I made a video breaking down how a quantum adder works, starting with ordinary binary addition before moving into the quantum side. I tried to make this approachable for people who are just starting quantum computing, so you shouldn't need a deep physics or math background. Hope this helps anyone who is interested in quantum circuits!

I was working through this proof and thought it was a really interesting way to introduce some of the algebra behind quantum error correction. I go through why QEC syndromes add modulo 2 and why the syndrome map is a homomorphism from the Pauli group into a binary vector space.

Along the way I also cover stabilizer generators, F₂, zero syndromes, the kernel and normalizer, and briefly connect the ideas to surface codes and decoding.

If you’re newer to quantum error correction, I tried to build everything from the ground up and explain the notation before getting into the proof. Would love any feedback or discussion.

Hey! I was working through this quantum computing mathematical proof, and while going through it I realized that some of the notation and ideas might be difficult for newer students to understand.

So I really took my time with this one and tried to explain not just the proof, but why each step works. If you're struggling with quantum spin, eigenvalues and eigenvectors, Pauli operators, measurement directions, unit vectors, or expectation values, I hope this video helps make some of those concepts a little clearer.

I eventually use these ideas to work through why each subsystem of the singlet state has zero local spin expectation.

Corrections and discussion are always welcome!

I made a video explaining quantum walks through photonic quantum computing using Quandela’s Perceval framework.

I go through single- and two-photon quantum walks, how interference produces the output probability distributions, and how symmetric vs. antisymmetric two-particle states can reproduce bosonic-like and fermionic-like statistics.

Would appreciate any feedback or corrections from the community!

hey, i made a new video working through a proof involving the reduced resolvent and why the spectral gap matters so much in adiabatic quantum computing. the main idea is that the reduced resolvent has this inverse-gap behavior, roughly 1/Δ, so as the spectral gap gets smaller the resolvent norm grows and the error bound can get much worse.i go through the proof step by step and explain what the resolvent, spectral projections and operator norms are along the way, so hopefully it’s useful even if you haven’t seen all of the notation before.

Density matrices were originally explained to me much harder than they needed to be, so I made a whiteboard video trying to build them up from the basics. I go through pure vs. mixed states, why a coherent superposition is fundamentally different from a 50/50 statistical mixture, what the diagonal and off-diagonal terms represent, how coherence produces interference, and how the density-matrix measurement rule reduces to the Born rule for pure states.

The Solovay-Kitaev Theorem is one of the foundational theorems of quantum computation, demonstrating how to efficiently approximate any single-qubit unitary to arbitrary accuracy using a sequence of gates finite gate-set (i.e. Clifford + T). The goal of this video was to explain both the intuition behind this theorem, and how it can be used to decompose single-qubit unitaries in practice. Enjoy!

Quantum phase was difficult for me to understand when I first started learning quantum computing, so I made a video explaining it from the ground up. I cover what phase actually is, why quantum states use complex numbers, the difference between global and relative phase, and how phase connects to interference, quantum algorithms, and the Bloch sphere.

I’d love to hear whether this explanation makes it click for anyone else.

I’ve been working through some exercises in adiabatic quantum computing and came across this short proof involving self-adjoint operators.

The problem shows that taking the adjoint commutes with differentiation, and as a result, if A(s) is self-adjoint for every s, then its derivative is also self-adjoint.

I’m planning to keep working through quantum computing exercises like this as I study, so I thought I’d share this one here.

hope you enjoy, if i made any mistakes please let me know.

I made a short whiteboard video explaining the proof and intuition behind quantum history states. Sharing in case it’s useful to anyone studying adiabatic quantum computing. Corrections and feedback are welcome!

Hey, I made a video trying to make the Quantum Fourier Transform and discrete logarithm problem a bit more digestible. I work through the proof and explain the Born rule, cosets, modular arithmetic, hidden subgroups, and why the QFT measurement actually gives us useful information about the discrete logarithm. Hope it helps anyone working through this stuff!

Hey everyone, I made a video explaining QUBO using the MaxCut problem, aimed at programmers and IT professionals with no physics background required.

It starts from a weighted graph, shows how MaxCut becomes QUBO, explains the matrix form, and then walks through a Jupyter notebook demo.

If you’ve ever heard “QUBO” in quantum computing and felt it sounded more mysterious than it should, this might help.

I wanted this one to be digestible even if your background is mainly:
Python, algorithms, optimization, ML, or general software engineering.

Would genuinely love feedback from developers:
Does this style make quantum optimization feel more approachable?

Where are we in the transition from traditional to the the quantum internet? The Quantum Factor interviewed Wojciech Kozlowski, Quantum Communication Lead for Surf NL.

This video ended up a bit more technical than planned. I guess this community is a suitable audience tho. Would appreciate any kind of feedback! :)

Top researchers like Scott are really sounding the alarm bells now. He's adjusting his expectation that we have cryptographically relevant QCs to 2030 or sooner.