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Quantum Computing is the Next Frontier in Innovation

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Quantum Computing is the Next Frontier in Innovation

GUEST POST from Chateau G Pato

As we stand at the crossroads of technological evolution, quantum computing emerges as a groundbreaking force poised to redefine the landscape of innovation. Unlike traditional computers that use bits as the smallest unit of data, quantum computers utilize qubits, which have the ability to exist in multiple states at once, leading to computational capabilities that are exponentially faster and more powerful. This revolutionary technology holds the potential to transform industries, solve complex problems, and open up new avenues for creativity and progress.

The Promise of Quantum Computing

Quantum computing represents a paradigm shift, offering immense power for problem-solving in fields such as materials science, cryptography, and artificial intelligence. Because of their ability to perform complex calculations at unprecedented speeds, quantum computers can analyze and process enormous amounts of data, providing solutions that were previously unimaginable.

Case Study 1: Pharmaceutical Innovation

Pharmaceutical companies are at the forefront of leveraging quantum computing to accelerate drug discovery. One groundbreaking example is the collaboration between the tech giant IBM and the pharmaceutical leader GlaxoSmithKline (GSK). This partnership aims to use quantum computing to simulate molecular interactions at an atomic level, dramatically speeding up the discovery of new compounds. By accurately predicting how molecules interact, GSK hopes to streamline the development of new drugs, reducing the time and cost involved in bringing lifesaving treatments to market.

With quantum computing, researchers are now able to run simulations that capture the complexities of molecular dynamics, leading to a better understanding of drug efficacy and safety. As a result, this case study underscores the transformative potential of quantum computing in the pharmaceutical industry, promising to revolutionize how new therapies are developed and personalized for patients.

Case Study 2: Revolutionizing Transportation with Quantum Optimization

The transportation sector stands to gain immensely from quantum computing, particularly in the realm of optimization. Volkswagen, in collaboration with D-Wave, a pioneer in quantum computing systems, explored the use of quantum algorithms to improve traffic flow and reduce congestion in urban environments. The pilot project targeted reducing wait times and optimizing routes for city buses in Lisbon during the Web Summit.

By leveraging quantum computing to process and analyze real-time traffic data, the project demonstrated its potential to minimize traffic jams and enhance the overall efficiency of transportation networks. This case study illustrates how quantum computing can be an engine of innovation, offering solutions that create value not only for businesses but also for cities and their inhabitants by reducing travel time, cutting emissions, and improving the quality of urban life.

The Road Ahead

While quantum computing is still in its nascent stages, the potential it holds for catalyzing innovation across industries is undeniable. As we invest in research, development, and collaboration, it’s vital for organizations to envision how they can harness the power of quantum computing to address unique challenges and seize new opportunities. As we step into this new frontier, interdisciplinary partnerships and a keen focus on human-centered design will be essential to unlocking the full potential of quantum technologies.

In conclusion, quantum computing is not just the next frontier in innovation; it is a catalyst for the radical transformation of the technological landscape. By continuing to explore and invest in this extraordinary field, we open doors to limitless possibilities that promise to reshape our world for the better.

SPECIAL BONUS: The very best change planners use a visual, collaborative approach to create their deliverables. A methodology and tools like those in Change Planning Toolkit™ can empower anyone to become great change planners themselves.

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Competing in a New Era of Innovation

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Competing in a New Era of Innovation

GUEST POST from Greg Satell

In 1998, the dotcom craze was going at full steam and it seemed like the entire world was turning upside down. So people took notice when economist Paul Krugman wrote that “by 2005 or so, it will become clear that the internet’s impact on the economy has been no greater than the fax machine’s.”

He was obviously quite a bit off base, but these types of mistakes are incredibly common. As the futurist Roy Amara famously put it, “We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.” The truth is that it usually takes about 30 years for a technology to go from an initial discovery to a measurable impact.

