Transcriber:
What will the biggest challenges of the 21st century turn out to be?
Today, one might guess climate change, public health, inequality,
but the truth is we don't yet know.
What we do know
is that supercomputing will have to be part of the solution.
For nearly a hundred years,
our reliance on high-performance computers
in the face of our most urgent challenges
has grown and grown,
from cracking Nazi codes to sequencing the human genome.
Computer processors have risen to meet increasingly critical and complex demands
by getting smaller, faster and better year after year,
as if by magic.
But there's a problem.
At the very moment that our reliance on computers is growing faster than ever,
progress in compute power
is coming to a standstill.
The magic is just about spent.
The timing couldn't be worse.
We rarely talk about it, but for all that we've accomplished with computers,
there remain a startling number of things that computers still can't do,
at a great cost to business and society.
The dream of near-instant computational drug design, for instance,
has yet to come to fruition, nearly 50 years after it was first conceived.
Never has that been clearer than now,
as the world sits in a state of isolation and paralysis,
as we await a vaccine for COVID-19.
But drug discovery is just one area in which researchers are beset --
and in some cases, blocked entirely --
by the inadequacy of even today's fastest supercomputers,
putting great constraints in areas like climate change,
and in value creation in areas like finance and logistics.
In the past,
we could rely on supercomputers simply getting better and faster,
as parts got smaller and smaller every year,
but no longer.
For now, we're drawing up against a hard physical limit.
Transistors have become so minuscule
that they're fast approaching the size of an atom.
Such a state of affairs invites a natural follow-up question,
and it's one that I've spent the last several years
encouraging business leaders and policy makers to address:
If not traditional supercomputers,
what technology will emerge
to arm us against the challenges of the 21st century?
Enter quantum computing.
Quantum computers like this one
promise to address the atomic limitation
by exploiting subatomic physical properties
that weren't even known to man a hundred years ago.
How does it work?
Quantum computing enables a departure from two major constraints
of classical semiconductor computing.
Classical computers operate deterministically:
everything is either yes or no, on or off, with no in between.
They also operate serially; they can only do one thing at a time.
Quantum computers operate probabilistically, and most importantly,
they operate simultaneously, thanks to three properties --
superposition, entanglement and interference --
which allow them to explore many possibilities at once.
To illustrate how this works, imagine a computer is trying to solve a maze.
The classical computer would do so by exhausting every potential pathway
in a sequence.
If it came across a roadblock on the first path,
it would simply rule that out as a solution,
revert to its original position
and try the next logical path,
and so on and so forth until it found the right solution.
A quantum computer could test every single pathway
at the same time,
in effect, solving the maze in only a single try.
As it happens,
many complex problems are characterized by this mazelike quality,
especially simulation and optimization problems,
some of which can be solved exponentially faster
with a quantum computer.
But is there really value to this so-called quantum speedup?
In order to believe that we need faster supercomputers,
we need to first believe that our problems are indeed computational in nature.
It turns out that many are, at least in part.
For an example, let’s turn to fertilizer production,
one of the hallmark problems in the science of climate change.
The way most fertilizer is produced today
is by fusing nitrogen and hydrogen to make ammonia,
which is the active ingredient.
The process works,
but only at a severe, a severe cost
to businesses, who spend 100 to 300 billion every year,
and to the environment:
three to five percent of the world's natural gas
is expended on fertilizer synthesis
every single year.
So why have scientists failed to develop a more efficient process?
The reason is that in order to do so,
they would need to simulate the mazelike molecular interactions
that make up the electrostatic field of the key catalyst, nitrogenase.
Scientists actually know how to do that today,
but it would take 800,000 years on the world's fastest supercomputer.
With a full-scale quantum computer,
less than 24 hours.
For another example, let's return to drug discovery,
a process COVID-19 has brought into sharp focus for most of us
for the very first time.
