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Tag: quantum computing

  • Businesses Must Bridge the Skills Gap to Succeed With Quantum Computing

    Businesses Must Bridge the Skills Gap to Succeed With Quantum Computing

    Quantum computing is poised to transform the way companies solve business problems. The technology needed to power this revolution is becoming a reality, with large developers like IBM making excellent progress toward building quantum-centric supercomputers. But while the technological barrier to deploying quantum is falling away, another obstacle remains: The lack of quantum skills in the labor market.

    Last year, Classiq commissioned a survey of over 500 professionals familiar with quantum technology, which highlighted a range of possible benefits that quantum computing will unlock, including cost savings, new revenue streams and competitive advantages for companies that successfully deploy the technology. But the survey also highlighted that the shortage of qualified engineers and scientists who can program quantum computers remains one of the biggest barriers to increased quantum deployments.

    This skills gap will not only prevent companies from leveraging the power of quantum computing but also lead to expensive competition for talent as businesses try to recruit the few quantum professionals available in the workforce. Why has this skills gap grown, and what can companies do about it?

    Coding for the Quantum Computing Era

    First, we need to understand what skills are missing that are contributing to a skills gap, as some readers may ask why existing programmers and computer engineers cannot meet this demand. Besides lacking the purely technical skills needed to code for quantum computing, such as creating quantum circuits and software, there is also a knowledge gap around how to solve business problems using a quantum computer.

    For instance, while many programmers trained in classical computing could figure out a way to design a program for portfolio optimization, they likely have no idea how to do the same using a quantum computer. They are unlikely to know where to start, how to check their results or even how to run their code on a quantum device.

    Coding for quantum computers is also more complicated than for classical computing. As each “qubit” can be one and zero simultaneously instead of either/or, writing the quantum code is more complex. Developers need to understand quantum concepts such as superpositions, so working in quantum requires new ways of thinking about problems and necessitates new ways of working.

    This last issue is compounded by a technology gap, as quantum programming has not been abstracted away as it has for classical programming – it is mostly still at the assembly language level. At this level, writing a few dozen lines of quantum programming is achievable but time-consuming, so creating complex quantum programs of hundreds or thousands of lines of code is almost impossible.

    Solving the Skills Gap

    The first instinct for companies wishing to leverage quantum technology is to hire their way out of this problem, but there are simply not enough Ph.D.-trained quantum experts in the market, and there is likely to be stiff competition for the few quantum-trained graduates coming out of university. Given the shortage, companies must instead consider how to train and upskill their staff.

    Convincing existing staff to take part in this training should not be an issue. Engineers and programmers are keen to learn – nearly 95% of respondents to our survey said they would like to be trained in quantum. Why is there such high demand? Not only is this new technology exciting, but many workers may hope to future-proof their jobs or increase their earning potential through quantum skills.

    Upskilling your workforce does not have to be expensive. There is already a wide range of open source material, such as the Qiskit development kit, designed to help people explore this new technology. For companies, a straightforward solution is to give their engineers the time and space to learn about quantum for themselves. Simply giving your existing coders permission and resources to learn about this technology and how it can benefit your business could prove to be a profitable long-term investment.

    The added advantage of upskilling your staff, rather than recruiting quantum Ph.D. graduates, is your existing workforce understands the sorts of problems your business needs to solve.

    Plus, new products are emerging that can help solve the technology gap and make it easier to code for the quantum era. Sophisticated low-code software can abstract away some of the complexity involved in writing quantum coding, which in turn will speed up the process of learning.

    But upskilling is only one thing to consider. Companies also need to invest in the resources and infrastructure around their emerging quantum talent. They need to build a team, identify use cases that can benefit from quantum technology and locate the right vendors and cloud providers that will allow them to test and simulate their quantum programs.

    The quantum revolution remains several years away, but companies need to make sure they have the skills and infrastructure ready to take advantage of this technology or risk being left behind by quantum-prepared competitors.

  • IBM z16 Delivers AI and Quantum-Safe Levels of Security

    IBM z16 Delivers AI and Quantum-Safe Levels of Security

    Business transformation and disruption is all about data. But it’s not just about collecting huge amounts of data, it’s what you do with that data and how it can enable you to create powerful insights.

