, Osprey, has 433 qubits, introduced in 2022, and its roadmap aims for a 1,121-qubit “Condor” chip in 2024 and beyond. IBM has also been a leader in quantum software, developing the Qiskit platform and quantum cloud services. Its strength lies in stability and integration, IBM offers access to its quantum processors via the IBM Quantum Network to hundreds of thousands of users, and it has demonstrated some of the highest quantum volumes and fidelities in the industry. IBM boldly announced it expects to have a “workable” large-scale quantum computer by 2029, interpreted as a fault-tolerant or broadly useful machine, underscoring its confidence. IBM’s long history in computing and its deep R&D pockets make it a frontrunner, though competitors are nipping at its heels. Google, another giant, Superconducting Qubits. Google’s Quantum AI team made headlines with the “quantum supremacy” experiment in 2019. Google’s approach also uses superconducting qubits; its current focus is on scaling up and improving error correction. In late 2022, Google revealed a new generation chip, “Sycamore Next” aka Willow, with more qubits and techniques to reduce error rates. Google is pursuing modular scaling, linking multiple chips together, and heavy-duty quantum error correction research. It aims to eventually build a stable error-corrected qubit from a cluster of many physical qubits. Google’s strength is its research excellence, working closely with leading academics, and computing infrastructure. However, unlike IBM, Google doesn’t offer broad public cloud access to its quantum processors yet, it partners selectively with national labs. Google’s target timeline for a useful quantum computer is similar to IBM’s, within the 2020s, though it has been more tight-lipped on exact dates. Microsoft (USA), Topological Qubits and Cloud Services. Microsoft took a different path, investing years in a quest for topological qubits, exotic qubits based on Majorana particles that promise inherent error resilience. Microsoft announced in 2023 it had achieved a key milestone by creating and controlling Majorana quasi-particles, claiming this paves the way to a new type of qubit that is far more stable. While this approach has taken longer, Microsoft still hasn’t unveiled a working multi-qubit device, the potential payoff is huge, if topological qubits work, Microsoft might need far fewer of them for a reliable quantum computer. Microsoft is so confident that in mid-2023 it declared an aspirational goal to build a quantum supercomputer of 1 million+ qubits within 10 years. In the meantime, Microsoft’s strength is its quantum software and cloud. Azure Quantum is a cloud platform that offers access to other companies’ quantum hardware including IonQ, Quantinuum, and QCI, and robust tools for developers, the Q# programming language, resource estimators, etc. Microsoft also underscored integrating quantum and classical computing seamlessly via Azure’s infrastructure. In short, Microsoft is betting on a moonshot hardware approach, but covering its bases with a strong software ecosystem. Amazon Braket / AWS (USA), Cloud Platform, plus internal R&D. Amazon Web Services launched Amazon Braket, a cloud service that gives users on-demand access to a variety of quantum computers from different providers, IonQ, Rigetti, D-Wave, OQC, and QuEra, etc. This neutral platform approach positions AWS as the “Switzerland” of quantum cloud, lowering the barrier for businesses to experiment. At the same time, Amazon is building its own quantum hardware at the AWS Center for Quantum Computing in California. Amazon unveiled “Ocelot,” in 2023, its prototype quantum processor using a novel bosonic qubit design, a form of cat qubit, that encodes information in electromagnetic oscillations. AWS’s approach focuses on error mitigation from the start, Ocelot’s cat qubits have built-in protection against certain errors, potentially cutting the overhead for full error correction by 90% . Amazon’s strength is its deep tech expertise and cloud dominance. It can integrate quantum with classical computing workflows including hybrid algorithms using AWS GPUs with a quantum coprocessor, and reach a broad user base via its cloud. With Ocelot, AWS signalled it’s in the hardware race too, targeting practical fault-tolerant machines within “a decade or so,” according to its engineers. For now, AWS provides the plumbing and is content to let a thousand quantum startups bloom on its platform. Amazon’s first-generation quantum chip “Ocelot,” unveiled in 2025. The AWS design uses bosonic cat qubits, oscillating electrical modes, with built-in error suppression, aiming to reduce the resources needed for quantum error correction. Major tech firms like Amazon, Google, IBM, and Microsoft are each pursuing different hardware innovations as they race to build scalable quantum computers. D-Wave Systems (Canada), Quantum Annealing Specialist. D-Wave is unique as the only company currently selling “quantum computers” that some non-academic customers use. Its machines, however, are quantum annealers, not gatebased universal quantum processors. D-Wave’s annealers, now in the 5,000+ qubit range, are tailored for solving optimisation problems by finding low-energy states of a system. They cannot run arbitrary algorithms but excel at certain tasks like optimisation and sampling. D-Wave has placed systems at NASA, Lockheed Martin, and Los Alamos, and offers cloud access. It has demonstrated applications from traffic flow, Volkswagen’s project, to scheduling and protein folding in research. D-Wave’s strength is practicality and first-mover advantage, it has cultivated a developer community and “quantum-ready” formulations for real-world problems. That said, critics note D-Wave’s performance advantage over classical solvers is not always clear-cut, and it too faces scaling challenges, quantum annealing benefits from more qubits but also needs increased connectivity and lowered noise. Recently, D-Wave