Advanced Computing
Quantum computing, neuromorphic chips, photonic computing, DNA storage, and analog architectures.
Recommended Prerequisites
These are recommendations - you can start this track at any time.
About this track
Quantum computing, neuromorphic chips, photonic computing, DNA storage, and analog architectures.
The Advanced Computing track collects 6 modules and 24 lessons into a single ordered path. Each module ends with a checkpoint quiz; passing the checkpoint unlocks the next module so you can track your progress without guessing whether the material has stuck.
You start with "Quantum Computing Fundamentals" and finish on "Analog & In-Memory Computing". The whole track sits inside the Specialist area of the curriculum, so the writing assumes the prerequisites listed above and skips ground that an earlier track has already covered.
What this track covers
- Quantum Computing Fundamentals — Step beyond classical bits into the world of qubits. Explore superposition, entanglement, quantum gates, and the algorithms that promise exponential speedups for specific problems.
- Near-Memory Computing & Processing-in-Memory — Confront the memory wall head-on. Learn how moving computation closer to data - through HBM-PIM, UPMEM, and other architectures - is reshaping everything from AI acceleration to genomics.
- Neuromorphic Computing — Discover how chips inspired by biological brains process information using spikes instead of clock cycles. Explore Intel Loihi, IBM TrueNorth, spiking neural networks, and event-driven computation.
- Optical & Photonic Computing — Explore computing at the speed of light. Learn how photonic integrated circuits, silicon photonics, and optical neural networks use photons instead of electrons to perform computation with unprecedented speed and energy efficiency.
- DNA & Molecular Computing — Explore computing at the molecular scale. Learn how DNA strands can store data at extraordinary density, perform logic through strand displacement, and solve combinatorial problems through massive parallelism.
- Analog & In-Memory Computing — Rediscover the power of continuous signals. Learn how memristors, crossbar arrays, and analog AI accelerators perform matrix multiplication in memory using physics itself, and how hybrid analog-digital designs are reshaping AI hardware.
How the track works
Every lesson is a short page with diagrams, runnable examples, and the kind of edge-case footnotes you usually only find in textbooks. Where it makes sense, the lesson is paired with a CPU simulation or a coding challenge so you can poke at the idea instead of just reading about it.
The lessons themselves are free to read with a free account. The 6 checkpoint quizzes that gate the next module are also free, as are the lesson IDE and CPU simulations. Optional 777-tier tools — the step-through debugger, decompiler, ROP gadget builder, heap visualiser, and exploit labs — sit alongside the lessons but are not required to follow the track from start to finish.
You can jump in at any point. Be Bitwise is built around a free forever curriculum, with no time limits and no expiring access. Pick the next lesson when you have an hour; come back when you do not.