    While many people focus on the cloud unicorns, the reality is that industry incumbents have a huge advantage. These businesses have decades of experience which translates into massive amounts of data and business logic. Where do many of these businesses host their most important workloads and data? IBM zSystems—the system that is best known as “the mainframe.” 

    Today, IBM is announcing its latest IBM zSystem—IBM z16. Two weeks ago, IBM spent time briefing the industry analyst community on the business strategy and technical underpinnings of the IBM z16. We met with a variety of IBM business leaders including executives from the IBM Systems division, Distinguished Engineers and IBM cloud leaders. The most significant updates to the platform are the inclusion of a dedicated on-chip AI accelerator and the addition of quantum-safe cryptography.

    It’s important to understand the size of the IBM zSystems ecosystem. The platform is used by 45 of the 50 largest banks in the world, four of the top five airlines and eight of the top 10 insurers, among others. Likewise, the partner ecosystem is huge, ranging from the largest system integrators to small DevOps- and AI-focused technology vendors. Suffice it to say, some of the world’s largest businesses and governments run on IBM zSystems.

    In this article, I will briefly cover the five biggest takeaways from the pre-announcement industry analyst briefings.

    IBM is Bringing AI to the Mainframe

    Many cloud data warehouse and analytics vendors say “Bring me the data.” IBM is flipping the script, because IBM zSystems already houses clients’ most critical customer and operational data. IBM z16 will be shipped with an on-chip integrated accelerator for AI.

    Everyone knows that AI creates a competitive advantage, so why should you care about the on-chip AI accelerator? The last mile of deploying machine learning/artificial intelligence (ML/AI) models is still a huge challenge. Businesses are investing large amounts of money and skills in developing models; however, latency often kills. The difficulty is that ML/AI models need to be built into applications so that the model can be executed at the point of transaction with virtually no delay. This also must be done at a massive scale! Think about the latency and security concerns if near-real-time banking transactions needed to go from a back-end system to a cloud AI platform to check for fraudulent transactions.

    For those of you who have been following the IBM zSystems platform, IBM’s z14 unveiled a pervasive encryption capability. This encrypt-by-default approach without traditional encryption overhead was enabled through an encryption-dedicated chip. Similarly, AI workloads will be performed by the integrated AI accelerator.

    Your AI Infrastructure is Only as Powerful as Your Software Ecosystem

    Developers and data scientists want to use data, ML and AI tools that they know and trust. AI success requires an open stack of software options, not a walled garden. While IBM has a wide range of AI technologies, including Watson Machine Learning for z/os, IBM Cloud Pak for Data and IBM Cloud Pak for Watson AIOps, just to name a few, an IBM-only approach would be a non-starter for many data scientists.

    The IBM z16 AI accelerator supports a wide selection of ML libraries and compilers. Clients can build and train models in a variety of popular frameworks including TensorFlow, SAS, Microsoft CognitiveToolkit (Microsoft CNTK), PyTorch and Keras, along with others.

    The system uses ONNX, an open source tool to enable interoperability of ML/AI models to prepare and deploy the model on the dedicated AI accelerator.

    What does this mean? You can bring your existing data models to IBM z16. In addition, your teams can use their existing preferred vendors while also working with IBM zSystem’s integrated accelerator chip.

    Cybersecurity Arms Race: Quantum-Safe Cryptography is a Growing Need

    Cybercrime is a multibillion-dollar business. Although fairly routine exploits like phishing remain the number-one money generator for cybercriminals, these gangs and nation-state-sponsored organizations are getting more sophisticated. For example, cybercriminals are investing in AI and ML just like traditional businesses. In some cases, cybercrime syndicate engineering employees believe they’re working for a legitimate company.

    What is quantum-safe cryptography? The research is focused on identifying algorithms that are resistant to attacks by both classical and quantum computers. 

    Why is this a growing area of concern? IBM, along with Amazon Web Services (AWS), Microsoft Azure and Google Cloud Platform (GCP) all offer quantum computing-as-a-service. While quantum computing has massive potential for applications across a wide range of industries including drug discovery, chemical engineering and combating climate change, keep in mind that cybercriminals are looking at this technology to unlock complex cryptography algorithms. 

