Despite the advances classical computers have brought to fields such as materials sciences, chemistry, finance, and artificial intelligence, there exist problems that are so large and complex that we will never have enough classical computing power to solve them.
These problems are complex in nature, and complex problems, especially in the sciences, tend to be quantum systems. Which is to say, nature is quantum mechanical. We think it’s fair to say that only quantum can solve quantum problems.
As quantum computers scale up in size, they offer the opportunity to solve these currently intractable problems.
Quantum computers are faster for certain types of calculations that require computational parallelism. The improvement is not the speed of operations, but rather the total number of operations that must be performed per unit time to arrive at the result.
Quantum computers can be used to solve NP-complete problems that cannot be solved in polynomial time with any conventional algorithm. An example of an NP-complete problem is the travelling salesman problem, a graph theory problem requiring the calculation of the most efficient route a salesman can take through each of (n) cities.
There’s a number of problems that finance that could be better solved with quantum computing. Problems like regression analysis, risk simulation, market collapse modeling, portfolio optimization, derivatives pricing, credit scoring, the list is endless.
Not only can quantum do it better, it can do it faster. Currently, QCI is developing an asset allocation system with algorithms that will run on quantum and digital annealers. We believe it’s possible to use this technology to construct optimal portfolios orders of magnitude faster than classical computers can.
In 2016, Northeastern University stated that globally, we create 2.5 exabytes, or 2.5 quintillion bytes of data every single day. Realistically, we probably create much more data now than we did in 2016, but to put 2.5 exabytes in perspective, it’s equivalent to 250,000 times the entirety of the Libraries of Congress. This virtual flood of data from laptops, computers, phones, and other technology has created the advent of Big Data, unfathomably large data sets on everything from how many people like to wear red polo shirts on a sunny day to what they’re likely to eat after a hard session at the gym.
The problem is that we now have so much data that it’s hard to find out what to do with it. Analysis on a higher level is nearly impossible due to just how much information is contained. It’s an ideal problem for quantum computing, which can handle massive data sets with ease, providing insights to artificial intelligence which can then analyze it further.
At the core of most, if not all, advanced artificial intelligence or machine learning systems is optimization problems. Machine learning is an incredibly iterative process, and utilizes huge data sets to learn and evolve to figure out improved approaches to the problem at hand. Novel quantum algorithms could dramatically accelerate the underlying processing required for machine learning.
The strange, nearly metaphysical nature that governs how qubits operate in quantum computing, not only hold the key for better and faster artificial intelligence, but may also be the secret to true artificial intelligence.
Learn how our super fast & efficient solution through QAI, a coined principle of joining Quantum with AI.