Today, as we near the end of the digital age and enter a new era of innovation, Amara’s point is incredibly important to keep in mind. New technologies, such as quantum computing, blockchain and gene editing will be overhyped, but really will change the world, eventually. So we need to do more than adapt, we need to prepare for a future we can’t see yet.

Identify A “Hair-On-Fire” Use Case

Today we remember the steam engine for powering factories and railroads. In the process, it made the first industrial revolution possible. Yet that’s not how it started out. Its initial purpose was to pump water out of coal mines. At the time, it would have been tough to get people to imagine a factory that didn’t exist yet, but pretty easy for owners to see that their mine was flooded.

The truth is that innovation is never really about ideas, it’s about solving problems. So when a technology is still nascent, doesn’t gain traction in a large, established market, which by definition is already fairly well served, but in a hair-on-fire use case — a problem that somebody needs solved so badly that they almost literally have their hair on fire.

Early versions of the steam engine, such as Thomas Newcomen’s version, didn’t work well and were ill-suited to running factories or driving locomotives. Still, flooded mines were a major problem, so many were more tolerant of glitches and flaws. Later, after James Watt perfected the steam engine, it became more akin to technology that remember now.

We can see the same principle at work today. Blockchain has not had much impact as an alternative currency, but has gained traction optimizing supply chains. Virtual reality has not really caught on in the entertainment industry, but is making headway in corporate training. That’s probably not where those technologies will end up, but it’s how they make money now.

So in the early stages of a technology, don’t try to imagine how a perfected version fit in, find a problem that somebody needs solved so badly right now that they are willing to put up with some inconvenience.

The truth is that the “next big thing” never turns out like people think it will. Putting a man on the moon, for example, didn’t lead to flying cars like in the Jetsons, but instead to satellites that bring events to us from across the world, help us navigate to the corner store and call our loved ones from a business trip.

Build A Learning Curve

Things that change the world always start out arrive out of context, for the simple reason that the world hasn’t changed yet. So when a new technology first appears, we don’t really know how to use it. It takes time to learn how to leverage its advantages to create an impact.

Consider electricity, which as the economist Paul David explained in a classic paper, was first used in factories to cut down on construction costs (steam engines were heavy and needed extra bracing). What wasn’t immediately obvious was that electricity allowed factories to be designed to optimize workflow, rather than having to be arranged around the power source.

We can see the same forces at work today. Consider Amazon’s recent move to offer quantum computing to its customers through the cloud, even though the technology is so primitive that it has no practical application. Nevertheless, it is potentially so powerful—and so different from digital computing—that firms are willing to pay for the privilege of experimenting with it.

The truth is that it’s better to prepare than it is to adapt. When you are adapting you are, by definition, already behind. That’s why it’s important to build a learning curve early, before a technology has begun to impact your business.

Beware Of Switching Costs

When we look back today, it seems incredible that it took decades for factories to switch from steam to electricity. Besides the extra construction costs to build extra bracing, steam engines were dirty and inflexible. Every machine in the factory needed to be tied to one engine, so if one broke down or needed maintenance, the whole factory had to be shut down.

However, when you look at the investment from the perspective of a factory owner, things aren’t so clear cut. While electricity was relatively more attractive when building a new factory, junking an existing facility to make way for a new technology didn’t make as much sense. So most factory owners kept what they had.

These types of switching costs still exist today. Consider neuromorphic chips, which are based on the architecture of the human brain and therefore highly suited to artificial intelligence. They are also potentially millions of times more energy efficient than conventional chips. However, existing AI chips also perform very well, can be manufactured in conventional fabs and run conventional AI algorithms, so neuromorphic chips haven’t caught on yet.

All too often, when a new technology emerges we only look at how its performance compares to what exists today and ignore the importance of switching costs—both real and imagined. That’s a big part of the reason we underestimate how long a technology takes to gain traction and underestimate how much impact it will have in the long run.

