Roughly three years ago, Goldman Sachs Group Inc. looked like it had an edge in Wall Street’s race to master quantum computing.
The banking giant had assembled a handful of highly specialized scientists and partnered with Amazon.com
to figure out how the nascent technology could be used to juice better
returns for its raft of wealthy clients. They were shocked by what they
found.
Goldman’s researchers discovered
they would have to run an algorithm for millions of years in order to
solve the problem. What’s more, the processor would need to have at
least 8 million so-called logical qubits — a set of quantum bits that
form the building blocks of quantum computers. Current machines consist
of fewer than 100.
Shortly
after, Goldman’s quantum team evaporated amid the bank’s widespread
cost cutting program. While it now employs next to none, its rival JPMorgan Chase & Co.,
on the other hand, has persisted with a team of well over 50
physicists, computer scientists and mathematicians, exploring
applications in optimization problems, machine learning and cryptography.
The
contrast between the two of the world’s largest lenders is emblematic
of the split among global financial firms debating ways to harness
what’s touted to be the next big thing after artificial intelligence.
Experts say quantum computing can reshape areas ranging from new drug
discovery to machine learning and risk modeling in finance, with the
potential to add billions of dollars in revenue. But it’s also thought
to be still years away from offering many practical solutions, raising
questions about its near-term value.
Unlike
pharmaceutical, defense or material sciences firms — which appear to
have a clearer understanding of where they would like to use quantum
computing — banks, insurers and asset managers are chasing fixes to a
myriad of complex problems: transaction fraud, risk management, how to
maximize returns from a portfolio and asset price prediction, to name
just a few. The wide array of issues they want to tackle and the
limitations imposed by currently available hardware have made it more
difficult for them to pinpoint potential benefits.
Wary
of these challenges, many financial firms have largely stayed on the
sidelines, happy to let others take the lead in exploring these machines
that are exponentially more powerful than existing supercomputers. But
some like JPMorgan are pouring resources in the hope that one day the
technology would give them an edge over competitors.
“We’re
positioning ourselves so we can take advantage by understanding what
the problem space is across our portfolio,” said Rob Otter, JPMorgan’s
head of global technology applied research who earlier ran State Street
Corp.’s digital technology department, including quantum research.
While
JPMorgan declined to reveal the exact size of the team, Otter said his
crew is seeking ways to resolve performance issues and bottlenecks using
a quantum computer across the business — including the investment bank —
working with colleagues covering portfolio analytics, asset and
mortgage pricing.
In
November, the bank said it had developed a method to process and
analyze large, fast-arriving datasets more efficiently using Quantinuum Ltd.’s
Helios processor, which would enable the bank to perform complex tasks
like anomaly detection, fraud monitoring, or network analysis quicker.
In March last year, it demonstrated an algorithm on a quantum processor
with Amazon.com that could make portfolio selection easier by
identifying large sets of uncorrelated assets, enhancing diversification
and risk management.
Otter
said his team may be able to start running useful algorithms on a
quantum processing unit in the next couple of years. Now, “we’re waiting
for the hardware to be more commercially viable in order to use them,”
he said.
Still
largely in the domain of academic research, the technology is based on
the complex principles underpinning quantum mechanics. Just like
traditional computers, quantum computers also use tiny circuits to
perform calculations, but they do that simultaneously, rather than in
sequence. That allows for complex problems to be solved at vastly faster
speeds than those of classical processors.
Business consultants even have some early estimates for its potential. Research by McKinsey & Co.
last year said revenue from quantum computing is likely to surge to as
much as $72 billion by 2035, from about $4 billion in 2024, fueled by
developments in industries such as chemicals, life sciences and finance.
Read More: Quantum Computing Is Finally Here. But What Is It?
Given
the stakes, others in the world of finance — besides JPMorgan and
Goldman — have been poking around as well, but with varying intensity.
UBS Group AG is upskilling around 50 of their quant analysts in the basics of quantum computing. Spanish lender BBVA SA has worked with Multiverse Computing SL on speeding up ways to optimize portfolio management, and also with other firms. Credit Agricole SA has looked at how quantum algorithms can anticipate
credit downgrades better. Many lenders are also racing to upgrade their
cryptography, wary that the immense power of the emerging technology
may enable it to break encryption standards.
But most of the action is currently led by tech titans including Alphabet Inc.’s Google and International Business Machines Corp., plus a raft of startups, as they build and test software and hardware, like Google’s Willow and IBM’s Heron
processors. Though current models are too small and unreliable to be
useful, they have been collaborating with companies across industries to
explore potential applications by offering their services on the cloud.
Read More: Google’s Quantum Computer Solves Septillion-Year Task in Minutes
For instance, BMW is working with Nvidia Corp. and quantum software firm Classiq to find ways to improve drivetrains and cooling systems; Novo Nordisk A/S and Roche Holding AG are looking at modeling molecular interactions for new discoveries; and, Exxon Mobil Corp. is working with IBM to map the most efficient routes for its tanker fleets.
But for financial firms, developing solutions for risk tolerance and portfolio diversification gets trickier.
Plus,
when it comes to applications for finance, “there’s a lot of confusion”
about the direction, further complicated by differences in system
architectures and technologies used to build them, said Subodh Kulkarni, chief executive of quantum computer builders Rigetti Computing Inc.
— one of a growing number of listed companies in this area. That could
mean one bank may have to work with multiple quantum computing companies
to meet its needs instead of just one.
“We
certainly see increased interest from various different higher-end
financial companies,” Kulkarni said. “We certainly see them hiring
quantum physicists, exploring algorithms and doing research with
companies like us, IBM and a few others.”....