Quantum Finance

In my glorious quest to discover something, anything practical to do with quantum computing, I finally have a project that ties together most of the disparate smaller projects that I’ve either been working on for a while or that I’ve had on my to-do list for a while. The overall project will be a “kitchen sink” project like my Google Web Scraper, which started off as Twitter sentiment analysis and grew into a search engine web scraper applying two sentiment analysis libraries, four text summarization techniques, keyword ranking, and more.

I’m still working on the project’s scope, but here are the major deliverables thus far:

  • Finance API. I’m starting here and actively searching for a source of real-time data. In the financial industry, timing can be everything. Once I select an API, though, we’ll call this the easiest phase of the project.
  • JSON. This should also be easy, but since I like to play with assembly languages I haven’t actually used JSON for anything yet. At a quick glance, all the APIs I’m considering offer JSON options.
  • Portfolio Optimization Algorithms. I already have several papers that have apparently led to Nobel Prizes in economics. I’m going to start relatively simple and try to get just one algorithm to work. Thanks to Chicago Quantum for the homework assignment that turned into this project.
  • Quantum Machine Learning (QML). The amount of historical financial data available lends itself well to training a model of some kind. Besides digging deeper into the current state of QML, this could be a good opportunity to dig deeper into Pandas, Tensorflow, and Keras.
  • Quantum Monte Carlo. The papers I’ve downloaded may offer better algorithms, but since classical Monte Carlo is so popular this is worth looking into.
  • IBM Watson. Natural Language Processing (NLP) could be useful for analyzing financial news. Honestly, I’ve just been looking for any excuse to play with a supercomputer, and here’s my excuse.
  • D-Wave. Portfolio optimization algorithms might be a good test of quantum annealing. I have an account set up and the documents downloaded; I’ve just been waiting for something to try out on it.
  • Quantum Error Correction (QEC). This is necessary for quantum practicality. I’ll be looking at both hardware and algorithms. Thanks to Dr. James Wootton for the inspiration here.
  • Simulation. You can’t have real-time input only to squander timeliness by waiting in a queue. Therefore, simulation seems to be the only viable option in the absence of partnership agreements with data and hardware providers. Thanks to Redditer u/LittleByBlue for the idea of possibly incorporating a custom simulator, or at least a local one.
  • Circuit optimization. Although waiting in a queue for real hardware is not timely, I’m still going to build the functionality. The point of quantum algorithms, however, is speed over classical algorithms, so waiting in a queue overnight is counterintuitive.

Basically, this is my fictitious university and these are the courses I want to take. Each course has its own final project, and then the entire program has a capstone project. I don’t have a timeline in mind for completion, but my plan is to build something basic and then add onto it over time.

I should also point out that I have a client for this project: me. There’s only one practical reason to do all this work, and that’s to put my own money where my code is, so to speak. The capstone project, in all seriousness, is to optimize my own portfolio. Real money will be in play.

For the record, I would prefer to do something more meaningful than making money. However, finance is understandable. Not easy, but understandable. I would prefer to discover a lifesaving drug or something like that, but quantum chemistry will have an even steeper learning curve than what I’ve already assigned myself.

Maybe next time.