The researchers’ model can complete in half an hour what would take a regular PC longer than a human lifetime.

By Stacy Elliott

The Bank of Canada has become the first G7 country to turn to quantum computing to simulate scenarios where cryptocurrency and fiat currency can coexist.

This week, Multiverse Computing, the startup leading Canada’s research, hit a milestone: Its model can evaluate more than 1 octillion possible scenarios in 30 minutes. An octillion is a 10 followed by 30 zeros.

That means Multiverse Computing has completed its proof-of-concept, which combines blockchain data from stablecoin Tether (USDT), whose tokens are pegged to the U.S. dollar, and public data from up to 10 major financial institutions. It also consulted with experts from two major Canadian banks to come up with realistic parameters. 

Multiverse Computing chose Tether for its model because the stablecoin, founded in 2014, had endured a variety of market scenarios in its eight years worth of blockchain data.

Most scenarios in the model showed that non-financial institution adoption of the cryptocurrency would be slow, since there was some upfront knowledge and cost associated with converting fiat to a digital asset. It was also able to simulate how banks might respond by reducing wire transfer fees to compete with the very low cost of crypto transactions.

The research itself has only just reached the proof-of-concept stage, so there aren’t yet any implications for Canada’s crypto regulations. But being able to use quantum computing models to simulate how fiat and digital currencies might compete for use and adoption is a big leap forward, says a Bank of Canada official.

“We wanted to test the power of quantum computing on a research case that is hard to solve using classical computing techniques,” said Maryann Haghighi, the central bank’s director of data science. “The collaboration helped us learn more about how quantum computing can provide new insights into economic problems by carrying out complex simulations on quantum hardware.”

The Bank of Canada initially reached out to Multiverse Computing in 2019 because of its work on predicting financial crashes. The startup’s flagship product, the Sigularity software development kit, augments mainstay financial quant tools like the Python program language or Microsoft Excel, with quantum-level cloud computing power.

To hear Multiverse Chief Technology Officer Sam Mugel tell it, the central bank’s decision to have the team simulate cryptocurrency adoption was a bit of a macroeconomic flex.

“The Canadian economy, in their view—and I hope it’s true—is too stable to have a high likelihood of financial crashes. So basically they said any financial crash we predicted would probably be wrong,” the computational physicist told Decrypt in an interview. “So they said, ‘Let’s look at something more volatile. Let’s look at crypto trades and predict crypto crashes.’”

From there the focus of the research evolved into looking at the effects of regulations on crypto.

Earlier simulations had been able to include only a few big banks, compared with the startup using its D-Wave Systems annealer, a type of quantum computer, to make possible including as many as 10.

Quantum computers are powerful on a scale that’s difficult to explain if you’ve primarily interacted with standard PCs. So let’s tap the Marvel Cinematic Universe for an analogy.

Dr. Strange, played by Benedict Cumberbatch, in the 2018 film “Avengers: Infinity War.”

When Dr. Strange, a hero with the ability to manipulate time and space, looks into the future during a pivotal moment in “Avengers: Infinity War,” he’s able to consider 14,000,605 possible outcomes in just a few seconds and find the one in which the heroes win. 

In that example, Dr. Strange is like a quantum computer because he can simultaneously churn through millions of scenarios. A non-super hero, or in this case a PC, could try that but would have to consider each possibility one by one. It would take decades, compared with half an hour.

Mugel said their next steps include making the model even more efficient and able to simulate more variables in an economy.

“Another space we touched upon in our study was looking at financial institutions exchanging currency, but what if we started adding in things like trading houses for crypto,” he said. “Like maybe we could look at a three-way model crypto adoption, the trading house and then the actual people adopting it.”