Knowledge base, experiments, and Q&A

CS-first, NISQ-aware learning journey

Quantum Computing — Learning Overview

This section documents my structured learning journey in quantum computing—from core concepts and circuits to algorithms, real hardware constraints, and practical experiments. I focus on a computer-science perspective: how quantum computation is represented, programmed, tested, and limited on today’s noisy (NISQ) devices.

Why I Started Learning Quantum Computing

I started in a very hands-on engineering environment—building interactive systems that connect software to hardware: sensors, controllers, timing systems, and real-time feedback loops. Working close to hardware made me curious about what “next-generation computing hardware” looks like when the rules change at the physical level. That curiosity led me into quantum computing—not through physics-heavy theory first, but through practical computing questions: how do we represent information, how do we program it, and what breaks on real devices?

How I’m Learning (Course + Practice)

To learn in a structured way, I enrolled in a quantum computing course offered by Packt on Coursera and I build alongside the material. I also cross-check concepts and APIs using official documentation and examples, especially from IBM’s Qiskit documentation and community resources.

What You’ll Find in This Learning Section

This learning area is organized as a set of topic pages. Each page includes:

• Clear explanations (CS-first; no heavy physics/maths unless needed)
• Implementation notes (what worked, what didn’t, and why)
• Updated code (modernized APIs where necessary)
• Practical perspective (simulation vs real hardware, noise and errors)

Q&A Pages (My Questions + My Answers)

Each topic also has a dedicated Q&A page containing the questions I genuinely had while learning, the best answers I found, and short clarification notes. This is meant to show the learning process—not just the final summary.

A Note on Scope

This section is intentionally not a physics deep-dive. The goal is to build intuition for circuits, measurement, algorithmic speedups, and today’s hardware limits—especially noise, calibration effects, and decoherence.