    IBM claims that IBM z16 is a “quantum-safe system.” The company uses a variety of different hardware, software and business process approaches to assure data security. For example, the system includes a new crypto express card with quantum-safe APIs to upgrade existing application security. At a firmware level, IBM z16 has a built-in dual signature scheme.

    In addition, for a number of years, the IBM zSystems platform has taken a zero-trust approach to security which includes a bring-your-own-key (BYOK) framework. IBM can never access the keys to your encrypted data–and neither can cybercriminals and/or governmental organizations—instead, those encryption keys remain inside your firewall.

    IBM zSystems Tech Goes to the Cloud; Mainframe Remains On-Premises

    What a difference a nine can make! Five nines mean 99.999% availability while four nines (99.99% availability) result in more than 47 minutes of downtime per year.

    When I work with C-level executives that have a mainframe within their environment, the role of IBM zSystems is always discussed. In many cases, businesses have explored the possibility of transitioning off of the mainframe, but resilience, predictability and security are the reasons why businesses remain on the platform.

    IBM mainframe services are increasingly being offered in the cloud. Most recently, IBM announced IBM Z testing in the cloud. Likewise, IBM’s Cloud Hyper Protect Crypto Services, a key management approach using a specific security chip module, is enabled by IBM zSystems technology.

    However, you shouldn’t expect mainframe-as-a-service. Between latency, security, resilience, governance and compliance, it’s going to be a long time until you migrate and/or send your mainframe workloads to the cloud.

    As the author of Hybrid Cloud for Dummies and Cloud for Dummies 2nd Ed. (among other books and articles) I can confidently say that the cloud is not a panacea. The business case for moving many mainframe workloads to the cloud doesn’t exist. 

    Digital Transformation Means More Than Slick Apps–Plumbing Matters!

    Don’t get me wrong, mobile check deposits, airline ticketing and contactless hotel check-ins are great, but in many cases, they rely on older systems that have not been updated. 

    A few weeks ago while traveling for work, my flights were delayed for hours. I saw that another flight was leaving within the hour. I was already at the airport and figured I could rebook it via the airline’s app. Of course, it wasn’t that easy—I finally went to the ticketing desk and told the agent that I’d like to book a different flight. The agent then went to work; they spent a long time entering information into their system, talking to a colleague, asking for help and asking me several questions. After about ten minutes my flight was finally rebooked.

    This airline has a great app experience when things are running smoothly (so great, in fact, that I prefer to book my travel on the app rather than the computer). However, even a slight disruption caused everything to go off the rails. It’s clear to me that the company still relies on old, broken processes and outdated technology.

    My Perspective

    Moving AI closer to the data just makes sense. The last mile of AI—moving models into transactions—continues to be a huge challenge. I work with many businesses that have tons of data and do an excellent job retrospectively analyzing that data. However, real-time data-driven decision-making remains a challenge because of latency and the fact that moving data between on-premises and cloud systems raises security and governance risks (in addition to costs).

    Quantum-safe security is a growing concern for highly regulated organizations in the banking, insurance and governmental segments. Over the last several years, financial industry CIOs and CISOs/CSOs at large financial services organizations were focused on establishing a zero-trust approach to security. However, as attacks become more sophisticated, security teams are starting to perform risk assessments on their approach to cryptography. The inclusion of quantum-safe cryptography is important for IBM zSystems customers.

  • How Quantum Computing can Solve Real-World Problems

    How Quantum Computing can Solve Real-World Problems

    The history of technology can seem like it was very predictable in retrospect, but the future typically feels more uncertain. Nowhere is this uncertainty more evident than in the domain of quantum computing. When the spectrum of possible outcomes spans from “quantum computers will be one of the most important technology developments of all time” to “quantum computing may never really become practical enough to justify using over a classical computer alternative,” trying to make forecasts can seem futile. However, as a technologist, investor, consumer or just a curious observer, the boundaries and texture of the uncertainty itself can be very interesting, valuable and informative. 

