u/dogpup3

Hey everyone! I recently made a video explaining Choi matrices, complete positivity, and quantum channels.

If you're studying linear algebra and are curious about how concepts like eigenvalues, positive semidefinite matrices, tensor products, and the trace are used in quantum computing, I thought this might be interesting!

I work through several examples step by step, including how a matrix can have a negative eigenvalue and what that tells us about whether a quantum operation is physically valid.

Thought I'd share since it's a nice real-world application of linear algebra!

Happy to hear any feedback or suggestions!

I made a video explaining Majorana qubits from a topological perspective. Majorana zero modes, fermion parity, how the qubit is encoded, and how braiding can perform quantum operations.

Hope this helps anyone interested in understanding the basic idea behind the approach Microsoft is pursuing with topological quantum computing!

Hope this helps! Also explained partial trace and open and closed evolution and how it helps us understand quantum noise and error correction

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.

if youre unfamiliar with superconducting qubits, i made a video going over how they work. from a classical LC circuit to a transmon qubit. I made this for people who have seen superconducting quantum computers and wondered how they actaully work. please leave feedback as hardware is not my main focus so i am not perfect at it, but i tried my best to explain.

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 made a short video working through a quantum computing exercise on measuring quantum states. Although the problem comes from quantum computing, the solution relies heavily on linear algebra—vectors, inner and outer products, projectors, Hermitian operators, and basis measurements.

I thought it might be an interesting example of how these linear algebra concepts show up in quantum computing.

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!

This proof in quantum error correction is full of linear algebra, so I thought I’d share it here. Along the way, I use the image and kernel of linear maps, rank–nullity, linear independence, and parity-check matrices to show how these ideas are applied to error detection.

I made a short whiteboard video explaining density matrices from the ground up, focusing on the linear algebra behind them: pure vs. mixed states, diagonal vs. off-diagonal entries, coherence, projectors, and how measurement probabilities arise from the matrix representation.

Sharing in case it’s useful to anyone interested in how linear algebra shows up in quantum mechanics. Corrections or additional insight are always welcome.

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, if you’re interested in getting used to QuTiP, I went through one of the simulations from their site and also explained the quantum physics behind everything and what all the parameters mean. I think it’s a great place to start if you’re new to QuTiP, and I included all the links I used as well! :)

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

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.

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!

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 a proof showing that each subsystem of the quantum singlet state has zero local spin expectation in every direction.

While going through it, I realized how much of the proof is really linear algebra underneath the quantum notation. So I took my time explaining the eigenvalues and eigenvectors, unit vectors, Pauli operators, tensor products, inner products, and expectation values behind the calculation.

If you're learning linear algebra and want to see how these concepts show up in quantum mechanics, I hope this helps!

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.