Find Your Place In The Ecosystem

We tend to see history through the lens of inventions: Watt and his steam engine. Edison and his light bulb. Ford and his assembly line. Yet building a better mousetrap is never enough to truly change the world. Besides the need to identify a use case, build a learning curve and overcome switching costs, every new technology needs an ecosystem to truly drive the future.

Ford’s automobiles needed roads and gas stations, which led to supermarkets, shopping malls and suburbs. Electricity needed secondary inventions, such as home appliances and radios, which created a market for skilled technicians. It is often in the ecosystem, rather than the initial invention, where most of the value is produced.

Today, we can see similar ecosystems beginning to form around emerging technologies. The journal Nature published an analysis which showed that over $450 million was invested in more than 50 quantum startups between 2012 and 2018, but only a handful are actually making quantum computers. The rest are helping to build out the ecosystem.

So for most of us, the opportunities in the post-digital era won’t be creating new technologies themselves, but in the ecosystems they create. That’s where we’ll see new markets emerge, new jobs created and new fortunes to be made.

— Article courtesy of the Digital Tonto blog
— Image credit: Pixabay

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Importance of Long-Term Innovation

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Importance of Long-Term Innovation

GUEST POST from Greg Satell

Scientists studying data from Mars recently found that the red planet may have oceans worth of water embedded in its crust in addition to the ice caps at its poles. The finding is significant because, if we are ever to build a colony there, we will need access to water to sustain life and, eventually, to terraform the planet.

While it’s become fashionable for people to lament short-term thinking and “quarterly capitalism,” it’s worth noting that there are a lot of people working on—and a not insignificant amount of money invested in—colonizing another world. Many dedicate entire careers to a goal they do not expect to be achieved in their lifetime.

The truth is that there is no shortage of organizations that are willing to invest for the long-term. In fact, nascent technologies which are unlikely to pay off for years are still able to attract significant investment. The challenge is to come up with a vision that is compelling enough to inspire others, while still being practical enough that you can still make it happen.

The Road to a Miracle Vaccine

When the FDA announced that it was granting an emergency use authorization for Covid-19 vaccines, everybody was amazed at how quickly they were developed. That sense of wonder only increased when it was revealed that they were designed in a mere matter of days. Traditionally, vaccines take years, if not decades to develop.

Yet appearances can be deceiving. What looked like a 10-month sprint to a miracle cure was actually the culmination of a three-decade effort that started in the 90s with a vision of a young researcher named Katalin Karikó, who believed that a molecule called mRNA could hold the key to reprogramming our cells to produce specific protein molecules.

The problem was that, although theoretically once inside the cytoplasm mRNA could instruct our cell machinery to produce any protein we wanted, our bodies tend to reject it. However, working with her colleague Drew Weissman, Karikó figured out that they could slip it past our natural defenses by slightly modifying the mRNA molecule.

It was that breakthrough that led two startup companies, Moderna and BioNTech to license the technology and for investors to back it. Still, it would still take more than a decade and a pandemic before the bet paid off.

The Hard Road of Hard Tech

In the mid-90s when the Internet started to take off, companies with no profits soon began attracting valuations that seemed insane. Yet the economist W. Brian Arthur explained that under certain conditions—namely high initial investment, low or negligible marginal costs and network effects—firms could defy economic gravity and produce increasing returns.

Arthur’s insight paved the way for the incredible success of Silicon Valley’s brand of venture-funded capitalism. Before long, runaway successes such as Yahoo, Amazon and Google made those who invested in the idea of increasing returns a mountain of money.

Yet the Silicon Valley model only works for a fairly narrow slice of technologies, mostly software and consumer gadgets. For other, so-called “hard technologies,” such as biotech, clean tech, materials science and manufacturing 4.0, the approach isn’t effective. There’s no way to rapidly prototype a cure for cancer or a multimillion-dollar piece of equipment.

Still, over the last decade a new ecosystem has been emerging that specifically targets these technologies. Some, like the LEEP programs at the National Laboratories, are government funded. Others, such as Steve Blank’s I-Corps program, focus on training scientists to become entrepreneurs. There are also increasingly investors who specialize in hard tech.