    Quantum computing isn’t likely a replacement for today’s classical computing—at least not initially—but rather a potential complement to it for specific applications. A 2021 report by Deloitte lists dozens of applications that could be transformed by quantum computing approaches: Protein folding, fluid simulation, credit underwriting, financial risk analysis, supply chain optimization and forecasting, vehicle routing, fraud detection, fault analysis, weather forecasting, semiconductor chip design, product-portfolio optimization, consumer product recommendations—and this is only half the list. 

    Quantum computing could make previously intractable simulation, search and optimization calculations relatively easy and quick. For some kinds of calculations, a quantum computer could be mind-bendingly faster than a powerful classical computer could ever be. While today’s classical computers running machine learning systems can make incredibly accurate predictions, quantum-based systems can, in theory, far outstrip their speed at certain prediction-related tasks.

    The blend of physics, math and computer science behind quantum computers is fascinating, but complex. Computer scientist Scott Aaronson, one of the best science communicators, wrote a recent essay for Quanta magazine called, “Why Is Quantum Computing So Hard To Explain?” 

    To oversimplify, quantum computers are built around “qubits,” which are subatomic-scale components, often kept at incredibly cold temperatures (think: Absolute zero) to minimize interference from the rest of the universe. These components can be put into a quantum physics state called “superposition” that allows them to, in a sense, take on many potential values at once. Even though their value is uncertain, reliable calculations and transformations can be performed on these qubits while in a superposition before being reduced to a more classical state, effectively parallelizing computations. Qubits in a quantum computer can also be entangled with one another (a phenomenon that Einstein referred to as “spooky action at a distance”), harnessing them as a team to increase the variety of logical calculations that it’s possible to compute. For certain types of math—for example, searching for an optimal outcome from myriad options—quantum algorithms can seem close to instantaneous when compared to those that run on classical computing hardware. 

    Quantum Computing Challenges

    Publicly available quantum computing has been on the horizon for decades, yet there’s still no reliable forecast for when products capable of widespread adoption will get released, exactly what form the systems will take or how broad and deep quantum computing’s impact will be. There are many technical challenges when it comes to getting a broadly applicable quantum computer to be useful at scale. And quantum computers are racing against classical computers, which have also been improving very rapidly in power, versatility and ease-of-use. 

    One important bottleneck for quantum computers is that correctly reading the results of a quantum calculation is prone to a very high error rate—the delicate superposition state can deteriorate before the correct result is presented as an output.

    Aaronson explains it:

    The problem, in a word, is decoherence, which means unwanted interaction between a quantum computer and its environment — nearby electric fields, warm objects, and other things that can record information about the qubits … The only known solution to this problem is quantum error correction… But researchers are only now starting to make such error correction work in the real world. When you read about the latest experiment with 50 or 60 physical qubits, it’s important to understand that the qubits aren’t error-corrected. Until they are, we don’t expect to be able to scale beyond a few hundred qubits.

    Some theorists even believe a practical quantum computer is impossible to build because of the inherently high amount of “noise in the system.” Mathematician Gil Kalai predicts that doing error correction to reduce that noise to a usable level would, by basic computing theorems, restrict the computer’s number-crunching capacity so low as to be not worth using.

    Nevertheless, researchers continue to chisel away at the challenge of error correction and to pass key technical milestones. For example, in July, Google researchers published a paper in Nature explaining how they have found a promising error-reduction method that scales as more qubits are added to a system. 

    Hardware, Software and App Landscape

    There are several different approaches to quantum hardware. Rigetti and Oxford Quantum Circuits use superconducting quantum circuits, a more widespread approach favored also by Google, Microsoft, and IBM. IonQ (which is planning to go public via a SPAC transaction) and Honeywell (which recently announced a combination of its quantum computing unit with UK-based Cambridge Quantum Computing) use trapped ions. D-Wave uses quantum annealing, ColdQuanta is using cold atoms and Xanadu is using photonics. Each approach has its pros and cons, and each hardware technology faces a fluid set of shifting bottlenecks in reaching affordable scale. Work at the materials layer continues to advance as well, for example, Raicol Crystals is growing quantum quasi-phase matching crystals that can be used in applications as varied as quantum computing, sensing, encryption and communications. And it’s not certain that there won’t be another approach that leapfrogs them in solving the challenges of error correction and price.