has expanded into offering a hybrid solver service, combining classical and quantum resources to solve large problems, and is even developing a gate-model quantum processor in-house for broader capabilities. As a public company, it went public via SPAC in 2022, D-Wave has been under pressure to generate revenue, and it has steadily grown a small customer base of businesses exploring optimisation use cases. Its clear forte remains optimisation problems such as scheduling, routing, allocation, where its annealers, even if not outperforming classical algorithms yet, provide a different tool that sometimes finds good solutions quickly. IonQ (USA) – Trapped Ion Qubits. IonQ is a leader in trapped-ion quantum computing and was the first pure-play quantum computing company to go public (NYSE: IONQ). Trapped ion qubits are atoms, like Ytterbium, held in electromagnetic traps and manipulated with lasers. They are known for very high fidelity operations, two-qubit gate fidelities over 99% in the lab, and long coherence times, at the cost of slower gate speeds. IonQ’s current systems, Harmony and Aria, have on the order of ~20 algorithmic qubits, usable entangled qubits, with very low error rates, making them relatively small but powerful for their size. IonQ’s strength is its technical performance and clear roadmap. IonQ declared an “Accelerated Roadmap” leveraging new tech, acquisitions of firms for photonic interconnects and integrated ion traps, to scale from dozens of qubits to thousands in just a few years. IonQ intends ~20,000 physical qubits via networking two 10,000-qubit chips by 2028, which it estimates would yield around 1,600 error-corrected logical qubits, enough for a “cryptographically relevant” quantum computer (CRQC) that could threaten RSA encryption. It even anticipates 2 million physical qubits (~50,000 logical) by 2030 for broad quantum advantage. These targets are aggressive, essentially IonQ aspires to leapfrog competitors in scale by the end of the decade. Its decision to go public gave it substantial capital to pursue this vision. IonQ’s approach to scaling uses modularity, many ion traps networked via photonic links, and it benefits from trapped ions’ all-to-all connectivity and consistency. If it meets its milestones, IonQ could be first to demonstrate a quantum computer that can crack real-world cryptography or solve industrial chemistry problems by later this decade. In the nearer term, IonQ has already made its systems available through cloud platforms including the likes of AWS, Google, Azure, and has partnerships to explore applications in machine learning and finance. Its forte: high-fidelity computing – IonQ holds records in quantum volume and algorithmic qubit counts among commercial systems, meaning it can run deeper circuits on fewer qubits than most. Quantinuum (USA/UK) – Trapped Ions + Quantum Software. Quantinuum was formed by the 2021 merger of Honeywell’s Quantum Solutions (hardware) and Cambridge Quantum (software). Honeywell developed high-quality trapped-ion quantum hardware similar to IonQ’s approach, emphasising high fidelity and mid-circuit measurement. Its H1 series machines achieved quantum volume records, CPU-like benchmark for quantum computers, and demonstrated innovative feats like real-time error correction of a logical qubit in 2022. Cambridge Quantum, meanwhile, brought expertise in quantum algorithms, especially in chemistry, as well as a quantum-safe cryptography platform. Together as Quantinuum, the company’s strength is being a full-stack integrator, it builds hardware, but also its own operating system, software (TKET compiler), and flagship applications in encryption and chemistry. Quantinuum released a commercially available quantum random number generator device, the “Quantum Origin” platform, that banks and cybersecurity firms can use now to strengthen encryption keys. They also worked with BMW on using quantum chemistry for next-gen fuel cell catalysts and with JSR, a materials company, on quantum simulations for photoresists. Quantinuum’s latest hardware roadmap involves moving to next-gen ionic qubits such as Ytterbium and Barium ions with higher fidelity and integrating more qubits, the upcoming H2 model. Because Honeywell’s business model isn’t to sell devices but to provide access, Quantinuum focuses on showcasing the highest performance on meaningful tasks, like simulating moderately complex molecules, to attract enterprise clients. Its forte is reliability and enterprise focus, it has perhaps the best error rates in the industry on a per-gate basis and is aligning its offerings with immediate business needs, even while true quantum advantage is pending. Pasqal (France), Neutral Atom Qubits (Analog/Digital). Pasqal is a European quantum computing star, using arrays of neutral atoms, typically rubidium atoms, trapped by lasers and excited into Rydberg states to perform computations. Pasqal’s approach allows qubits to be arranged in flexible 2D (even 3D) geometries and to operate either as an analog quantum simulator or a digital gate-based computer. Its near-term focus is often on analog quantum simulation for specific problems like solving differential equations or materials models by directly programming inter-atom interactions. Pasqal has demonstrated programmable 100+ atom setups and aims for 1000-qubit scale within a couple of years. In January 2023, Pasqal raised €100 million, one of Europe’s largest quantum rounds. Its strength lies in strong analog simulation capabilities and industrial partnerships. Pasqal is working with Siemens and EDF on energy grid optimisation and with chemical company BASF on molecular simulations. It also partnered with South Korea’s POSCO to explore quantum methods for improving steel manufacturing for optimising crystalline structures. Pasqal’s devices have an inherent ability to naturally simulate certain physics problems like modeling magnetic materials or fluid dynamics by configuring