The Quantum Hybrid Training System

Our hands-on workbook teaches you how to build a real quantum circuit. Learn the fundamentals of quantum gates, superposition, and entanglement in a practical way. It's the perfect starting point for any aspiring quantum practitioner.

Explore the intersection of AI and quantum computing. Create a project that uses a quantum algorithm (like a Quantum Support Vector Machine) for a complex classification task. Quantum parallelism can offer speedups for certain machine learning problems by exploring high-dimensional data in ways classical computers can't.

— What Alan Turing would have built.

This track focuses on creating a high-fidelity dataset of human intuition and behavior. This 'synthetic human' data is then broadcast to our agentic systems via `broadcastpeople.com`, providing them with a constant stream of common-sense reasoning via endpoints like `api.whatwould.work`, making their decisions more robust and aligned with human values.

— What Daniel Kahneman would have built.

Simulating molecules is notoriously difficult for classical computers. This project could use quantum algorithms to calculate molecular ground states—a fundamental quantum mechanical problem—which is crucial for drug discovery and materials science. Develop a cloud-native app that models molecular structures.

— What Marie Curie would have built.

Use quantum-inspired algorithms for complex financial modeling, like option pricing. Quantum computing's ability to explore vast possibility spaces can help find optimal trading strategies or assess risk in ways that are intractable for classical computers. Connect it to a real-time data feed and visualize risk profiles.

— What John von Neumann would have built.

Build an intelligent lead scoring system. A Quantum Machine Learning (QML) model could identify subtle patterns in vast customer datasets that classical algorithms might miss. Use GenAI to enrich the data, then apply a quantum classifier to predict which prospects are most likely to convert, optimizing the sales pipeline.

— What a Y Combinator founder would build.

Tackle a classic hard problem: the Traveling Salesperson. Build an application that calculates the most efficient route for complex logistics. Quantum annealing concepts are perfectly suited for such optimization problems, finding near-optimal solutions in a vast search space that would overwhelm classical computers.

— What George Dantzig would have built.

Build an AI that synthesizes multiple data types—like text-based symptoms, medical images, and audio clips—to form a more holistic preliminary diagnosis. Quantum Machine Learning can be key here. QML algorithms could analyze the incredibly complex, high-dimensional data created by fusing these different inputs, potentially uncovering subtle, non-linear relationships between symptoms that classical models would miss.

— What Rosalind Franklin would have built.

Develop a system that processes high-volume data streams, like live video or network traffic, to identify anomalous patterns in real time. This is a classic challenge where speed is critical. Quantum-enhanced perception could process vast amounts of data in parallel, allowing the system to detect faint signals or complex deviations from normal behavior that would be computationally prohibitive for classical systems.

— What Grace Hopper would have built.

Create a financial engine that identifies and acts on market arbitrage opportunities in microseconds. This is a high-frequency optimization problem. Quantum algorithms like QAOA excel at rapidly exploring a massive number of potential trading strategies to find the optimal one, enabling execution at speeds impossible for classical computers, capturing value before it vanishes.

— What Richard Feynman would have built.