    Other companies, such as Quantum Machines, Q-CTRL and Seeqc, are building control hardware that can operate in the space between classical and quantum hardware. Control systems can exist at temperatures warmer than absolute zero, but colder than room temperature. Some of these systems involve both software and hardware and can perform tasks like monitoring the quantum processor’s performance, performing error correction or serving as an abstraction layer connecting classical and quantum hardware. 

    There are also companies creating quantum computing application layer software, like Zapata Computing, Riverlane or QC Ware. This can include application frameworks, components, algorithms or even turnkey quantum computing applications. Classiq has software for quantum circuit synthesis. There’s also a lot of interesting fundamental research happening at the software intersection between quantum computing and artificial intelligence – e.g. Zapata published work recently using IonQ’s ion trap quantum system to train a machine learning system to generate high-quality handwritten digits with low error rates. 

    One of the first theoretical uses for quantum computing, proposed in 1994 by mathematician Peter Shor, would be to crack the RSA encryption widely used today to protect data. The threat spawned research into post-quantum cryptography, with researchers proposing multiple new approaches to encryption that would need to replace today’s algorithms to preserve the privacy of data. Companies such as ISARA (quantum-resistant encryption) and Quantum Xchange (an out-of-band, symmetric key delivery platform) emerged to help prepare governments and companies now for a world where quantum-based, encryption-cracking algorithms start to work on breaking today’s encryption.  

    There are several widely used software development kits (SDKs) that let developers write software for a quantum computer without actually having one to run it on. In fact, a kind of format war has already broken out between quantum computing development environments.

    One widely-used quantum SDK is IBM QISKit, an open-source Python-based development kit for prototyping software and devices on simulators or IBM Quantum Experience, a cloud-based quantum service. ExxonMobil, Goldman Sachs, Boeing, JP Morgan, Samsung and PayPal are just a few of IBM Quantum’s marquee customers. Google Cirq, another Python-based development kit, touts Volkswagen as a user, and Volkswagen is also a contributor to Google’s TensorFlow Quantum, an open source library that extends Google’s TensorFlow machine learning algorithms to quantum processors for rapid prototyping. Microsoft’s Quantum Development Kit (QDK) is based on Microsoft’s Q# programming language and brings quantum compatibility to Microsoft Azure cloud services. Early customers of Azure Quantum include Dow for chemical simulations and Toyota Tsusho, Toyota’s trading arm, developing traffic light optimizations to reduce urban traffic congestion.

    In a recent piece, The Information noted that “Amazon is looking to hire more than 100 new quantum computing scientists, hardware developers and engineers within its Amazon Web Services cloud business,” following the enormous amount of funding flowing to startups in the space, such as Quantum Motion, Atom Computing and PsiQuantum. Amazon also launched Amazon Braket, a fully managed quantum computing service that gives customers access to hardware from a diverse set of vendors such as IonQ, Rigetti and D-Wave. 

    It’s also possible that the most valuable quantum platforms won’t be versatile, horizontal platforms at all; rather, the space could evolve instead to include special-purpose machines, or quantum application-specific integrated circuits (ASICs). Unlike the way things have played out with classical computing, this could mean that quantum computers would be highly specialized integrated hardware and software machines tailored to specific applications, e.g. fluid simulation or fraud detection. 

    Early use cases may involve quantum systems taking specialized roles as part of a larger classical system, similar to how NVIDIA’s graphics processing units (GPUs), which often run alongside an advanced CPU, can be used to offload specific computations for which the GPU is well-suited. In this model, where quantum components are an “accelerator” to classical systems, a credit underwriter might use a quantum processor to handle one of the tasks involved in scoring applicants and might use a different special-purpose quantum processor to perform a piece of a calculation to optimize its borrower portfolio. 

    For many use cases, the error rates of calculations are still too high for developers to do more than hone their coding chops on quantum simulation platforms, waiting until quantum computers start to really work at scale. Quantum simulators, such as QMware, make quantum software algorithms and applications believe they are running on actual quantum hardware, but they are really running on classical computers pretending to be quantum computers. 

    In the meantime, to manage some of the timing uncertainty, quantum-related startups can pursue profitable projects with government, corporate and university buyers today as a source of funding and requirements. Like some biotech startups, they can do this while assembling a strong team and seeking an early adopter clientele, progressing through scientific milestones while keeping an eye on future commercial markets.  