lasers and letting the atoms’ quantum evolution mimic the system, a potentially faster path to useful results than fully error-corrected digital algorithms. Its challenge will be adding error correction for digital quantum computing, but Pasqal’s CEO has projected achieving practical quantum advantage for some real-world problems by the mid-2020s. In summary, Pasqal’s forte is versatile neutral-atom processors that straddle the line between quantum simulator and computer, potentially delivering value sooner in domains like materials science. QuEra Computing (USA) – Neutral Atom (Analog) Specialist. QuEra, based in Boston, also works with neutral Rydberg atom arrays. QuEra’s 256-qubit analog quantum computer Aquila became available on Amazon Braket in 2022, making it the first neutral-atom machine on a public cloud. Aquila is designed for Analog Hamiltonian Simulation (AHS), essentially, programming the atoms’ positions and interactions to emulate quantum many-body systems or graph optimisation issues. Researchers have used it to explore exotic physics, like observing a possible spin-liquid phase, a quantum state of matter hard to simulate classically, on a 2D lattice. QuEra has also shown how Aquila can solve the Maximum Independent Set (MIS) problem on graphs, relevant to network design and scheduling tasks, by mapping the graph onto atom interactions. QuEra’s strength is scaling and analog performance, 256 qubits is one of the largest quantum platform sizes available, and they plan to scale beyond 1,000. While analog, the ability to program arbitrary atom layouts, lattice geometries or graph shapes, gives it a lot of flexibility. QuEra’s roadmap includes moving to digital gates in the future, but it’s currently focused on demonstrating useful quantum simulation results that might be impossible for classical computation. By quadrupling access hours and collaborating with platforms like qBraid, QuEra is making its tech accessible to researchers aiming for near-term quantum advantage in specialised problems. Alice & Bob (France), Cat Qubits / Error-Corrected Qubits. Alice&Bob is a Paris-based startup taking a novel approach with “Schrödinger’s cat” qubits, which are superconducting microwave resonators designed to be inherently immune to certain errors. Alice&Bob announced its cat qubits were 10,000× more resistant to bit-flip errors than previous transmon qubits. The announcement came in 2023. This dramatically reduces one of the two major error types in qubits, bit-flips, while phase-flips are corrected via clever encoding. The startup raised €100 million in 2023 to scale up this technology, and even taped-out a chip called Helium with 16 of these cat qubits as a step toward a logical qubit. Alice&Bob’s strength is fault-tolerance by design, their goal is a universal quantum computer that needs far fewer physical qubits to make one error-free logical qubit, thanks to the stability of cat states. They have a roadmap targeting a fully faulttolerant prototype by 2030. In essence, Alice&Bob hopes to “skip” the NISQ era as much as possible and jump into the era of error-corrected quantum computing sooner. Their forte, error correction at the hardware level, which could vastly reduce complexity in scaling. Atom Computing (USA), Neutral Atom, Gate-based, with Scale. Atom Computing, based in California and Colorado, uses optically trapped neutral atoms, nuclear-spin qubits in alkaline earth elements. In late 2023, Atom Computing announced it had created a record 1,225-atom array with 1,180 functional qubits in a next-gen system, the first time any gate-based quantum platform surpassed 1,000 qubits. While these qubits are still “physical” qubits without error correction and not all are entangled yet, it is a remarkable scaling achievement. Atom’s design also boasts long coherence times (up to 40 seconds) for its qubits, meaning they maintain quantum states far longer than superconducting or even trapped-ion qubits. The company uses laser interactions to perform multi-qubit gates and has demonstrated key capabilities like mid-circuit measurement, to check and correct errors on the fly. Atom’s strength is clearly scalability, as their CEO put it, a “leap from 100 to 1,000+ qubits within a generation” shows the potential of neutral atom tech to rapidly grow qubit counts without the fabrication challenges of solid-state chips. Of course, quality matters as much as quantity, so Atom is also working to boost gate fidelities, recently achieving 99.6% in a two-qubit gate, comparable to leading platforms. If Atom can combine large qubit numbers with decent error rates, its systems might tackle more complex problems sooner in the NISQ regime. Its forte, high qubit count and long coherence, positioning it as a serious contender once error rates drop enough to leverage all those qubits. Rigetti Computing (USA), Superconducting Qubits. Rigetti was one of the earliest quantum computing startups, founded in 2013, and positioned itself as a smaller, fast-moving rival to IBM and Google in superconducting qubits. Based in Berkeley, Rigetti built multiple generations of chips, 8 qubit, 19, 32, and 80 qubit versions, and launched one of the first quantum cloud services. Its strength was in innovation and integration, pioneering multi-chip modules and a hybrid quantum-classical computing toolkit. However, Rigetti has faced challenges in recent years, hitting a plateau in qubit quality and encountering financial losses, it went public via SPAC in 2022 but has since seen its valuation drop. In 2023–24, Rigetti refocused with a new strategic plan emphasising higher fidelity over just higher qubit count. By mid-2025 it plans to launch a 36-qubit processor composed of four 9-qubit chips with 99.5% two-qubit gate fidelity, a significant jump in reliability. Later in its roadmap is a “Lyra” 336-qubit system using a modular 84-qubit tile design. Rigetti’s advantage is having its own fab for superconducting chips and deep