Look closely and you can see a subtle shift taking place. Traditionally, venture investors have been willing to take market risk but not technical risk. In other words, they wanted to see a working prototype, but were willing to take a flyer on whether demand would emerge. This new breed of investors are taking on technical risk on technologies, such as new sources of energy, for which there is little market risk if they can be made to work.

The Quantum Computing Ecosystem

At the end of 2019, Amazon announced Braket, a new quantum computing service that would utilize technologies from companies such as D-Wave, IonQ, and Rigetti. They were not alone. IBM had already been building its network of quantum partners for years which included high profile customers ranging from Goldman Sachs to ExxonMobil to Boeing.

Here’s the catch. Quantum computers can’t be used by anybody for any practical purpose. In fact, there’s nobody on earth who can even tell you definitively how quantum computing should work or exactly what types of problems it can be used to solve. There are, in fact, a number of different approaches being pursued, but none of them have proved out yet.

Nevertheless, an analysis by Nature found that private funding for quantum computing is surging and not just for hardware, but enabling technologies like software and services. The US government has created a $1 billion quantum technology plan and has set up five quantum computing centers at the national labs.

So if quantum computing is not yet a proven technology why is it generating so much interest? The truth is that the smart players understand that the potential of quantum is so massive, and the technology itself so different from anything we’ve ever seen before, that it’s imperative to start early. Get behind and you may never catch up.

In other words, they’re thinking for the long-term.

A Plan Isn’t Enough, You Need To Have A Vision

It’s become fashionable to bemoan the influence of investors and blame them for short-term and “quarterly capitalism,” but that’s just an excuse for failed leadership. If you look at the world’s most valuable companies—the ones investors most highly prize—you’ll find a very different story.

Apple’s Steve Jobs famously disregarded the opinions of investors, (and just about everybody else as well). Amazon’s Jeff Bezos, who habitually keeps margins low in order to increase market share, has long been a Wall Street darling. Microsoft invested heavily in a research division aimed at creating technologies that won’t pan out for years or even decades.

The truth is that it’s not enough to have a long-term plan, you have to have a vision to go along with it. Nobody wants to “wait” for profits, but everybody can get excited about a vision that inspires them. Who doesn’t get thrilled by the possibility of a colony on Mars, miracle cures, revolutionary new materials or a new era of computing?

Here’s the thing: Just because you’re not thinking long-term doesn’t mean somebody else isn’t and, quite frankly, if they are able to articulate a vision to go along with that plan, you don’t stand a chance. You won’t survive. So take some time to look around, to dream a little bit and, maybe, to be inspired to do something worthy of a legacy.

All who wander are not lost.

— Article courtesy of the Digital Tonto blog
— Image credit: Pexels

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Exploring the Potential of Quantum Computing in Solving Complex Problems

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Exploring the Potential of Quantum Computing in Solving Complex Problems

GUEST POST from Chateau G Pato

Quantum computing has emerged as an exciting frontier in the field of computer science, promising to revolutionize problem-solving capabilities. By harnessing the unique properties of quantum mechanics, quantum computers have the potential to solve complex problems that are impossible for classical computers. In this thought leadership article, we will delve into the opportunities and challenges associated with quantum computing, while illustrating its potential through two compelling case studies.

Case Study 1: Drug Discovery Acceleration

The process of drug discovery is a time-consuming and expensive endeavor that typically involves screening large chemical databases for potential therapeutic compounds. Quantum computing offers a promising solution by enabling rapid exploration of chemical space. One notable case study involves the collaboration between IBM and pharmaceutical company Merck. By leveraging IBM’s Qiskit software platform and accessing IBM’s quantum systems, researchers at Merck were able to investigate various molecular configurations and accelerate the discovery of novel drug candidates. Quantum simulations provided valuable insights into the interactions of molecules at a quantum level, leading to more efficient drug design and reducing costs associated with traditional laboratory-based testing.