    The hurdles to generally available quantum computers are much like those faced by alternative energy sources: Unlocking the potential will require breakthroughs in hardware design, lower manufacturing costs and a driving force to spur large-scale adoption. And many approaches won’t work. 

    And as with any technology, it is, of course, possible that software developers will find valuable applications for quantum processors that people haven’t thought of yet today—just as developers found that GPUs meant to handle graphics processing also excel at training highly parallelized machine learning models.

    That said, a broadly useful and effective quantum computer’s arrival has the potential to be as disruptive an opportunity as the creation of the Internet or the use of machine learning to make predictions. 

  • Five Technology Trends to Shape Future Investments

    Five Technology Trends to Shape Future Investments

    Humans invented technology to introduce innovative products in the market. Technology’s relentless cycle of innovation has now touched humanity, redefining what it means to be a human, a meaningful change that is impacting business priorities and is expected to do so in the future.    

    The most important tech investment for C-level executives relates to digital transformation, artificial intelligence (AI), Internet of Things (IoT) and cybersecurity. With 71% of the world expected to use mobile phones by 2024 and the successes of mobile banking in developing countries, experts predict that mobile and blockchain technologies will also join to bring about more financial inclusions.

    Here are five things that C-suite business people should invest in.

    Digital Transformation: Everything Online

    As one of the weapons in the arsenal of business innovation, digital transformation has become such a buzz word that it turns people on. According to a report by Forrester, 90% of the executives believe digital transformation is a necessity for their business, and around 60% believe they lag behind in digital transformation. By the end of 2020, a company would either have transformed or will be on its way to adapt to digital transformation.

    IoT: A Thousand Points of Entry?

    Business implementation of IoT and blockchain are very popular these days. However, if we look to enterprises it’s a different story. Costs, security and current political climate are the major hurdles for the enterprise-level implementation of IoT. To get the most out of this innovative technology, businesses need to determine what stands in their way and then work on strategies to sort things out. So until the issues are dealt with, IoT will be seen as a promising technology that’s not ready for prime time.

    AI: A Real Game-Changer

    Every so often a technology comes along that has the potential to completely change everything around it. The last such thing was cloud computing. Like cloud computing, AI has been around for a long time. AI, machine learning and deep learning are catching up with the initial promises and it will become more evident in the coming years.

    Most common usage of AI and machine learning will be seen in business intelligence, automating the repeated tasks, and making future prediction using the previous data. Previously, AI has been used for decision making using data analytics and this is expected to increase in the coming years with more businesses investing in the AI-driven decision-making system. Another use of AI that will be more common is digital identity verification for online businesses.

    Ultimately, AI/machine learning and deep learning will be seen more frequently in the years ahead. However, the promises it brings are far fetched and it’s worth waiting to see the true potential of AI.

    Quantum Computing: The Next Big Thing

    Currently, in R&D, quantum computing will open new ways by solving problems that require a lot of computation. Google’s investment in quantum R&D is expected to grow and more researchers will shift their focus from AI to quantum. With the emergence of 5G, the quantum research industry will see a boom.

    DevOps: For a Customer-Centric Approach

    According to the Forrester report, firms will embrace a customer-centric approach as the DevOps speeds flatter. DevOps continues to impress the heart and minds. However, with a slow-release speed, engineers are experiencing a stress point. DevOps teams must see this as an opportunity to approach DevOps differently. Within the next six to 12 months, DevOps adoption will change and the team will implement new strategies. Testing will revolve around previous radical methods and new technology will help developers to learn new skills.

    — James Efron

  • 4 Amazing Quantum Computing Applications

    4 Amazing Quantum Computing Applications

     Researchers across the globe, from the quantum researchers at University of Maryland synthesizing time crystals, to the research team at the University of New South Wales who are embedding atoms and their electrons into silicon, are pushing quantum computers forward, attempting to arrive at the most powerful quantum computing technology.

    IBM, Google and Microsoft are racing for quantum supremacy. IBM expects to create a 50-qubit general purpose quantum computer “within the next few years.” Indeed, the next iteration in quantum computing may be near.