expertise in that technology. It also secured U.S. government contracts, DARPA programs, that could keep it in the game. If Rigetti achieves its 99%+ fidelity and modular scaling, it may re-emerge as a key hardware provider. Its current forte is hybrid computing and partnerships, it works closely with national labs and companies like Aspen and Deloitte on pilot projects as quantum machine learning for climate modeling. Rigetti’s story underscores that quantum startups face a long road and the need for sustained capital, it had to cut workforce by ~28% in 2023 to extend its runway. But it remains one of the few with a full-stack approach including software as it developed the Quil programming language and quantum/classical integration experience. Quantum Circuits Inc. (USA), Superconducting Qubits with Error Detection. QCI is a Yale University spin-off cofounded by Prof. Robert Schoelkopf, one of the inventors of the transmon qubit. QCI’s approach is to build “dual-rail” superconducting qubits which include built-in error detection capabilities. Essentially, they use two physical resonators per logical qubit in such a way that if one experiences an error, like a photon loss, the system detects it, an erasure error, without collapsing the computation. This simplifies error correction because known erasures are easier to correct than arbitrary unknown errors. QCI’s system, called Aquarius, integrates this hardware with a custom control architecture that allows real-time classical feedback, fast qubit reset, and conditional logic during quantum circuits. The company has deployed a prototype on AWS Braket, making it accessible to developers. QCI’s strength is quantum error awareness, by catching errors as they happen, its machine can discard bad runs or correct them, yielding more consistent results. This can significantly boost effective fidelity for algorithms. Its focus is not on having many qubits now, but high-quality, repeatable operations on the qubits it has, on the order of a few qubits so far. QCI, with strong academic roots, is also pushing the envelope in algorithm development, inviting alpha users to test variational algorithms and dynamic circuits on its platform. Its forte, hardware-embedded error mitigation leading to more “stability, repeatability, consistency” in quantum computations, which is crucial for scaling up reliably. SEEQC (USA), Digital Quantum Computing SoC. SEEQC, based in New York, is pioneering a hybrid approach where classical and quantum hardware are integrated on the same chip using superconducting electronics. It builds Single Flux Quantum (SFQ) digital logic circuits that operate at the same cryogenic temperature as qubits. These digital SFQ chips can control qubits, read out qubit states, and even do some classical processing, like error decoding, in situ, rather than sending signals to room-temperature electronics. The result is effectively a quantum computer system-on-chip with much lower latency, lower power, and simpler wiring than conventional setups. SEEQC showcased prototypes of SFQbased control chips and is collaborating with partners like NVIDIA, for GPU integration into the quantum stack, and QuantWare, to use third-party qubit arrays with its SFQ controllers. SEEQC’s strength is engineering scalability, by eliminating bulky room-temperature control racks and coaxial cables in favour of on-chip digital control, it aims to make it feasible to manage thousands or millions of qubits. Their design claims 1000× lower power consumption, 10× faster readout, and 10× lower latency compared to conventional setups. They also highlight cost-efficiency, a projected $1k per qubit, 10× cheaper than now, and easier multiplexing of signals. In 2023, SEEQC demonstrated a fully digital chip-to-chip quantum/classical interface, essentially a qubit chip communicating directly with a classical logic chip in a cryostat in real time . Its forte, solving the control and interconnect bottleneck, which is often cited as a major challenge in scaling quantum computers. By co-inventing and patenting many of these SFQ techniques, SEEQC is positioning itself as the provider of the “quantum control plane” that could be used across different qubit modalities. If successful, its technology could become an essential component in many large-scale quantum computers, regardless of who makes the qubits. The quantum ecosystem remains broad. Baidu (China) has developed a 10-qubit superconducting computer, and a cloud platform “Quantum Leaf”, and Alibaba operates an 11-qubit superconducting machine on its Aliyun cloud in partnership with the Chinese Academy of Sciences. Chinese startup Origin Quantum reportedly built a 24-qubit superconducting system and is working on a 64-qubit one, alongside developing quantum OS software. Xanadu, Canada, is a leader in photonic quantum computing, like PsiQuantum, and created the PennyLane software for quantum machine learning; it made news in 2022 by achieving quantum computational advantage using a photonic boson sampling device with 216 squeezed-state qubits. QC Ware and Zapata Computing are notable in software, helping companies develop quantum algorithms without committing to a hardware bet. In hardware, IQM, Finland, builds superconducting QPUs and sells them to research labs, a unique business model, and Quantum Brilliance (Australia/ Germany) is exploring room-temperature diamond-based quantum accelerators. Q-CTRL (Australia) focuses on quantum control software to reduce errors on any quantum hardware. Quantum Machines (Israel) builds classical electronic controllers for quantum computers and is part of Israel’s project to develop a 40-qubit machine. And the list goes on, by one count there are over 250 quantum computing startups globally, each trying a different angle. While it’s a competitive field, many of these players are complementary, some do hardware, others software, some focus on integration or components, and collaboration is common.
