CHRO 2023

Interactive Data

Lenskart: A vision for e-commerce success

Feature

CHRO Survey: Despite layoffs, talent is still king

Data Analysis

Fetch: From startup to category leader in a crowded market

CHROs weigh in on hybrid work, culture and recruiting

Inside the SoftBank Vision Fund portfolio ecosystem

Video

Finding e-commerce gems across the globe

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CEO Insights: Thriving in the world of hybrid work

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CEO Data Viz

Efficient marketing that moves the needle

One-on-one with WorkBoard’s CEO Deidre Paknad

CFO Survey: AI takes center stage as economic woes recede

Brand new: How a new identity became a unifying force for LTK

The strategy behind the SoftBank Vision Fund

CFOs weigh in on inflation, talent, volatility, and more

Starting at discovery

Be humble and keep learning

IonQ’s Peter Chapman on quantum computing’s coming of age

CEOs brace for a new wave of challenges in 2023

Investing in e-commerce

Leading a high-growth company through an economic downturn

Doubling down on marketing in an uncertain environment

CFOs are cautiously optimistic about sidestepping a recession

CMO data viz

How Carro leveraged AI to drive used car sales in Asia Pacific

Thirteen countries, one culture

CHROs: Hybrid work statistics say it’s here to stay, and employees are in the driver’s seat

Cloudbeds: The art of calculated risk

Leveling the financial playing field

COVID-19’s impact on technology

Remaking the last mile of e-commerce

How CFOs are navigating a changing market

Paper’s fast learning curve

Turning IoT’s promise into reality

Finding opportunity in a cooling market

Job van der Voort: The future of work is remote

Venturing across public and private markets

Quantum computing has long been seen with a mix of promise and skepticism. Back in 2019, Google, one of the many companies pushing the technology forward in the lab, made front-page news when it claimed a major breakthrough: Its quantum computer performed in three minutes a calculation that would have been impossible with classical machines. Since then, other advances have quietly pushed the technology forward—slowly. But the promise is so great—the ability to do massive computations in a tiny fraction of the time and at much lower cost than on classical computers for applications that could range from weather forecasting to drug development and market modeling—that research and development have continued apace, backed by investment dollars chasing a mind-bogglingly big opportunity.

There are plenty of skeptics who say quantum computing will never be mainstream. You disagree.Let me just say this. There is going to be a moment, in perhaps a not too distant future, where suddenly quantum computers are going to break onto the scene. They&#039;re going to have their ChatGPT moment. The difference is that ChatGPT is great for a limited number of uses. But quantum computers may impact a wide swath of industries and associated products.That’s a bold statement. To understand why that may be the case, it feels like we need to understand a bit more about the technology and how it works. Can you help?It’s a new mode of computation based on quantum mechanics. A quantum computer is a probability machine, and at the very base it&#039;s got a set of components that allow you to build programs. Unlike today’s digital electronics, which are built upon switches that can either be in the state zero or one, a quantum computer can work with both zero and one simultaneously, dealing with a probability of either case that goes along with it. That extra information is its superpower. The more of these “quantum bits” you have, the number of probabilities describing the system explodes exponentially.There are many fascinating things about quantum computing and how it works. But the thing that&#039;s actually the most interesting is that there&#039;s a set of problems, generally known as optimization problems, where we think quantum computing will be extremely useful. And just about everything can be converted into an optimization problem. So it&#039;s like machine learning nowadays, people are finding ways to turn their business problem into an ML problem. And so we expect in the future that people will be able to take their business problem, convert it into an optimization problem, and then run it on a quantum computer.

Aren’t quantum computers primarily in the research lab?There are many ways to build a quantum computer.  Each technology has its own roadmap and timeline. Some technologies are still in the lab and have years before they will be brought to market. At IonQ, we use ion traps, which are based on proven technology, though they still need to be refined. We&#039;re transitioning right now from the lab to a product. This is confusing for customers because they sometimes hear voices from other technologies still in the lab with roadmaps still a decade or more away.So what’s holding back IonQ’s model of quantum computing?Two key ingredients are needed: better quantum computers, which we are steadily producing, and better tools and algorithms that can be used by developers.The latter problem is just one of the newness of the field. When the first smartphones came out, it was nearly impossible to find someone who could program them. That&#039;s what&#039;s happening in quantum right now. We continue to invest in building these capabilities with our commercial and academic partners, so the field of programmers who can write quantum applications continues to grow.When we talk about making quantum computers better, what we really mean is making them smaller while also making them more performant. The quantum version of the binary bit in classical computers is called a qubit. Atomic ions are nature&#039;s qubits—they are naturally quantum and essentially perfect. They’ve been used as the best atomic clocks for years. We face some issues when we trap them and interact with them, but these issues have nothing to do with the quantumness of the qubits and have everything to do with the conventional control systems, which until now have never been engineered. So we are improving our control hardware and software as we further miniaturize our systems and make them cheaper per qubit, which is required for scalability.

Despite these hurdles, there are people out there using your machines and others like it. Who are they and what are they doing?We&#039;re working with companies like Hyundai on self-driving cars. We’ve shown our quantum computers can do object recognition of images to say, “Oh, that&#039;s a stop sign, that’s a yield sign, etc.” Even with these early quantum computers, we are finding that quantum algorithms can do these modeling tasks with stunning efficiencies. We&#039;ve done market forecasting analysis with Fidelity’s Center for Applied Technology and shown that the machine learning algorithms we can generate provide better results than standard approaches. While our quantum computers are still too small to beat classical computers outright in these cases, as we scale our systems up we expect this to change. Airbus is doing logistics work. Also, our computers are on Azure, AWS, and Google Cloud. Volkswagen and many others are using the cloud to get access to IonQ hardware.It’s early days still in the industry. But even with our limited computational power, we have beaten comparable classical approaches in important dimensions of performance. And that&#039;s really interesting because sometimes the classical approach has been worked on for 40 years, and if in six months you can come up with a better algorithm, that&#039;s a pretty impressive result. Again, while classical computers can themselves simulate what our small quantum computers are doing today, this will change soon.

Despite those customers you mentioned, CTOs, CEOs, and founders are not really thinking about their quantum strategy. Should they be? And if so, what should they do about it?At the very beginning of the computer industry, the microprocessor was highly disruptive. Then networking was highly disruptive. The internet was highly disruptive. Mobile computing was highly disruptive. Quantum is going to be probably the most disruptive of all. Anyone doing ML today should be thinking about how they would do quantum ML, because early results suggest quantum will likely get better outcomes. With quantum machines in the cloud, there’s not a lot of overhead to getting started.So how far in the future do you think this ChatGPT moment is for quantum computing?My guess is two and a half years. And so there&#039;ll be those companies that will be ready for it. And there are those companies that won’t. They’ll be just like the companies today that aren’t ready for ChatGPT and AI.

A vision for e-commerce success

Still searching for talent and grappling with hybrid work challenges