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# Schroedinger's cat: Thinking in- and outside the box of quantum mechanics

How do you debug code when observing it alters the outcome? Discover the quantum physics, logic gates, and error correction strategies needed to program real quantum hardware today.

- **Speakers:** Alexandra Waldherr
- **Event:** World Congress 2021
- **Published:** July 1, 2021
- **Duration:** 43:21
- **URL:** https://www.wearedevelopers.com/videos/216-schroedinger-s-cat-thinking-in-and-outside-the-box-of-quantum-mechanics

## Summary

Understanding quantum computing requires a leap from classical binary logic into the probabilistic physics of superposition, entanglement, and wave-particle duality. The famous Schrödinger's cat thought experiment illustrates the reality of quantum systems: a particle exists in multiple states simultaneously until measured. This fragile wave function collapse creates distinct challenges for software engineers, as debugging an unobserved quantum state fundamentally alters its outcome, forcing developers to rely on constructive interference to amplify correct computation paths while canceling out errors.

To transition from theoretical physics to actionable code, structural foundations in linear algebra, complex numbers, and statistical probability are essential. Quantum logic gates, such as Hadamard and CNOT, function as complex matrices that evolve prepared quantum state vectors over time. Engineers can begin tinkering with real quantum hardware and simulators using platforms like the IBM Quantum Experience or Google's OpenFermion. These tools are already being leveraged for variational quantum eigensolvers (VQE) to simulate complex molecular interactions, such as modeling ammonia production to optimize industrial chemical processes and understand nitrogen fixation.

Current hardware architectures present diverse engineering challenges, ranging from superconducting circuits controlled by microwave pulses in ultra-cryogenic environments to more thermally stable ion traps manipulated via lasers. As the hardware scaling matures, practical enterprise applications are emerging in secure quantum cryptography, search optimization, and hybrid solutions utilizing the TensorFlow Quantum framework for machine learning. A critical operational takeaway for developers entering the field is the necessity of quantum error correction; stabilizing computation currently requires aggregating dozens of volatile physical qubits to yield a single, reliable logical qubit.

**Keywords:** quantum computing basics, superposition and entanglement, wave-particle duality, quantum wave function collapse, quantum logic gates, linear algebra matrices, ibm quantum experience, google openfermion, variational quantum eigensolvers, quantum error correction, physical vs logical qubits, superconducting circuit hardware, ion trap computers, quantum cryptography transmission, tensorflow quantum framework, molecular simulation algorithms, hybrid machine learning, shor algorithm implementation

## Chapters

1. **Core concepts of Schrödinger's cat and quantum mechanics** (00:02) — Understanding the dual nature of quantum states reveals why measuring a radioactive particle forces it into a single definitive outcome.
1. **Fundamental physics principles enabling quantum computation** (02:52) — Physics properties like superposition, entanglement, and wave-particle duality allow quantum computers to process complex configurations in parallel.
1. **Mathematical foundations and matrix operations for quantum gates** (06:28) — Applying probability concepts and linear algebra equations accurately models the evolution of quantum states across logic gates.
1. **Exploring the IBM quantum computing cloud platform and simulators** (09:50) — Connecting to cloud-based simulators enables practical experimentation with quantum gates to map superposition results across probability distributions.
1. **Quantum algorithm classes for computation speed and chemical simulation** (13:00) — Shor's and Grover's search algorithms drastically reduce computational latency, while variational solvers accurately estimate complex molecular energy states.
1. **Implementing chemical models using OpenFermion and Cirq frameworks** (17:01) — Translating chemical molecular structures into functional creation operators bridges the gap between biological simulation and quantum circuit execution.
1. **Comparing quantum hardware technologies and their physical architectures** (19:59) — Architectural designs relying on ion traps, superconducting circuits, and photonic components offer differing stability advantages for scalable physical qubits.
1. **Accessing quantum hardware through cloud providers and platforms** (22:23) — Evaluating tiered cloud platforms reveals varying noise levels and availability limitations on intermediate-scale quantum hardware for industrial adoption.
1. **Promising applications in quantum cryptography and machine learning** (23:36) — Leveraging fragile qubit states creates inherently secure cryptographic key exchanges, while hybrid architectures refine early machine learning optimization models.
1. **Learning resources and community tools for exploring quantum computing** (28:00) — Navigating comprehensive community textbooks and interactive repository games provides practical pathways toward building intuitive proficiency in quantum programming.
1. **Developing quantum intuition and running practical chemistry experiments** (29:49) — Experimenting practically with fluorescent quantum dots provides foundational intuition for recognizing how scale alters atomic rules and physical chemistry.
1. **Distinguishing logical qubits and understanding the measurement problem** (33:00) — Aggregating redundant physical qubits creates resilient logical equivalents capable of enduring observation-triggered wave function collapse across unstable environments.
1. **Integrating machine learning and utilizing particle entanglement** (39:42) — Entangled multiqubit architectures coordinate processing tasks dynamically while classical machine learning mechanisms configure noise mitigation steps for rudimentary endpoints.

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- [Discussing quantum computational boundaries and learning pathways](https://www.wearedevelopers.com/videos/481-what-is-quantum-computing) (from "What is quantum computing?")
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- [Validating quantum-inspired methods against traditional computational approaches](https://www.wearedevelopers.com/videos/100314-quantum-computing-separating-the-breakthroughs-from-the-buzz) (from "Quantum Computing: Separating the Breakthroughs from the Buzz")
- [Educating developers for the new quantum programming paradigm](https://www.wearedevelopers.com/videos/1145-the-quantum-computing-future) (from "The Quantum Computing Future")
- [Software development challenges in quantum computing ecosystems](https://www.wearedevelopers.com/videos/934-quantum-computing-the-tiny-and-the-big-challenges) (from "Quantum Computing - The tiny and the big challenges")

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