The Quantum Investments Outlook
Quantum computing has attracted large investments ahead of tangible returns, much like AI did. There are now signs of an investment hype cycle, 2021–2022 saw euphoria with record funding, startups going public, and frothy valuations; 2023 brought a correction with funding halving and some companies retrenching. This reflects a broader tendency in global tech investment, a willingness to pour money into “the next big thing” early, from self-driving cars to fusion energy, followed by impatience if breakthroughs take longer than expected. Artificial intelligence also experienced such cycles, enormous investment in autonomous vehicles and general AI in the 2010s, followed by tempered expectations when full self-driving or strong AI didn’t arrive as soon as hoped. Yet, those investments laid groundwork that eventually led to real progress including today’s AI models like GPT-4. For quantum, the lesson is that there may be a “quantum winter” or dips in enthusiasm, but the overall trend is increasing as milestones are met. Indeed, governments and corporates remain committed for strategic reasons, even if some VCs become cautious. Sustained public funding (the $40 billion+ globally) acts as a stabilizer, keeping research going through the hype troughs. We often see big tech including the likes of IBM and Google coexisting with startups, and collaborating – Google investing in IonQ, or IBM partnering with European labs. Some consolidation has occurred (Honeywell + Cambridge to form Quantinuum, Rigetti acquiring QxBranch earlier, IonQ acquiring Entangled Networks in 2023), and more is likely as weaker startups get acquired by larger firms with deeper pockets. It’s telling that even behemoths like Amazon and Microsoft are open to hosting competitors’ tech on their clouds, it indicates the field is early and diverse enough that no single architecture has “won” yet. While the U.S. currently leads private quantum investment and research output, Europe’s recent surge in funding and China’s massive state investment could shift the centre of gravity. Policymakers worry about “quantum divide” between countries; this is spurring international projects to share knowledge, like EU-U.S. interoperability initiatives, or Canada partnering with France on quantum research. There is also a notable trend of talent migration, quantum PhDs and founders often move to where funding and facilities are, many top European researchers joined U.S. startups or faculty, Chinese students trained in U.S. labs then returned to spearhead Chinese programs, etc. Whichever regions can create a supportive environment for quantum talent will attract the brightest minds that ultimately drive breakthroughs. Another investor class emerging is industry end-users like banks, automakers, and pharma companies investing in quantum startups or projects. For instance, Volkswagen and Bosch have invested in quantum software startups (VW in D-Wave and Classiq, Bosch in Zapata) . HSBC, BMW, Merck and others have partnerships or internal teams for quantum. These companies aren’t investing for immediate return, but to ensure they have a seat at the table in shaping quantum applications for their industries – and to avoid being blindsided if a competitor gains a quantum advantage first. Their involvement is a sign that quantum computing is moving from pure science toward the realm of industrydriven innovation, albeit gradually. In all, the quantum computing “industry” is still in a formative stage, heavily underwritten by government support and speculative capital. But the presence of serious players, from Fortune 500 tech firms to well-funded startups, and the steady drumbeat of technical progress give investors and stakeholders confidence that a payoff will come. As one venture capitalist noted, “We have a chance to establish trillion-dollar quantum companies, but only by ensuring hardware, algorithms and interfaces serve real world use cases”. The next few years will be critical in demonstrating those first few use cases that justify the billions invested. If that happens, say a quantum computer that definitively beats classical supercomputers on a useful optimisation or simulation task, it could trigger a new rush of investment, dwarfing even the current levels. If not, some consolidation and reduced funding could occur until technical milestones catch up. Either way, the major players identified above are positioning themselves for the long haul, convinced that quantum computing will eventually become an essential pillar of the tech economy.
Does AI Accelerate the Quantum Revolution?
The rise of artificial intelligence and quantum computing is often discussed in tandem, as both are cutting-edge fields poised to redefine computing. A key question is whether AI can help quantum (and vice versa) – in other words, will advances in AI make it easier to build or use quantum computers, and will quantum computers supercharge AI? Many experts believe there is a synergistic relationship, though it may unfold in stages. On one hand, AI techniques are aiding quantum development. Designing and calibrating quantum hardware is extremely complex, there are many control parameters including laser frequencies, voltages, etc. that need tuning to keep qubits stable. Researchers have started using machine learning algorithms to optimise these controls more efficiently than humans. A deep learning can analyse qubit output patterns and adjust control pulses to reduce error rates. AI can also assist in quantum error correction schemes, given the daunting task of monitoring hundreds of thousands of physical qubits, smart algorithms are needed to predict and correct errors in real-time. Additionally, when searching for new quantum algorithms or materials, AI can comb through vast parameter spaces or even propose novel quantum circuit designs, an approach sometimes called “AI-designed quantum algorithms”. In short, AI serves as a tool in the quantum engineer’s toolkit, potentially accelerating R&D by automating tasks and discovering configurations humans might miss. As Dr. Liming Zhu of Australia’s CSIRO noted, progress in practical quantum applications can act as a “guidepost” shaping hardware trajectories, and leveraging AI will be key to refine those applications and hardware together. Conversely, quantum computing could eventually supercharge AI. AI, particularly machine learning, involves heavy computational lifting – optimising millions of parameters in a neural network or searching through combinatorial possibilities in generative models. Quantum algorithms might speed up certain subroutines of AI. A famous quantum algorithm can solve systems of linear equations, a core operation in many ML algorithms, exponentially faster in theory. Quantum computers might perform efficient sampling and search, which could help with probabilistic models and AI creativity. There is a nascent field of Quantum Machine Learning (QML) exploring these ideas. So far, no quantum algorithm has been proven to provide a significant advantage for real AI tasks like image recognition or language modelling, and skeptics point