    Why? Quantum computing could be the best path to ultra-powerful computers, and our best bet to solve unimaginably difficult problems. As our need for powerful processors continues to increase, and problems become larger in scope and complexity, we’ll need more complex computational systems to power solutions.

    Quantum Computers in Use

    Here are four amazing applications of quantum computers:

    Machine Learning

    Artificial intelligence needs to be able to pull from large datasets of image, video and text. Thankfully, there doesn’t seem to be a shortage of content. In fact, there may be an overabundance. Big data is out there to be analyzed, but we need more powerful computers to process the petabytes of unanalyzed data.

    Quantum computers could empower machine learning by enabling AI programs to search through these gigantic datasets concerning medical research, consumer behavior and financial markets—and make sense of them.

    Optimization

    Imagine, say, you are a traveling salesman. You wish to visit a handful of cities and want to know what the most optimal routes would be. This would be an example of an optimization problem. It sounds simple enough, but, in reality, the process can get quite involved as you up the number of variables. With only 270 destinations, for example, there are more combinations of travel than atoms in the universe.

    With quantum computers, however, we could expect a machine to be able to handle almost innumerable permutations and combinations, which could advance system design and analysis in massive ways.

    Biomedical Simulations

    With quantum computers, we can create, simulate and model molecular structures. Researchers at Harvard University used a D-Wave One quantum computer to solve the puzzle of how some proteins fold in 2012.

    “The model consisted of mathematical representations of amino acids in a lattice, connected by different interaction strengths,” writes Geoffrey Brumfiel in a news article for Nature about the Harvard researchers’ protein folding models. “The D-Wave computer found the lowest configurations of amino acids and interactions, which corresponds to the most economical folding of the proteins.”

    While the technology was nowhere near perfect—as the researchers cited that “10,000 measurements using an 81-qubit version of the experiment gave the correct answer just 13 times”—it was able to accomplish an amazing feat by modeling the behavior of protein folding with some degree of accuracy.

    Financial Services

    Quantum computing is already on its way. D-wave, a company backed by Goldman Sachs and Bezos Expeditions, among others, deployed its first commercial quantum computer: the D-Wave 2000Q, a quantum annealing system with 2000 qubits and advanced feature controls.

    Despite their prohibitive price, these computers are being utilized by a small niche, as illustrated by Harvard’s use of D-wave’s first model back in 2012. The systems could be used for complex financial modeling and risk management within the financial industry as well. Quantum computing could be used to find “new ways to model financial data” and isolate “key global risk factors,” according to IBM.

    “It would be great to build systems to help Wall Street better manage risk using this type of technology,” D-Wave Systems President and CEO Vern Brownell told Bloomberg. “They spend a lot on computing power [to manage risk].”

    Conclusion

    From managing money to massive datasets, quantum computing’s applications are seemingly endless. Advances in machine learning and optimization could boost efficiency dramatically. Biomedical and chemical simulations could help us land more drug discoveries and uncover new medical treatments in record time. Advanced computing power could lead to more than just innovation; it also could lead to lessened risk. Indeed, enhanced financial services could fundamentally change how we invest.

    As tech companies, computer scientists, and theoretical physicists help to perfect this technology, these truly amazing applications of quantum computing could become ubiquitous—and drive us further into a quantum age.

    — Ellie Martin

  • Mobility and Quantum Adaptation

    Mobility and Quantum Adaptation

    As you look at the preponderance of DevOps tooling solutions available today, you notice similarities in deployment automation and how they work. DevOps is purported to be the most significant catalyst for change in how we innovate in the last two decades. Yet, as you examine the tooling and the strengths and weaknesses of each, so many products are strikingly the same. It seems to me that if DevOps is to ingrain itself in IT culture, it must use its own principles to remain current, or remain fresh in the coming years. The doctors of IT must learn to use the tool of change to change it once again, and then again, and so on.

    Case in point: The deployment discipline of DevOps is premised largely on a “controlled” rollout. Deploying versions of code and versions of applications into production is a fully controlled event. Our entire release orchestration (RO) discipline is merely an extension of our basic deployment tooling. RO tools add governance and quality assurance (QA), segregating it directly from a specific deployment and handling it more broadly in a release event. But whether we are examining a deployment tool or release orchestration software, both rely upon a premise of controlled distribution of applications to their respective destination.

