I started this series knowing nothing about quantum computing — no physics degree, no quantum mechanics background. Just curiosity, linear algebra, and a willingness to work through the math with AI as a thinking partner.

The goal is ambitious: 14 parts, from "what is a qubit" to designing original quantum algorithms. Every equation derived from scratch. Every algorithm traced gate by gate. Working code throughout.

Part 1 is done — Grover's search algorithm, built completely from first principles: - Qubits as vectors, gates as matrices, no hand-waving - Full oracle circuit derived step by step (all 8 superposition terms traced through every gate) - Phase kickback explained from the math up - Diffusion operator worked out gate by gate - Run on IBM's ibm_fez quantum processor — 94.5% on simulator, 57.5% on real hardware, with an explanation of exactly why the gap exists

https://darshanfofadiya.com/zero-to-quantum/grovers-search

The Colab notebook includes one-click access to IBM's real quantum hardware — free account, no credit card required, 10 minutes of QPU time per month. You can run Grover's algorithm on an actual superconducting quantum processor yourself in about 15 minutes.

The remaining 13 parts cover entanglement, QFT, Shor's algorithm, QPE, VQE, QAOA, quantum error correction, and ultimately designing your own algorithm from scratch.

My questions for HN: 1. Is this the right depth and sequence for genuinely understanding quantum computing — or am I missing something foundational? 2. What would you add, cut, or reorder in the curriculum? 3. For anyone who works in quantum: what do you wish you'd understood earlier that most tutorials skip?

I'd rather get hard feedback now on Part 1 than write 13 more parts in the wrong direction.