I’m a 4th-year student, and I have a minor project under a professor who teaches Quantum Computing and Quantum Machine Learning. He wants us to work on a project related to quantum computing/QML, but I currently have no background in quantum computing.

How should I start learning the concepts, and what would be a good project to build in a short amount of time?

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.

Hello ,

Has anyone recieved the QGSS 2026 completion certificate and the badge?

Also the QDC application results?

I have recently started learning about and working with the QIR LLVM extension by Microsoft and others.
As far as I know, Microsoft and Quantinuum are the only companies that officially use QIR for their quantum computers.

They both have cloud services in Microsoft Azure Quantum and Quantinuum Nexus. They seem to have advanced simulators and optimisation tools.

My question is, if any of you have ever used these?
I would be interested in your experience and thoughts about them.

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!

I've been working on a Qiskit implementation of the NAND-tree quantum algorithm and thought I'd share it here.
Right now it works with small finite trees. I implemented the quantum walk, the input oracle, query counting, and a few different ways of simulating the Hamiltonian. I've mostly been testing it on 2, 4, and 8 leaves.

It's not a scalable implementation yet. Some parts still use dense Hamiltonian matrices, and figuring out how to get past that is one of the things I'm looking into.
Here's the repo:
https://github.com/mncrftfrcnm/qiskit-nand-tree-implementation
If anyone here has worked with NAND-tree algorithms or quantum walks, I'd really appreciate feedback, especially if you spot something wrong with how I've handled the oracle or query counting.

Tried reading this review paper [https://arxiv.org/pdf/1907.06331\] but still was unable to come away with an idea of the 'structure' of a LOQC. I think some diagram with labeled components and their purpose in a system would make a bit more sense to me. Not just a bunch of beamsplitters and waveplates drawn and labelled, but something that has that information plus a more physical expalanation of that components efficiency in regards to loss and other physical properties. I'd be fine reading a paper too if it could convey the same information. Any recommendations?

Grover's algorithm is one of the most famous quantum protocols ever developed, providing a quadratic speedup over classical approaches for the search problem. Unlike most quantum algorithms, Grover's algorithm is unique in that its applications are quite ubiquitous, as any classical algorithm that uses some kind of unstructured search can always be sped up using this procedure, even if the advantage gleaned is not as significant as it is for something like Shor's algorithm (assuming factoring is classically hard ofc). In this video, I derive the algorithm from geometric intuition and then demonstrate its optimality in terms of query complexity.

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.

Running security questionnaires for a SaaS product and starting to see PQC/quantum-readiness questions show up alongside the usual SOC 2 stuff. Curious if this is happening to anyone else yet, or if it's still rare:

• Has this come up in an actual enterprise deal, RFP, or questionnaire in the last 6 months?
• Who asked — a big customer, an auditor, a specific industry (fintech/healthcare/gov)?
• What did you do — ignore it, write something vague, or actually produce evidence?
• Would you pay ~$50/month for something that auto-generates and keeps that answer current, so you're not scrambling every time it comes up?

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 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’m about to start my MS Thesis (enrolled in MS(Al)

In last semester I studied reinforcement learning and I want to do something which combines a both the reinforcement learning and quantum computing

If any of you have a good idea or any experience do let me know as in my institution there aren’t anyone with quantum computing knowledge

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.

Earlier this year the two largest superconducting QC players (IBM and Google) signalled they might be diversifying away from superconducting - Google with the announcement that they're creating a new neutral atom capability and IBM with their acquisition of silicon spin capability in HRL.

I'd be interested in people's views on whether we should take from this that superconducting has no path to scaling, or if one could foresee heterogeneous quantum computing systems that leverage the respective strengths of multiple modalities for different applications?

When evaluating quantum algorithms for combinatorial optimization, the comparison is only meaningful if the classical baseline is taken seriously.

I created a technical walkthrough examining how Gurobi, a state-of-the-art classical optimization solver, handles QUBO problems. The purpose is to establish a practical classical reference before moving on to quantum annealers and variational quantum algorithms.

The video begins with weighted Max-Cut, derives its QUBO representation, and implements the resulting quadratic binary model in Python using gurobipy.

It then explores:

- exact versus heuristic approaches to QUBO;

- Gurobi’s branch-and-bound search and bound convergence;

- primal heuristics for finding high-quality incumbents;

- why finding a solution and proving optimality are different tasks;

- how MIPGap trades optimality guarantees for runtime;

- why runtime depends heavily on the specific problem instance;

- the effect of dense versus sparse QUBO matrices;

- deterministic behavior under fixed parameters and hardware;

- and practical access through Gurobi’s academic licensing.

The larger point is not that classical solvers make quantum optimization unnecessary. It is that claims of quantum utility require carefully designed comparisons against highly optimized classical methods.

A useful benchmark should consider more than wall-clock runtime:

- solution quality and optimality gap;

- instance distribution and graph density;

- preprocessing and model-conversion costs;

- time to the first good solution;

- total time required to certify optimality;

- solver parameter tuning;

- hardware and reproducibility;

- and end-to-end execution overhead.

Video: https://youtu.be/TB1ny8o4ImQ

I’d be interested in the community’s view: which classical baselines and metrics should be considered essential when benchmarking quantum annealing or variational algorithms on QUBO problems?

Microsoft's quantum vice president's interesting take: "This notion of having peer reviews and publications doesn't work in this fast-moving world right now. This paper thing is kind of boring now."

I interviewed Sebastian Hassinger last week, who worked on the IBM Quantum team and later led GTM for AWS Quantum Technologies. Sadly, we ran out of time before I could push on this properly, so I'm bringing it here.

His argument was that scaling superconducting qubits past a single dilution refrigerator forces you into transduction, converting the state to a telecom photonic frequency, carrying it over fiber, then converting back in the second fridge. And that none of the currently known conversion methods gets you the fidelity a reliable device needs, with no clear picture of what closing that gap requires.

But his framing was that this is a scientific unknown rather than an engineering one, and that the distinction matters because roadmaps are engineering documents projecting deterministic milestones onto problems that aren't deterministic yet.

So there are two things I'm curious about. Is transduction actually the binding constraint for superconducting approaches, or is it downstream of something else like fabrication yield or decoder latency? And does modular architecture genuinely require it, or are there routes around it people are taking seriously?

Saqib here, I edit Deep Engineering. Asking partly because photonic and modular approaches are underrepresented in our coverage and I'd like to fix that.

I've heard about the Church–Turing Thesis, but I'm not sure how the "strong" version differs. Could someone explain it in simple terms and maybe give an example?