Case Study 2: Optimizing Supply Chain Management

Supply chains are often complex networks with numerous variables and interdependencies, making them difficult to optimize. Quantum computing holds tremendous potential in analyzing and streamlining supply chain processes. Volkswagen, in collaboration with Google and mobileX, explored the application of quantum computing in optimizing electric vehicle spare parts delivery. By utilizing Google’s quantum processors and advanced machine learning algorithms, they demonstrated how the quantum approach can significantly enhance route optimization, reduce transportation costs, and improve overall efficiency in supply chain management. The results showcased the immense potential of quantum computing in revolutionizing traditional logistics strategies.

Challenges and Future Considerations:

While the opportunities presented by quantum computing are undoubtedly transformative, challenges remain on the path to widespread adoption. Quantum systems are highly sensitive to noise and environmental factors, making it challenging to maintain stability and accuracy in computations. Building error-correcting mechanisms and scalable quantum hardware are pivotal for overcoming these hurdles. Furthermore, educating and training a workforce equipped with the required skill sets will be crucial.

To pave the way for the widespread implementation of quantum computing, collaboration between academia, industry, and governments is necessary. Investments in research and development, as well as infrastructure, are key to advancing quantum computing capabilities and fostering innovation.

Conclusion

Quantum computing holds immense potential in solving complex problems that are beyond the reach of classical computers. The case studies involving drug discovery acceleration and supply chain optimization highlight its promising applications in real-world scenarios. Though challenges persist, investments in research, collaboration, and skill development can help unlock the full potential of quantum computing. As the technology continues to evolve, organizations that leverage quantum computing will gain a significant competitive advantage, enabling breakthroughs in a wide array of industries and ultimately shaping a better future for humanity.

SPECIAL BONUS: Futurology is not fortune telling. Futurists use a scientific approach to create their deliverables, but a methodology and tools like those in FutureHacking™ can empower anyone to engage in futurology themselves.

Image credit: Pixabay

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Training Your Quantum Human Computer

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Quantum Human Computing

What is quantum computing?

According to Wikipedia, “Quantum computing is the use of quantum phenomena such as superposition and entanglement to perform computation. Computers that perform quantum computations are known as quantum computers.”

Rather than try and explain all of the ins and outs of how quantum computing differs from traditional computing and why it matters, I encourage you to check out this YouTube video:

In case you were curious, according to the Guinness Book of World Records, the current record holder for quantum computing is a Google machine capable of processing 72 Quantum Bits. There is supposedly a machine in China capable of 76 Qubits, but it has yet to be fully recognized as the new record holder.

So, what does quantum computing have to do with humanity and the human brain and our collective future?

Is the human brain a quantum computer?

The easy answer is – we’re not sure – but scientists are conducting experiments to try and determine whether the human brain is capable of computing in a quantum way.

As the pace of change in our world accelerates and data proliferates, we will need to train our brains to use less traditional brute force computing of going through every possibility one after another to do more parallel processing, better pattern recognition, and generating an increase in our ability to see insights straight away.

Connect the Dots

But how can we train our brains?

There are many different ways to better prepare your brain as we move from the Information Age to the Age of Insight. Let me start you off with two good ones and invite you to add more in the comments:

1. Connect the Dots

Many of us grew up doing connect-the-dot puzzles, and they seemed pretty easy. But, that is with visual queues. The image above shows a number of different visual queues. Connect the dots, especially without numbers or visual queues are great proving grounds for improving your visual pattern recognition skills.

2. DLAIY JMBULE

One of my favorites is the word game DAILY JUMBLE in my local newspaper. You can also play it online. The key here is to work not on using brute force to reorder the letters into a word, but trying to train your brain to just SEE THE WORD – instantly.

Succeeding at this and other ways of training your brain to be more like a quantum computer involves getting better at removing your conscious analytical brain from the picture and letting other parts of your brain take over. It’s not easy. It takes practice – continual practice – because it is really hard to keep the analytical brain out of the way.

