How quantum computing will help AI deliver
As companies globally are looking to scale their AI ambitions, we're hitting a critical inflection point: the limitations of classical computing architecture will become a clear bottleneck for AI adoption at scale.
In recent conversations with semiconductor and advanced tech leaders, it's become obvious that quantum computing is rapidly shifting from future tech to an essential enabler for next-generation AI capabilities. Having spent the last few weeks talking to those involved in the semiconductor and advanced technology ecosystem and reading a number of industry sources, here are a few things that came up.
🔹 Processing Power: We're hitting fundamental limits of silicon. Tasks that would take classical supercomputers years to solve are becoming achievable in seconds with quantum systems.
🔹 Complex Problem Solving: Current AI systems, even with massive GPU clusters, struggle with optimisation problems critical for supply chains, R&D projects, and financial risk modelling.
🔹 Simulation Capabilities: Pharma R&D leaders are looking at how quantum simulation of molecular structures could compress drug discovery timelines from 8+ years to 18-24 months.
The venture ecosystem gets this - quantum computing investments hit £1.4B in 2023. By 2030, we're looking at 5,000+ operational quantum systems globally. Companies in financial services and advanced materials are already building quantum-ready teams, and national governments have significantly increased investment in this area.
This convergence of quantum and AI creates unprecedented talent challenges. Beyond quantum engineers, organisations need to develop hybrid teams that can bridge classical, quantum, and AI domains and hire commercially minded leaders to help bring the technology to the mainstream.
If you want to discuss how this impacts your organisation's quantum strategy, contact Kalpesh Baxi to explore how Critical Talent Solutions can help you build the right team.
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