Hey, I'm Elise, you you're listening to Ted talks daily, we have come to trust supercomputers to make breakthroughs and help solve so many problems.

But in today's talk from TED at BCG in 2020, quantum advocate Matt Langone says classical supercomputers have reached their limit. And it's time for a different, more advanced kind of computing to confront the even more complex problems to come.

What will the biggest challenges of the twenty first 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 one 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 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 policymakers to address. If not traditional supercomputers, what technology will emerge to arm us against the challenges of the twenty first century? Enter quantum computing. Quantum computers promise to address the atomic limitation by exploiting subatomic physical properties that weren't even known to man one hundred years ago.

But 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 maze like quality, especially simulation and optimization problem, some of which can be solved exponentially faster with a quantum computer. But is there really value to this so-called quantum speed up? 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 fertilizers 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 one hundred to three hundred 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 maze like molecular interactions that make up the electrostatic field of the key catalyst. Nitrogenous scientists actually know how to do that today.

But it would take eight hundred thousand years on the world's fastest supercomputer. With a full scale quantum computer. Less than twenty four 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 of 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 is 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 five trillion, trillion, trillion, trillion years on the world's fastest supercomputer on a quantum computer a little more than a half hour.

The quantum computing is not just about Triumph's in the lab. The flow of progress in industries of all kinds is currently blocked by discrete 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 banks were able to lend more freely to individuals, entrepreneurs and businesses?

One of the key hold ups 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 investable 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 cubitt.

Would take one hundred years to build. Ten years later, it arrived, now IBM has nearly five hundred cubits across twenty nine 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 the, quote, one Nobel Prize winning physicist. Quantum computers are more different from current computers. Then current computers are. From the abacas, it'll take time to make the necessary workflow integrations.

It'll take time to on board 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 I'm 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 processors.

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. So when we can't afford to lose. Quantum computers are in pole position, there's the car to beat. Ted talks daily, is hosted by me, Elise Hu, and produced by Ted theme music is from Allison Layton Brown. In our mixer is Christopher Fazi Bogon.

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