out classical methods and Moore’s Law have kept AI improving without quantum plus there’s the overhead of data loading into a quantum machine. However, the hope is that as quantum hardware grows, it could find patterns in data that classical AI might miss, or train certain models faster by escaping local minima through quantum parallelism. Quantum computers might be very good at generating high-dimensional feature mappings such as kernel methods or at simulating probabilistic models that classical computers handle by approximation. In the near term, the more concrete convergence is likely in hybrid quantum-classical algorithms for AI. A classical computer could handle the bulk of a neural network, but call on a small quantum processor to evaluate a kernel or sample from a difficult probability distribution as part of the training. Already, companies like Cambridge Quantum, now Quantinumm, have explored quantum-enhanced natural language processing algorithms, and startups like ProteinQure use quantum-inspired methods for drug discovery AI. A recent CSIRO study showed a quantum machine learning method that compressed and assessed a large dataset faster than classical methods could, in a test case for groundwater monitoring. The implication is that quantum techniques may improve AI’s ability to handle big, complex data, making machine learning models more robust or less data-hungry by leveraging quantum insights. Another aspect is that the AI boom is indirectly accelerating quantum by boosting investment and interest in computing innovation generally. The success of AI reminds stakeholders that paradigm-shifting tech can unlock enormous value, which makes them willing to fund the next paradigm, quantum, even if it’s uncertain. There’s also a talent crossover, many physicists and computer scientists attracted to quantum computing are also well-versed in AI, and vice versa, leading to fertile exchange of ideas, some quantum computing teams use AI to manage experiments; some AI research teams study quantum principles for inspiration. Tech giants are explicitly linking the two: such as Google’s Quantum AI division, or startups like SandboxAQ focusing on the intersection, wherein “AQ” stands for AI + Quantum. Looking further ahead, AI and quantum together could be revolutionary. Imagine by the 2030s, moderately powerful quantum computers available on the cloud routinely assist AI models – perhaps optimising complex models or encrypting their communications via quantum protocols. Meanwhile, AI helps orchestrate fleets of error-corrected qubits. The combination might enable breakthroughs such as AI-designed drugs tested via quantum simulations, or realtime optimisation of smart grids and traffic using quantum-enhanced solvers, or ultra-secure AI systems powered by quantum cryptography. Some futurists speculate about a cascade effect, quantum computing dramatically speeds up the training of AI models, which in turn accelerates scientific discovery, as suggested by Microsoft’s goal to “compress the next 200 years of science into decades” by combining AI and quantum. This could bring societal changes, rapid invention of new materials for climate tech, or personalised medicine via AI/quantum-crunched genomics, on a faster timeline than historically seen. However, a grounded view is that in the short run, AI will continue advancing largely on classical hardware (GPUs, TPUs), and quantum computing will advance largely through targeted research. The real fireworks of synergy likely require quantum computers reaching a certain threshold of power (error-corrected logical qubits, maybe in the hundreds), which is still perhaps a decade away. When that happens, we may see quantum-accelerated AI become a selling point of quantum cloud platforms – for instance, a machine learning task that took days might run in hours with a quantum subroutine. Until then, AI’s impact on quantum is more about enabling and inspiring – using AI to fine-tune qubits, and using the AI success story to motivate quantum funding. AI and quantum computing are on parallel tracks that are gradually converging. AI can accelerate quantum’s development by providing tools and motivation, while quantum, once mature, can accelerate AI’s capabilities by providing new computational horsepower and security features. Together, they are often seen as the twin pillars of the future of computing, one expanding what we can do with algorithms and data (AI), the other expanding the raw computational reach into new physical regimes (quantum). As both fields progress, their interplay is expected to deepen, potentially leading to compound benefits that change the technological landscape far more than either would alone.
Lessons from AI for Quantum Investment
The trajectory of quantum computing invites comparisons to the rise of artificial intelligence, although it would potentially overshadow it in the coming days – particularly how investment flows and expectations can get ahead of reality. Artificial intelligence, despite immense progress, has faced criticism that it hasn’t fully delivered on its massive investment, at least not immediately or evenly. Trillions have been invested in AI worldwide, from self-driving car ventures to virtual assistants, yet fully autonomous vehicles or human-level reasoning AIs remain elusive. Some early investors saw setbacks, think of the “AI winter” periods, and only recently has AI, especially deep learning, truly boomed into widespread deployment, ChatGPT-style models, and vision systems, after a long gestation. This history offers a cautionary tale, and perhaps a road map, for quantum computing. So could quantum follow the same pattern of over-investment and under-delivery in the short term, before ultimately coming good? It’s very possible. By traditional business metrics, quantum startups today have minuscule revenue relative to their valuations and funding. The expectations placed on quantum are huge, solving climate change, breaking encryption, revolutionising industries, and timelines are often optimistic to the point of marketing necessity. As mentioned, we’ve seen a bit of a correction, Quantum funding in 2023 halved compared to 2022 as initial hype gave way to the realisation that profits from quantum computing may be years away. Some publicly listed quantum firms saw their stock prices drop sharply in 2022–2023 (Rigetti, D-Wave, IonQ had volatile rides) as investors re-evaluated the road to monetisation. This mirrors AI’s trajectory, recall how heavily hyped self-driving car companies hit technical roadblocks and had to scale back promises, or how early chatbot makers overpromised general intelligence. Yet, also like AI, the strategic importance of quantum keeps money flowing despite setbacks. Governments won’t drop quantum funding because it’s tied to national security and long-term competitiveness even when short-term milestones slip. Large corporations treat it as R&D, not expecting immediate ROI but fearing to miss out long term. One trend in global investment tendencies is a growing tolerance for long-horizon “moonshot” investments. In a lowinterest rate environment, until