Quantum Computing

TL;DR for operators Risk teams do not only need more samples. They need samples from the part of the distribution that almost never appears until it ruins the quarter, the grid, the model launch, or the compliance meeting. The paper behind this article, Quantum enhanced rare event discovery and sampling, proposes a quantum algorithm for doing exactly that: sample from outcomes whose probabilities are below a threshold $\Delta$, without first identifying the rare set by brute force.1 ...

Errors are expensive. That is the boring sentence underneath most quantum computing roadmaps. A physical qubit is noisy, so engineers encode one logical qubit into many physical qubits. If the target computation is large enough, the redundancy becomes enormous. Then the spreadsheet starts doing what spreadsheets do best: quietly turning physics into capital expenditure. ...

Routes look simple until someone has to pay for them. A delivery van does not care whether an optimization model sounds elegant. It cares whether the assigned route wastes fuel, crosses another vehicle’s territory, violates capacity, or produces a schedule that looks clever in a paper and stupid on the street. The Capacitated Vehicle Routing Problem, or CVRP, is where that mundane reality becomes mathematically unpleasant: multiple vehicles, limited capacity, customer demand, depot returns, and a search space that grows far faster than managerial patience. ...

TL;DR for operators Financial institutions do not suffer from a shortage of market ticks in the abstract. They suffer from a shortage of repeated histories. There is only one realised S&P 500 path, one realised liquidity crisis, one realised volatility regime sequence. Synthetic data is attractive because it promises more examples of rare-but-important behaviour without waiting politely for the next crisis to arrive. ...