Predicting the Market with Quantum Computing
When it comes to financial modeling, optimization is key.
Certain problems like optimal trading trajectory problems (algorithmic trading rate management for large trades) and finding optimal arbitrage opportunities have already been solved to a limited degree on a D-Wave quantum annealer. While annealers do not lend themselves to solving a variety of problems like you will be able to on a universal quantum computer, annealers are especially good at solving optimization problems, and thus are specifically equipped to handle problems like these.
Annealers can carry out optimization problems without going through all possible options to find the best one, but what about the problems that do require that you go simulate all possible scenarios in order to find the best one? Simulation is something that universal quantum computers are specially good at. Solving problems like portfolio optimization (choosing what to invest in as well as how much to invest) and optimal feature selection in credit scoring (figuring out which aspects of an applicant's profile are most relevant to assigning a credit score) is extremely hard, if not impossible, on a classical computer. With the combination of variables and various scenarios, you would quickly run into something called a combinatorial explosion. For example, did you know there are 8×10^67 ways to rearrange a deck of cards? That is eight followed by 67 zeros, or more atoms than there are in the entire universe. With that in mind, you can start to see how running financial simulations could easily turn into far more expensive a problem than could be reasonably processed be on any supercomputer.
Because of the qubit's ability to take on virtually infinite values at once, these sorts of simulation problems are something that a quantum computer is very good at. At Quantum Labz, we've assembled a team of quantum programmers and experts in financial modeling to bring to life new solutions to beat the - until now insurmountable - challenges in predicting the financial markets.