Quantum & AI
in
practice
Aideskblog runs hands-on workshops in quantum computing and AI for participants across every state — people who want to build real skills, not just follow a lecture.
Who this is for
This platform has a specific kind of participant in mind
Not everyone who signs up benefits equally — and that is worth being direct about.
- You have at least 6 months of programming experience and can read Python code without help
- You are curious about how quantum gates and AI inference actually work at the implementation level
- You have 4 to 8 hours per week to spend on assignments — not just watching, but building
- You are comfortable working independently between live sessions, using tools like Claude AI to check your reasoning
- You want to discuss your work with other participants, not just submit it and wait for a grade
If you are looking for a general survey course or a certificate to put on a resume without doing the work, this is probably not the right fit. The workshops are designed around doing, and they take time.
What makes the structure different
Most online learning puts content first and practice second. Here it is the other way around.
Each workshop opens with a problem you cannot yet solve. The reading, the demonstrations, and the discussion all exist to get you to a working solution. By the time you finish an assignment, you have used the concept — not just read about it. Assignments are designed around real tools: Qiskit for quantum circuits, PyTorch for neural architectures, and Claude AI as a reasoning partner when you get stuck on a logic problem. The collaborative element is not optional. Participants share partial solutions, challenge each other's approaches, and explain their thinking in peer review sessions. That friction is part of the learning.
Problem-first sequencing
Every module starts with a challenge that requires the new concept to solve. Theory follows the problem, not the other way around.
Peer review built in
Participants review each other's submitted work every week. Reading someone else's circuit implementation teaches you things a rubric cannot.
Location-independent access
All sessions are remote and asynchronous-compatible. Participants from rural Montana and downtown Baltimore work through the same material on the same schedule.
After completing the program
What becomes possible
Finishing a workshop here does not guarantee anything — but it does leave you with specific, demonstrable capabilities.
The people who built and run this
Aideskblog was put together in 2018 by practitioners who were frustrated with courses that taught theory without ever touching a real implementation. The instructors here work in the field — they are not translating someone else's curriculum.
Orin Vaszary
Quantum Systems Lead
Orin spent eight years building quantum error correction models at a national laboratory before moving into education. He designs the quantum computing track and runs the weekly implementation review sessions. His approach is to find the simplest version of a hard concept that still requires you to understand it.
Delia Marchetti-Foust
AI Curriculum Architect
Delia has built production AI systems for logistics and materials science applications. She leads the AI track, with particular focus on how large language models like Claude interact with structured reasoning tasks. She wrote the peer review framework that the platform runs on.
Staying current with what the field is actually doing
The curriculum is revised every quarter — not because of a policy, but because the field moves fast enough that a module from 18 months ago may already describe a deprecated approach.
When IBM releases a new quantum processor architecture or Anthropic updates Claude's reasoning capabilities, those changes show up in the next workshop cycle. Participants work with the same tools and face the same constraints that researchers and engineers are dealing with right now. That means some assignments get harder when the underlying technology shifts — and that is intentional. The workshops are connected to an active practitioner network across the United States. Instructors bring in problems they are currently working on, and participants occasionally contribute to real research questions rather than synthetic exercises. The platform is not a snapshot of what quantum computing and AI looked like when someone wrote a textbook. It reflects what practitioners are building and debating today.