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We achieved quantum advantage in portfolio optimization (in R&D mode) using a DWave quantum annealer

6/11/2020

6 Comments

 
Picture
Over the past 3-4 months, our portfolio team (Jeffrey Cohen, Alex Khan, Clark Alexander and associate Professor Terrill Frantz) has focused on moving from classical computation of portfolio optimization to running that same code on a quantum computer.  We have been running on the DWave system for a few months, and finally feel comfortable with how to operate it, tune it, speak to it in its own language, and achieve results we can verify and feel confident in sharing.

I am proud to announce that last night we achieved that milestone.  We were able to optimize a set of 40 assets and select a (group of) portfolios that are best to invest in for someone who cares about minimizing risk and maximizing return, or about balancing those metrics.

We will be publishing our results. 

Three things to share:
1.  We learned a great deal about how to formulate a problem for a quantum annealer.  You see, quantum computers don't multiply nor divide.  They are not Microsoft Excel.  You have to think differently to use them.
2.  Quantum computing time, and our own time, is precious.  We used just a little each month, and learned how to simulate quantum annealers very well.  We could run a 'quantum computing' problem in simulation mode, exactly matching the quantum energy levels, using Python for up to 24 assets in seconds, 28 assets (268m) in a 1-3 minutes, and 32 assets (4.2b) in just over a day on a 48GB RAM server.  We could not achieve brute force classically with 40 assets...we would lose too much time.
On a quantum computer, 32 assets takes just a few seconds.  40 Assets classically was beyond our reach.  
3.  We are having fun doing this.  We have Google Hangouts meetings for hours, code Python and DWave into the late night, dig into the math, physics, and economics when needed to crack a problem, and I think this has given us a better understanding of the equities markets.  If you check our company's Twitter handle, @chicago_quantum, you will see posts on money flows that are well beyond our comprehension when we started this effort.

We will publish our findings in a technical way (arXiv or a publication - call us Physics Review, American Banker, McKinsey Quarterly, and Harvard Business Review), and in non-technical ways (e.g., YouTube videos, Medium Articles, LinkedIn, and Tweets).   Thank you, Jeffrey Cohen.
6 Comments
Robert R Tucci link
6/12/2020 22:57:56

1. Quantopian does classical portfolio optimization
(ref. https://qbnets.wordpress.com/quantopian-bayesian-networks/
2. Fujitsu's does quantum inspired optimization

Will you compare D-Wave portfolio optimization with 1 and 2 as far as speed and cost?

Reply
Jeffrey Cohen link
6/15/2020 12:08:44

Hi Robert, good to hear from you. Thank you for the comment.

Have not tried either of them, so cannot comment on speed and cost. However, Fujitsu is on the roadmap, and would be glad to have a call / collaboration with Quantopian due to their complementary focus.

Jeff Cohen
jeffrey@quantum-usaci.com

Reply
Robert Tucci link
6/15/2020 13:54:45

Thanks for the reply.

I have no ties with Quantopian. I just admire very much their contributions to the open source program PyMC3, and their really cool, unique business model. If you can collaborate with them, that would be awesome

fred de Leeuw link
7/1/2020 11:10:47

Looks interesting however one should be careful in portfolio computational approach as the market is not behaving logical, not taking into account a possible recession or even depression! Hard to forecast based on historical data . Yet could be useful. Curious is this would work for bonds .

Reply
Jeffrey Cohen link
7/1/2020 11:55:57

Hi Fred, we built our model as the markets fell, and tuned it as markets rose (and learned how to adjust for this). When portfolios are selected based only on historical variance / covariance, it removes noise and builds confidence. We also saw significant reversion to mean @ 5 years.

RE: fixed income. Should work, but we would need to understand the market data services, model the math, and reformulate the QUBO. Would be a great client project.

Reply
Jeffrey Cohen link
7/6/2020 12:46:06

Here is the arXiv link. It went live late last night:

https://arxiv.org/abs/2007.01430

Reply



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    Jeff Cohen

    Strategic IT Management Consultant with a strong interest in Quantum Computing.  Consulting for 29 years  and this looks as interesting as cloud computing was in 2010.

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  • Home
  • Team
  • Research
  • Buy Analysis
  • Market Questions & Data
  • Contact
  • Blog
  • Use Cases
  • Platforms
  • Quantum FAQs
  • Chicago Quantum | Corporate Finance 101
  • 1-hour working session
  • 1-day working session
  • 1-week strategy workshop
  • Analyze up to 3,250 stocks (quantum or classically)
  • 64 Stocks (Quantum & Classical)
  • 64 Stocks (Classical)
  • T-Shirts
  • Custom Algorithm Development
  • Platforms Detail
  • Use Case Detail
  • Market Data
  • Validated tickers
  • Portfolio.m