Two-track view of quantum optimisation: practical, quantum-inspired problem framing on the left; real quantum hardware constraints on the right.
Over the last few months I’ve been working on quantum optimisation, deliberately before quantum advantage exists.
The Question Everyone Skips
Most conversations about quantum computing jump straight to hardware timelines or speculative speedups. I think that skips the harder question: how do we even know which business problems are worth putting on quantum hardware when it’s ready?
Two Intermingled Tracks
I’ve been thinking about this through two intermingled, but distinct, tracks:
The first is quantum-inspired problem framing.
Things like travelling-salesman problems, min/max-cuts, and optimisation landscapes aren’t just maths problems, they’re ways of seeing coordination, trade-offs, and constraint tension more clearly. You can get real value from this today, even on classical machines.
The second is quantum hardware reality (i'm using IBM's Qiskit).
Bell / CHSH, VQE, QAOA - not as buzzwords, but as hybrid workflows dominated by noise, measurement cost, and very real physical limits. Until you’ve paid the full price of expectation estimation on hardware, it’s hard to appreciate why advantage is rare.
Where These Two Worlds Might Converge, And Where They Won’t
Over the next few weeks I’m going to work through both tracks in parallel:
where quantum-inspired thinking genuinely helps business problems
what actually runs on today’s quantum hardware, and
where (precisely) those two worlds might converge, and where they clearly won’t.
If you’re interested in optimisation, systems, or how frontier tech actually shows up in practice, you might enjoy following along.
#QuantumComputing #QuantumOptimization #SystemsThinking #BusinessOptimization #ComplexSystems #DecisionMaking #EmergingTechnology