Edward Farhi invented two of the most-studied algorithms in quantum computing. Both aim at optimisation, which is also the application the industry most often promises. He built the quantum adiabatic algorithm in 2000 and QAOA in 2014. Between them they shaped how the field thinks about hard combinatorial problems. What makes Farhi the right person to profile is not just that he built these tools, but that he has been among the clearest voices on their limits. He came to quantum computing after a full career in particle physics, and he brought with him a physicist’s insistence on knowing exactly what has been proved and what has merely been hoped. On the question the whole field turns on, whether quantum optimisation actually beats the best classical methods, the inventor of the leading algorithm is notably careful. Farhi built two landmark quantum algorithms. The adiabatic algorithm came in 2000 and QAOA in 2014. Both aim at approximate optimisation, the application quantum computing most often promises. He came from particle physics. As a student he introduced thrust as a QCD observable, a variable still used at the Large Hadron Collider, and he co-created the Farhi-Susskind technicolor model before turning to quantum computing. The adiabatic algorithm computes by staying in the ground state. Start in an easy state, deform the problem slowly, and the answer is where you end up, provided the change is slow enough. QAOA is its near-term cousin. It chops the smooth adiabatic evolution into a few tunable layers a noisy gate machine can run, which is why it is the most-run optimisation algorithm on real hardware. Neither has a proven advantage. Whether quantum optimisation beats the best classical methods on a useful problem remains open, and Farhi’s own group has published some of the sharpest results on where it