His GitHub repos (if public) typically include notebooks, simulated backtests, and comparisons to classical benchmarks (e.g., MPT, Black-Scholes). This is rare and valuable.

As he honestly admits, for most finance models, classical methods still win on scale and precision. The quantum versions are slower on current hardware, though future fault-tolerant QCs could change this.

In classical computing, optimizing a portfolio involves navigating a labyrinth of constraints and objectives (maximizing returns while minimizing risk). As the number of assets grows, the number of possible combinations explodes, a phenomenon known as the "curse of dimensionality." Pere advocates for and develops algorithms utilizing quantum annealing and the Quantum Approximate Optimization Algorithm (QAOA). These methods allow quantum processors to explore a multitude of potential solutions simultaneously, identifying the optimal portfolio configuration in a fraction of the time required by classical heuristics.

Christophe Pere Financial Modeling Using Quantum Computing [repack] Direct

His GitHub repos (if public) typically include notebooks, simulated backtests, and comparisons to classical benchmarks (e.g., MPT, Black-Scholes). This is rare and valuable.

As he honestly admits, for most finance models, classical methods still win on scale and precision. The quantum versions are slower on current hardware, though future fault-tolerant QCs could change this. christophe pere financial modeling using quantum computing

In classical computing, optimizing a portfolio involves navigating a labyrinth of constraints and objectives (maximizing returns while minimizing risk). As the number of assets grows, the number of possible combinations explodes, a phenomenon known as the "curse of dimensionality." Pere advocates for and develops algorithms utilizing quantum annealing and the Quantum Approximate Optimization Algorithm (QAOA). These methods allow quantum processors to explore a multitude of potential solutions simultaneously, identifying the optimal portfolio configuration in a fraction of the time required by classical heuristics. His GitHub repos (if public) typically include notebooks,