recently, and with technology moving fast, investors have been willing to fund companies that might not have commercial products for a decade fusion energy startups, brain-computer interface companies, and quantum. This patience is partially driven by FOMO, fear of missing out on the next trillion-dollar platform. With AI, those who stuck it out, like Google investing in deep learning early, or Elon Musk pouring money into self-driving AI, are starting to see payoffs, albeit later than initially hoped. For quantum, the likely scenario is similar, significant economic returns will accrue, but not uniformly or immediately. It may take 10–15 years to see broad adoption, and even then, benefits may concentrate in certain sectors first, like pharmaceutical design, where quantum simulation could prove revolutionary, or in national security computing. Investors and analysts are trying to apply lessons from AI’s hype cycle to quantum, manage expectations, focus on intermediate milestones like achieving quantum advantage for a useful task, even if small-scale, and avoid overpromising timelines. Many quantum company CEOs now temper their language with acknowledgements of the challenges and the decade-plus journey ahead for full fault tolerance. Publications like Gartner’s Hype Cycle have placed quantum computing on the “Peak of Inflated Expectations” in recent years, with a “Trough of Disillusionment” possibly coming before the “Slope of Enlightenment.” That is a natural progression. The key is that unlike some overhyped tech that fizzles out entirely, quantum computing is underpinned by solid physics and demonstrable (if limited) success, so it is broadly expected to eventually reach the Plateau of Productivity, to continue the Gartner analogy, just on a longer timeline. Cross-pollination and pivoting can salvage investments. AI research from the 80s/90s that didn’t pan out immediately, like neural nets, roared back in the 2010s when compute caught up. Similarly, algorithms devised in early quantum computing research might lay dormant until hardware improves to run them. Companies might pivot, an example is DWave, which pivoted to offer hybrid solvers and to develop a gate-model approach after years of focusing solely on annealing, responding to market feedback. We might see quantum startups repurpose partial breakthroughs for nearerterm wins like Q-CTRL taking quantum control software and selling it for improving precision in other devices, or PsiQuantum using its photonic tech in ultra-secure communication links as a step before a full computer. AI taught investors that if Plan A doesn’t work immediately, Plan B might, and the knowledge gained is rarely wasted. What does this tell us about broader investment tendencies globally? It highlights a shift in how we value emerging technologies, more willingness to invest based on potential and strategic importance, not just immediate profit. In a world where controlling foundational tech such as AI, quantum, and biotech is seen as both lucrative and critical for national influence, investment dollars often behave as strategic capital. This sometimes leads to overshooting, too much money chasing too few viable ideas (hence some froth and eventual correction, but it also means truly world-changing innovations can be funded to fruition even if the journey is long. Quantum computing is benefiting from this mindset. There is a quasi-consensus among governments and industry that “we can’t afford not to invest in quantum,” because the downside of being left behind is unacceptable. So even when progress is slower than hoped, funding tends to persist with periodic rotations of which entity is doing the funding, if VC pull back in a given year, government grants or corporate programs often fill in. However, one must guard against irrational exuberance. If hype runs too far ahead, it could trigger a backlash where disillusionment cuts off funding prematurely. The quantum community is aware of this: it has been careful not to claim miracles that aren’t backed by data. After Google’s supremacy claim, IBM swiftly responded to dampen hyperbole; many researchers emphasise error correction challenges to avoid unrealistic expectations. In contrast, AI in the 2010s often saw bombastic claims, specific dates for full self-driving, that later hurt credibility. Quantum leaders seem to be learning that lesson, striving for transparency about progress and roadblocks. From a global investment perspective, one interesting tendency is how different regions reconcile hype and strategy. The U.S. and China, flush with capital and strategic intent, are willing to throw big money at quantum and absorb failures as part of the cost of doing business at the technological frontier. Europe, more cautious traditionally, is trying to adopt a more coordinated, patient capital approach, acknowledging that scale-up requires consistent funding and a tolerance for failure, which Europe historically lacked in tech, leading it to lose out on internet and AI booms to the U.S./China. Europe’s push to invest “€100 million per company” to scale quantum champions and its effort to unify resources across the EU is a reaction to past lessons where fragmentation led to underperformance. Globally, there’s also a trend of joint public-private investment models like Germany’s public funds matched by industry consortia to spread risk and reward. In essence, AI’s story has taught the world that transformative technologies may require sustained investment through long periods of R&D with uncertain payoff, but when they hit, the impact can be exponential, and missing out has high opportunity cost. Quantum computing appears to be following that high-risk/high-reward investment paradigm. As an investor quipped, quantum is a bit like oil exploration, you spend a lot and drill many wells, most come up dry, but one gusher can pay for all. The collective “gusher” in quantum would be a machine that clearly outperforms classical computers on valuable tasks. Once that is demonstrated, even at small scale, we can expect the next wave of quantum hype, but at that point it might be justified hype. The timing of that is uncertain, it could be a small advantage in a niche problem in 2–3 years, or a broader advantage in 5–10 years. Until then, the prudent approach, for both investors and policymakers, is to stay the course but remain grounded, funding fundamental advances and nurturing talent so that the field doesn’t collapse under unrealistic timelines. The world’s experience with AI, going from a curiosity to ubiquitous technology, suggests that those who invest through the hype trough often come out leaders when the technology matures. Quantum computing, by all accounts, is trending toward maturity in the 2030s. The bets being placed now globally will determine who reaps the rewards then. And like AI, those rewards could be substantial, in the form of new industries, productivity leaps, and solutions to pressing global challenges, but only if we navigate the intervening years of development with informed optimism rather than blind hype.
So, When Will Quantum Change the World?
