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Modern life is punctuated by market cycles.

One year the gears of commerce are whirring along. Businesses are hiring and investing. People are buying houses and cars, televisions and computers. Things are going great. Then a year later, the gears screech to halt—sweeping layoffs, plummeting investment, and crashing markets. No one’s buying anything.

Recessions are usually signaled first in financial markets, and painful downturns are felt acutely by investors, from savvy Wall Streeters to retirees. Yet we still struggle to figure out how best to invest our savings, and most professional investment managers don't outperform the market they're trying to beat.

So, why can’t we more clearly see the financial landscape?

According to Marcos Lopez de Prado—senior managing director at Guggenheim Partners and a research fellow at Lawrence Berkeley National Laboratory's Computational Research Division—financial research faces some big challenges.

Because real world markets are more complex than the simplified models used to describe them, in practice, research-backed strategies rarely work as expected.

But according to Lopez de Prado, there’s hope we’ll soon learn to better model markets using quantum computing—and it could transform the way we study financial systems.

Like Predicting Intelligent Weather Patterns…

Speaking at Singularity University and CNBC’s Exponential Finance conference recently, Lopez de Prado sketched out two big challenges facing financial research.

The first is that researchers don’t have a laboratory. There’s no controlled setting in which they can run experiments and carefully measure the results. Instead, experts observe real world events and make models based on those observations.

However, though some financial events are similar, they’re never the same. These real-world “experiments” aren’t reproducible. Imagine a physicist who can’t repeatably drop a ball to learn how gravity works, Lopez de Prado said.

“That's the situation in finance. It's a very big problem that most research is done without having the ability to reproduce an experiment by controlling the variables involved in that experiment.”

The second problem is that financial models are way too simple.

Modern markets are some of the most complicated systems in existence. They involve millions of humans (and computers) making billions of transactions involving a multitude of different kinds of assets every day. Prices ebb and flow moment to moment, some crashing others shooting higher.

When scientists predict the weather, Lopez de Prado says, they have to model many variables interacting with each other to arrive at a forecast of rain or sun.

Now, imagine that interacting weather patterns have brains and are continuously looking around to see what their peers are up to and adapting their behavior based on what everyone else is doing. Financial markets are a little like that.

How can we ever hope to model them? More powerful computers, of course.

Modeling Nature Using Nature’s Own Algorithms

In scientific studies, supercomputers allow us to model the world in richer, more complex hues. If researchers want to predict the weather or tease out exotic new particles, they have to use supercomputers—there’s just no other way.

But even supercomputers have limits. Which is where quantum computers come in.

A quantum computer is a little like a digital computer in that can represent information with 1s and 0s. In addition, however, each component of a quantum computer can also be some combination of 1 and 0 at the same time. This is known as superposition.

Superposition is one of the weird laws of physics on the smallest scales. It allows unobserved particles to be in many states simultaneously, but as soon as they’re observed they assume a discrete state—in the case of quantum computers, 1s and 0s representing a possible solution to a problem the computer has been tasked with.

“Essentially…a quantum computer is a device that uses nature to solve a problem that is relevant to us,” Lopez de Prado said.

Because quantum computers can work on many solutions in parallel, it’s believed they will be exponentially better than traditional computers at certain tasks. And one of those tasks, Lopez de Prado said, will be much improved financial models.

Some of these applications might include more accurate options pricing, the ability to run a portfolio through millions of market scenarios to make it more robust, or grouping thousands of assets into a few categories based on their similarities.

Problems that would be too much for today’s computers—requiring many years to solve—will be a breeze for tomorrow’s quantum computers. They could be the basis of a virtual laboratory to better test financial hypotheses and strengthen theories.

The Dawn of Quantum Computing

Last year, Google and NASA announced their D-Wave quantum computer worked out a solution to a specially tailored problem far faster than a traditional machine. Recently, another Google team said they’ve taken first steps toward a universal quantum computer able to solve any problem, not just a special subset of problems.

Quantum computing is full of promise. But then again, we’ve been dreaming of quantum computers for decades. The question is how close we are to practical applications. And although progress is being made, the answer isn’t clear just yet.

Currently, there are only a handful of commercial quantum computers in the world. These are still multi-million-dollar machines that require a team of PhDs to operate. Most practical applications, no matter how compelling, are still mostly on paper.

Even so, Lopez de Prado is excited at the prospects.

"The digital computing era is about to end," Lopez de Prado said. "We need something to replace it. And right now the best candidate is quantum computing."


Image credit: D-Wave Systems/Wikimedia Commons

Jason is managing editor of Singularity Hub. He cut his teeth doing research and writing about finance and economics before moving on to science, technology, and the future. He is curious about pretty much everything, and sad he'll only ever know a tiny fraction of it all.