Quantum algorithms,
explained properly.
A place to learn quantum algorithms properly — interactive simulations you can push around, plain-language explanations, and honest notes on where quantum computing might eventually matter versus where the claims currently outrun the evidence.
Three problem families worth watching
Quantum advantage is not general-purpose. It shows up in narrow places with the right structure — rugged optimisation landscapes, and systems that are themselves quantum. These three are where I think the case is strongest, and they are what I am reading toward. To be clear: this is where my interest points, not a description of work I am currently doing.
Combinatorial optimisation
Scheduling, routing and resource allocation are NP-hard and turn up everywhere in industry. Ising and QUBO encodings turn them into something a quantum device can attack — though the classical solvers they would have to beat are very good.
Materials discovery
Electronic structure is a natively quantum problem. Variational eigensolvers estimate ground-state energies of lattice and molecular Hamiltonians that scale badly classically.
Molecular simulation
Binding affinity and conformational search for drug candidates. Today this means small active-site models and hybrid quantum–classical pipelines, not whole proteins.
Quantum computing, visually
Seven short lessons covering qubits, superposition, phase, interference, entanglement, measurement, and what a quantum algorithm actually is. Every concept has a live simulation you can drag, not a static diagram.
Open the course → Sandbox · PuzzlesThe quantum playground
Build circuits gate by gate and watch the state respond in real time. Make a Bell pair, break interference, cheat at a coin flip. Nine puzzles, from "make a superposition" to "build a GHZ state".
Start playing → Reading listBooks worth your time
A short, opinionated shelf — sorted by where you actually are, not by title. Each entry says who it is for, so you don't end up in a graduate text on day one and conclude the field isn't for you.
See the shelf →Key papers
Founder
Amplitwist is one person learning in public, not a company. Saying so plainly seems better than hiding behind a corporate "we".
Where the field actually is
Today's quantum hardware is noisy and small. For nearly every industrial problem, a good classical algorithm still wins — and saying otherwise does the field no favours. What is worth doing now is finding which problem structures might eventually invert that, building the mappings, and measuring honestly against the best classical baseline available. Anything on this site that sounds more confident than that is a mistake — tell me.