So, are you willing to give it a try?

Stay tuned for the next article in this series “The Age of Insight” …

Image credits: Utrecht University, Pixabay


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Unraveling the Potential of Quantum Computing in Solving Complex Problems

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Unraveling the Potential of Quantum Computing in Solving Complex Problems

GUEST POST from Chateau G Pato

In recent years, the field of quantum computing has captured the imagination of scientists, researchers, and technologists worldwide. Promising significant advancements over classical computers, quantum computing has the potential to revolutionize various industries by solving complex problems that were once considered insurmountable. With its ability to harness the principles of superposition and entanglement, quantum computing offers novel approaches to computation, unlocking new frontiers in fields such as cryptography, drug discovery, optimization, and modeling complex physical systems.

Case Study 1 – Cryptography

One of the most exciting prospects of quantum computing lies in its ability to break cryptographic codes that are currently deemed unbreakable by classical computers. Case in point, the advent of quantum algorithms such as Shor’s algorithm allows for the efficient factorization of large numbers, a crucial foundation of many encryption methods currently employed. To illustrate how this could impact various industries, let’s consider the financial sector. Banks and financial institutions rely on encryption to protect customers’ sensitive information and ensure secure online transactions. Should quantum computers become capable of breaking existing encryption algorithms, the financial industry would need to swiftly adapt by implementing quantum-resistant encryption methods. The ripple effect of quantum computing in cryptography extends beyond finance, affecting communication, military intelligence, and data security for various sectors worldwide.

Case Study 2 – Drug Discovery

Another compelling case study showcasing the potential of quantum computing can be found in the field of drug discovery. The process of discovering new drugs is an intricate and time-consuming task involving extensive computational analysis. Quantum computing has the potential to significantly accelerate this process by simulating the behavior of molecules with unparalleled precision. By leveraging quantum algorithms, researchers can more accurately predict how drugs will interact with target molecules, reducing the need for costly and time-consuming laboratory experiments. This computational power could pave the way for the discovery of new drugs and the ability to personalize treatments based on an individual’s unique molecular makeup, revolutionizing healthcare and ultimately saving lives.

Additionally, quantum computing holds great promise in optimizing complex systems, offering solutions to previously intractable problems. Consider the logistics industry, which heavily relies on optimization algorithms to optimize delivery routes, minimize costs, and decrease transportation time. Quantum computing could offer significant advancements in this field by exponentially improving the efficiency of optimization algorithms. By analyzing vast amounts of data and considering intricate variables, quantum computers could determine optimal routes, minimizing fuel consumption, and reducing carbon emissions. Such advancements benefit not only the logistics industry but also have implications for supply chain management, traffic control, and urban planning, ultimately leading to more sustainable and efficient infrastructures.

While these case studies provide a glimpse into the future capabilities of quantum computing, it is important to acknowledge that the field is still in its infancy. Overcoming the current challenges of maintaining qubits’ stability, error correction, and scaling remains critical for the practical implementation of quantum computers. However, tremendous strides have been made, and as technology continues to evolve, quantum computing holds the potential to unlock new frontiers and transform countless industries.

Conclusion

Unraveling the potential of quantum computing offers a new chapter in computational possibilities. The breakthroughs it can provide, from breaking encryption codes to accelerating drug discovery and optimizing complex systems, can transform industries and shape the world we live in. Embracing quantum computing’s potential opens up new avenues for innovation and brings us closer to solving complex problems that were once thought to be beyond the reach of classical computation. Let us embrace this frontier with curiosity, resilience, and collaboration, as we stand on the precipice of a quantum revolution.

SPECIAL BONUS: Braden Kelley’s Problem Finding Canvas can be a super useful starting point for doing design thinking or human-centered design.

“The Problem Finding Canvas should help you investigate a handful of areas to explore, choose the one most important to you, extract all of the potential challenges and opportunities and choose one to prioritize.”

Image credit: Pixabay

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