Designing a vaccine for an infectious disease like COVID-19,
from identifying the drivers of the disease,
to screening millions of candidate activators and inhibitors,
is a process that typically takes 10 or more years per drug,
90 percent of which fail to pass clinical trials.
The cost to pharmaceutical companies is two to three billion dollars
per approved drug.
But the social costs of delays and failures are much, much higher.
More than eight million people die every year of infectious diseases.
That's 15 times as many people as died during the first six months
of the coronavirus pandemic.
So why has computational drug design failed to live up to expectations?
Again, it's a matter of limited computational resources, at least in part.
If identifying a disease pathway in the body is like a lock,
designing a drug requires searching through a massive chemical space,
effectively a maze of molecular structures,
to find the right compound,
to find a key, in other words, that fits the lock.
The problem is that tracing the entire relevant span of chemical space
and converting it into a searchable database for drug design
would take 5 trillion trillion, trillion, trillion years
on the world's fastest supercomputer.
On a quantum computer, a little more than a half hour.
But quantum computing is not just about triumphs in the lab.
The flow of progress and industries of all kinds is currently blocked
by discreet, but intractable computational constraints
that have a real impact on business and society.
For what may seem an unlikely example, let's turn to banks.
What if banks were able to lend more freely
to individuals, entrepreneurs and businesses?
One of the key holdups today
is that banks keep 10 to 15 percent of assets in cash reserves,
in part, because their risk simulations are compute constrained.
They can’t account for global or whole-market risks that are rare
but severe and unpredictable.
Black swan events, for example.
Now, 10 to 15 percent is a whole lot of money.
When you consider that for every one-percent reduction in cash reserves,
it would lead to an extra trillion dollars of investible capital.
What this means is that if banks ultimately became comfortable enough
with quantum-powered risk simulations
to reduce cash reserves to, say, five to 10 percent of assets,
the effect would be like a COVID-19-level stimulus
for individuals and businesses
every single year.
Once the transformative power of quantum computing is clear,
the question then becomes: Well, how long must we wait?
Researchers are cautious when asked about the timeline to quantum advantage.
Rightly so -- there remain a number of critical hurdles to overcome,
and not just engineering challenges,
but fundamental scientific questions about the nature of quantum mechanics.
As a result, it may be one, two, even three decades
before quantum computers fully mature.
Some executives that I've spoken with have come to the conclusion on this basis
that they can afford to wait,
that they can afford to postpone investing.
I believe this to be a real mistake,
for while some technologies develop steadily,
according to the laws of cumulative causation,
many emerge as precipitous breakthroughs,
almost overnight defying any timeline that could be drawn out in advance.
Quantum computing is a candidate for just such a breakthrough,
having already reached a number of critical milestones
decades ahead of schedule.
In the late 80s, for example,
many researchers thought that the basic building block of quantum computing,
the qubit,
would take a hundred years to build.
Ten years later, it arrived.
Now IBM has nearly 500 qubits across 29 machines,
available for client use and research.
What this means is that we should worry less
about quantum computers arriving too late
and more about them arriving too soon,
before the necessary preparations have been made.
For to quote one Nobel prize winning physicist,
"Quantum computers are more different from current computers
than current computers are
from the abacus."
It'll take time to make the necessary workflow integrations.
It'll take time to onboard the right talent.
Most importantly,
it'll take time, not to mention vision and imagination,
to identify and scope high-value problems
for quantum computers to tackle for your business.
Governments are already investing heavily in quantum technologies --
15 billion dollars among China, Europe and the US.
And VCs are following suit.
But what's needed now to accelerate innovation
is business investment in developing use cases,
in onboarding talent
and on experimenting with real quantum computers
that are available today.
In a world such as ours,
the demands of innovation can't be put off for another day.
Leaders must act now,
for the processor speedups that have driven innovation for nearly 70 years
are set to stop dead in their tracks.
The race toward a new age of magic and supercomputing is already underway.
It's one we can't afford to lose.
Quantum computers are in pole position. They're the car to beat.
Thank you.