Peering into the future, one finds a range of scenarios for how and when quantum computing will truly transform our world. Some experts are bullish, as noted, companies like IBM target 2029 for a usable large-scale quantum computer, and IonQ speaks of potentially cracking encryption by 2028 if all goes well. Others urge caution, suggesting it could be the 2030s or 2040s before quantum computers are mainstream tools for industry. What’s clear is that the trajectory is not if, but when and in what way. In a optimistic-yet-plausible scenario, by the late 2020s, we will likely see the first demonstrations of quantum advantage for practical problems, a quantum computer accurately simulating a complex chemical molecule that classical supercomputers cannot, leading to the discovery of a new drug or material. Around the same time, a quantum machine might solve an optimisation problem like a national power grid routing or a large portfolio optimisation with better quality or speed than classical solvers, marking a milestone in operations research. These achievements will probably require early error-corrected machines with on the order of 100–1000 logical qubits, or clever use of NISQ systems with algorithmic error mitigation. IBM’s plan to have around 200 logical qubits by 2029 and IonQ’s plan for ~1600 logical qubits by 2028 , if met, would support such breakthroughs. By the early to mid-2030s, we could enter the era of broad quantum utility. Governments might employ quantum computers for real-time cryptanalysis and signals intelligence, hence the rush to deploy post-quantum cryptography before then. Pharma and chemical companies might rely on quantum simulators for routine discovery pipelines, cutting years off R&D cycles for new compounds, fulfilling that vision of compressing “200 years of chemistry into maybe 20”. Finance firms could use quantum optimisation to dynamically rebalance portfolios or run risk scenarios overnight that previously took weeks. Artificial intelligence itself might be augmented by quantum sub-processors handling tasks like natural language query optimisation or complex pattern matching, it’s conceivable that the successor to today’s GPT models might use quantum circuits as part of its backend to achieve leaps in reasoning capabilities. If quantum error correction is mastered by then, quantum cloud services will flourish, offered by major providers like an ordinary part of computing infrastructure, abstracted away from end-users. Collectively, AI and quantum by the 2030s would start to deliver on the hype, AI providing cognitive capabilities and automation at massive scales, and quantum cracking the hardest computational problems underpinning science and logistics. Their combined effect could indeed be world-changing. Climate change mitigation could benefit, quantum computers might discover novel catalysts for carbon capture or more efficient battery chemistries, while AI manages smart grids and climate models. Together, they could also pose challenges, the workforce will need re-skilling as these technologies change job profiles, and ethical considerations like ensuring quantum-powered AI doesn’t get misused or that broken encryption doesn’t cause chaos will be paramount. By the 2040s and beyond, in a visionary scenario, fully mature quantum computers with millions of qubits might coexist with classical and neuromorphic computers as part of a rich computing fabric. At that stage, almost every industry could be impacted. Medicine might have personalised quantum-computed treatment plans; transportation systems might be globally optimised in real-time; national security will have quantum-secured communications as standard. We might even see consumer or edge quantum devices, perhaps quantum sensors in smartphones enabling new AR experiences or health diagnostics, quantum magnetometers, etc., which are actually likely earlier. In effect, quantum computing might become as ubiquitous and invisible as classical computing is today, embedded in the systems that run our world. Of course, this rosy timeline assumes steady progress and no fundamental roadblocks. There are still unknowns, will we hit a wall in scaling qubits due to noise or manufacturing yield? Can we get error correction to work without impractically high overhead, today it’s estimated you might need 1,000 physical qubits per logical qubit or more, the field is trying to reduce that? If a major obstacle emerges, these timelines could slide further out. It’s also possible that alternative approaches like analog quantum simulators or specialised quantum devices will deliver value sooner than fully universal quantum computers, and those might satisfy certain needs without requiring the whole problem of errorcorrected universal QC to be solved. If D-Wave’s annealers or Pasqal’s analog simulators consistently solve certain optimisation or simulation tasks better by 2025–2030, industries will adopt those, even if they’re not “universal” quantum computers. One more factor in “when” is competition and necessity, if global crises or competition intensify, that can accelerate timelines. The original space race put men on the moon faster than many thought because of intense competition and funding. A quantum equivalent might be if, say, there are signs one country is close to a code-breaking quantum computer, others might double-down and we’d see breakthrough efforts akin to the Manhattan Project or Apollo program for quantum. Already, the U.S. NSA and other agencies are quietly preparing for a “Y2Q,” the day quantum breaks encryption, by transitioning to quantum-resistant cryptography in the next few years, implying they think a large cryptography-breaking quantum computer could exist in the 2030s. Whether that’s optimistic or precautionary is debated, but it does drive urgency. In conclusion, while precise dates are impossible to guarantee, the consensus is that the next 5–10 years will move quantum computing from laboratory curiosity to a practical tool for some high-value problems, and the next 10–20 years could see it mature into a widely impactful technology on par with AI or classical computing in its breadth of influence. There will likely be no single “quantum moment” where everything changes overnight; rather, change will be incremental but accelerating. Today we’re already seeing modest but concrete achievements as detailed with Volkswagen’s traffic demo, etc., by 2030, we expect significant industry use in specific domains, and by 2040 perhaps routine use across domains. Each step will validate the investments and answer skeptics. And if AI’s recent explosion is any indicator, once the pieces, data, algorithms, and hardware, come together, the growth could be exponential and catch the world by surprise. We stand at a similar juncture with quantum computing as the early days of classical computing or the dawn of the internet, an inflection point. The science is largely proven; the engineering is fiendishly hard but making headway. The global commitment, funding, talent, policy, is unprecedented for a technology at this stage, which bodes well. As quantum and AI evolve together, they are likely to feed a virtuous cycle of innovation, quantum computers helping design better AI, and AI helping unlock the potential of quantum computers. For a business audience, the key takeaway is that quantum computing is transitioning from science fiction to an emerging business reality. Smart companies and countries are already preparing, experimenting with prototypes, developing quantum skills internally, and participating in the ecosystem. Just as those who embraced the internet or AI early reaped huge benefits, those who position themselves in the quantum value chain now, even if ROI is years out, stand to be the leaders in the post-AI, quantumenabled economy of the future. The coming decade will reveal whether quantum computing can carry forward the mantle of transformative technology that artificial intelligence has held. If it does, the global tech evolution will enter a new phase, one where the rules of computation themselves are fundamentally rewritten by the principles of quantum mechanics, and where the only limit to progress may be how fast we can harness those strange, powerful quantum laws for human advancement.