For the past few years, artificial intelligence has dominated the technology conversation. It has reshaped how organisations think about data, compute and automation, and it has accelerated demand for powerful infrastructure at an unprecedented pace.
But as AI matures and becomes embedded into everyday systems, another question is beginning to surface quietly across research labs, enterprises and governments alike: what comes after AI?
One possible answer is quantum computing. Not as a replacement for AI, and not as an overnight revolution, but as a fundamentally different approach to computation that could, over time, change what is possible altogether.
At Boston Limited, we spend our time working with technologies that sit well beyond the mainstream. Quantum fits naturally into that conversation; not because it is ready today, but because its trajectory mirrors many of the early patterns we have already seen with AI and high-performance computing.
In exploring what’s next, we’re proud partners of SECQAI, a UK-based pioneer building ultra-secure hardware and software aimed at making tomorrow’s compute both powerful and protected. SECQAI’s work spans post-quantum cryptography, quantum-inspired algorithms and hybrid quantum-AI models that address real-world challenges today, from optimising complex systems to advancing confidential computing, and they’re helping organisations prepare for a future where quantum and classical systems coexist.
Why Quantum Feels Harder to grasp than AI
AI is comparatively easy to explain. It is software-driven, trained on data, and runs on hardware we broadly understand - CPUs, GPUs and accelerators. Even when the models are complex, the idea of scaling compute to solve problems feels intuitive.
Quantum computing does not offer that same comfort. It is rooted in quantum mechanics, where particles can exist in multiple states at once and behave in ways that defy everyday logic. Explaining it accurately often comes at the expense of clarity, while simplifying it risks distortion.
This complexity has kept quantum firmly in the background while AI has taken centre stage. Yet behind the scenes, major technology vendors and research institutions are making steady progress, laying foundations that could support a very different kind of computing future.
A Reality Check... Quantum is Early, Fragile and Imperfect
Despite bold headlines, quantum computing remains firmly in its infancy. There are believed to be only a few hundred quantum computers worldwide, most of them housed in laboratories rather than data centres.
Unlike classical machines, quantum systems are extremely sensitive. Their building blocks - qubits - can be disrupted by tiny environmental changes such as light, heat or electromagnetic noise. Maintaining stable operating conditions requires extreme cooling, precision engineering and, in some cases, unconventional materials.
Errors are a defining challenge. In the same way that early AI systems were prone to unpredictable outputs, quantum machines struggle to preserve accuracy for long enough to complete complex calculations. This is not a flaw in implementation, but a fundamental limitation of working at the quantum level.
What matters is not that quantum is imperfect today, but that this stage is familiar. Every major shift in computing, from supercomputers to GPUs to modern AI accelerators, passed through a period where capability lagged behind ambition.
Where Quantum is Already Making an Impact
While large-scale, fault-tolerant quantum computers remain a work in progress, quantum technologies are already beginning to show practical value.
In healthcare and life sciences, quantum systems could dramatically accelerate drug discovery by modelling molecular interactions that are too complex for classical computers to handle efficiently. Tasks that currently take years of simulation may one day be reduced to hours or minutes.
Quantum sensing is further along the maturity curve. These sensors, which exploit quantum effects to measure time, gravity and magnetic fields with extreme precision, are already used in atomic clocks and experimental medical imaging. Their ability to operate accurately without requiring patients to remain perfectly still opens new possibilities for scanning young children and studying brain development in more natural conditions.
Navigation is another promising area. Quantum-based alternatives to GPS are being trialled to provide resilient positioning systems that work underground and are resistant to jamming or interference - a capability with clear implications for infrastructure, defence and financial systems that depend on precise timing.
Across energy, logistics and aerospace, quantum algorithms are also being explored to optimise complex systems, from balancing power grids in real time to loading aircraft more efficiently to reduce fuel consumption.
These are not abstract use cases. They are targeted solutions to problems that classical computing struggles to solve economically or at scale.
Quantum and AI are Complimentary, Not Competing
It is tempting to frame quantum computing as the successor to AI, but that comparison misses the point. AI is primarily software-led, driven by data and statistical learning. Quantum computing is physics-led, designed to explore problem spaces that are effectively unreachable using classical methods.
The more realistic future is hybrid. AI will continue to rely on classical and accelerated compute, while quantum systems may be used selectively to solve specific optimisation, simulation and cryptographic challenges.
This hybrid approach echoes what we already see in modern infrastructure design. No single architecture solves every problem. Instead, organisations combine CPUs, GPUs, specialised accelerators and high-speed networking to match workloads to the right platforms.
Quantum will eventually take its place within that ecosystem; not everywhere, but where it makes a measurable difference.
The Security Question no one can Ignore
One area where quantum’s long-term implications are already influencing decisions today is security.
It is widely accepted that sufficiently powerful quantum computers will be capable of breaking many forms of encryption currently used to protect sensitive data. This has given rise to the concept of ‘harvest now, decrypt later’, where encrypted information is collected today with the expectation that it can be decoded in the future.
The moment a fully operational, cryptographically relevant quantum computer exists (sometimes referred to as Q-day) is uncertain, but estimates often place it within the next decade. As a result, governments and technology providers are already working towards post-quantum cryptography to protect long-lived data.
The challenge is that encryption choices made now can persist for years. Waiting until quantum arrives may be too late.
Quantum as a long-term strategy, not a short-term bet
Quantum computing will not follow the same adoption curve as AI. It will not be consumer-facing, and it will not scale through software updates alone. Progress will be incremental, uneven and highly specialised.
Yet its potential is profound. Problems that would take the lifetime of the universe to solve on even the most powerful supercomputers could, in theory, be completed in seconds. That shift does not need to be immediate to be transformative.
At Boston, we view quantum as part of a longer conversation about advanced computing. Our role has always been to help organisations navigate emerging technologies pragmatically, understanding where they deliver value today, and where preparation matters more than immediate deployment.
Quantum may not be bigger than AI. But it may, in time, change the boundaries of what computing can achieve, and that makes it worth paying attention to now.