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# Quantum computing for developers: Solving optimization problems with Qiskit

You don't need to wait for commercial supremacy to write quantum code. Learn how to solve intractable optimization problems today using hybrid classical-quantum algorithms in Qiskit.

- **Speakers:** Isabell Heider, David Alber
- **Event:** World Congress 2023
- **Published:** October 23, 2023
- **Duration:** 34:14
- **URL:** https://www.wearedevelopers.com/videos/777-quantum-computing-for-developers-solving-optimization-problems-with-qiskit

## Summary

Quantum computing introduces a computational paradigm shift for developers facing the limitations of classical processing, leveraging qubits that exist in complex superpositions to solve multidimensional problems probabilistically. Though large-scale commercial supremacy remains on the horizon, developers can already build and test hybrid classical-quantum algorithms using open-source tools like the Qiskit framework. A prime workflow is the Variational Quantum Eigensolver (VQE), an optimization algorithm that iteratively pairs a classical optimizer with a quantum computer to minimize an objective function, such as finding the lowest eigenvalue of a physically measurable quantity.

Implementing VQE effectively requires defining a programmatic observable and establishing an ansatz—a parameterized quantum circuit that acts as a tunable, educated guess. By utilizing classical optimization techniques like the SPSA optimizer to refine these parameters, developers dynamically navigate complex cost landscapes toward an optimal minimum. Prototyping first on a quantum simulator proves essential, granting developers the ability to verify convergence trajectories and visualize solution spaces before deploying code to inherently noisy real-world quantum hardware.

As developers transition to more complex applications, such as calculating the molecular ground-state energy of an H2 atom, the structural complexity of the targeted ansatz increases significantly. Because real quantum execution inevitably introduces environmental noise, compensating for these disruptions becomes critical for accuracy. Integrating error mitigation techniques like zero-noise extrapolation effectively bridges the gap between raw, noisy quantum outputs and mathematically exact classical calculations. Ultimately, while VQE currently operates as a heuristic algorithm without a theoretically provable speedup, its unique capability to explore classically intractable parameter spaces positions hybrid quantum computing as an increasingly pragmatic tool for advanced software architecture.

**Keywords:** quantum computing optimization, qiskit framework programming, variational quantum eigensolver, vqe algorithm performance, quantum bits superposition, parameterized quantum circuits, quantum computing ansatz, ibm quantum hardware, classical computation limits, quantum state measurement, objective function minimization, spsa optimization routine, quantum simulator prototyping, molecular ground state modeling, zero noise extrapolation, quantum error mitigation, heuristic quantum algorithms, optimization cost landscape

## Chapters

1. **Understanding the real world need for quantum computing** (00:03) — The limitations of classical computing power when solving complex chemical simulations and factorization problems.
1. **Differences between classical and quantum bits and gates** (01:40) — How quantum bits form complex combinations using quantum gates and probability mechanics instead of deterministic algorithms.
1. **The current scaling roadmap and state of quantum hardware** (03:50) — Current milestones on the IBM hardware scaling roadmap and the availability of development kits.
1. **Solving complex optimization problems with variational quantum eigensolvers** (04:51) — How the variational quantum eigensolver helps minimize costs by finding the smallest value of an observable.
1. **The three core ingredients of quantum eigensolver algorithms** (07:10) — The iterative combination of an ansatz on a quantum computer with a classical optimizer computing algorithm costs.
1. **Programming quantum eigensolvers in python using qiskit simulators** (08:45) — Setting up an ansatz for a Pauli matrix to run optimization natively in Python using the Qiskit runtime estimator primitive.
1. **Executing python programs on a real quantum hardware backend** (19:08) — Switching the session context manager to target an actual IBM quantum device instead of a local simulated backend.
1. **Calculating the ground state energies of hydrogen molecules** (21:41) — Mapping molecular configurations of hydrogen atoms and mitigating hardware noise when predicting chemical traits.
1. **Next steps and getting started with quantum computing resources** (26:49) — Steps available for trying real quantum instances through notebooks and drag-and-drop composer tools.
1. **Audience questions on quantum optimization and algorithm behavior** (29:09) — Answers to audience questions about analytical costs, heuristic advantages, and physically possible states in iterative algorithmic loops.

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- [The evolution of the quantum developer ecosystem](https://www.wearedevelopers.com/videos/137-hands-on-journey-to-quantum-computing-with-ibm) (from "Hands-on Journey to Quantum Computing with IBM")
- [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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