    The Mobility Disruption

    Mobility, or mobile apps, disrupt this thinking. The platform our Apple partners have given us (in its current form, though I understand that is evolving as we speak), is based on an “at will” acceptance of a new version of a given application. When I look at my phone, I see a red balloon informing me of how many application “updates” are available for me to accept. I can decline an upgrade just by not taking any action. This is true, even of the newest version of the Apple OS (the foundational building block of application capabilities). Or, when I am home on my Wi-Fi network, I can at my leisure begin downloading and updating the various software application versions to the latest offerings. All are my actions, determined completely by my choice.

    It’s a little different on my PC—I have more flexibility of what version I have running on this platform, at least in terms of rollback. If I do not like the next version of my web browser, I can pull up the site and choose an older version to use on my PC, until the errors that bothered me are resolved in the latest offering. Mobile apps, in the mobile catalog so many of us use on our phones, offer no such options. Once I have updated the application to the latest offering, I am stuck with it, errors and all. If it becomes too intrusive to my work efforts, I would have to pick something else to take its place, or just stop using it and wait until a new version is available again.

    DevOps deployment tooling is foreign to this model. The servers in production do not get a choice about whether they intend to accept our deployments, and when they might get around to it. They do what we tell them to do, and when we tell them to do it. But it seems updating our servers, upon which future versions of applications will rely, does not cover the last mile of our application delivery. Mobility is accomplished ad hoc, by consent, and without controls on my part.

    This has an immediate impact to my thinking about application distribution (and how DevOps will play in this new world). If I make a catastrophic change to the servers my mobile apps rely upon (that is, a change where only the new version of the app works), it will require my users to upgrade, or stop using the product as it sits today. This is dangerous if my application is utilitarian (not critical to daily life), and could be replaced by a competitor. It might mean that my applications must seek out version-specific back ends if I want to have multiple versions still working until people get around to upgrading. That costs time, money and support. Either case forces me to think differently about distribution.

    You can imagine how angry you might be if your online banking software just stopped working. Mobility in its current form also makes rolling back to a previous version a near impossibility. So if the new version of the app has a bug in it that prevents key functionality, I am going to get a huge black eye with my customers, and must race to produce a new version minus the old bug, even assuming universal adoption. Alternatively, my customers may be using the buggy version for a while before they choose to upgrade to the bug-free version.

    There is tooling for corporations to more tightly control a provided device or even govern what happens to personal devices for corporate software. But the ability for DevOps deployment solutions to integrate with a mobility platform requires a new way of thinking. The limitations and the new capabilities must be taken into account specifically before any vendor can legitimately offer a holistic solution for DevOps for mobile.

    The Quantum Obliteration

    Quantum computing is likely to prove once again to be another disruption to our DevOps model. Evolution will help us bridge the gaps and minimize the impacts. But consider that quantum systems are not based on a foundational binary model. This is not like English speakers having to learn the Cyrillic version of the alphabet to read and write in Russian. No, it is more like communication will be telepathic from now on. The entire constructs of binary will eventually fade into the history books, and a new construct will emerge in its place.

    Today we use binary-based computing systems to attempt to converse and control the quantum systems built so far. When you don’t know how to communicate telepathically, you use what you do know: reading and writing. But it will take a long time before the power of quantum computing is truly unlocked; until then, we have to constrict its capabilities with binary-based computing methods. When native quantum languages, and perhaps hardware, are let loose, the true power of the platform will be better understood and impacts experienced.

    How will DevOps play in a quantum computing environment? Will the fundamentals about deployment and distribution once again evolve to incorporate the impacts of new quantum platform capabilities such as entanglement? Or will we experience what we have in the mobile platforms of today: a series of catchup steps to deal with what is different? If we use our catalyst of change in DevOps to begin to think differently about our own future and the capabilities our tools should provide, perhaps DevOps will still be unlocking possibilities none of us dream of today. If not, I foresee DevOps becoming only another fad, replaced by the term we give to the next wave of innovation thought needed to unlock our future.

    To continue the conversation, feel free to contact me.