The Quantum Decade

 

Find out how you can be quantum-ready—and how this bleeding-edge technology can help you and your business thrive the moment quantum computers come of age. Because that moment is closer than you think.

For decades, quantum computing has been viewed as a futuristic technology: it would change everything, if it ever moved from the fantastical to the practical. Even in recent years, despite billions of dollars in research investment and extensive media coverage, the field is sometimes dismissed by real-life decision makers as too arcane, a far-off, far-out pursuit for academics and theorists. As we enter the Quantum Decade—the decade when enterprises begin to see business value from quantum computing—that perspective is quickly becoming an anachronism.

Insights

• Priorities of a post-pandemic world. As entire industries face greater uncertainty, business models are becoming more sensitive to and dependent on new technologies. Quantum computing is poised to expand the scope and complexity of business problems we can solve.
• The future of computing. The integration of quantum computing, AI, and classical computing into hybrid multicloud workflows will drive the most significant computing revolution in 60 years. Quantum-powered workflows will radically reshape how enterprises work.
• The discovery-driven enterprise. Enterprises will evolve from analyzing data to discovering new ways to solve problems. When combined with hyper-automation and open integration, this will ultimately lead to new business models.

Because quantum computing is coming of age, and leaders who do not understand and adapt to the Quantum Decade could find themselves a step—or more accurately, years—behind. Over the next few years, we foresee a profound computing revolution that could significantly disrupt established business models and redefine entire industries.

Historically, crises have been the impetus for both new technologies and their widespread adoption. World War I ushered in factory processes that are still in place today. The Cold War accelerated the creation of the Advanced Research Projects Agency Network (ARPANET), a predecessor to the internet, in the late 1960s. And now COVID-19 has driven an increased need for agility, resiliency, and accelerated digital maturity. We anticipate quantum computing—in combination with existing advanced technologies—will dramatically impact how science and business evolve. By accelerating the discovery of solutions to big global challenges, quantum computing could unleash positive disruptions significantly more abrupt than technology waves of the past decades.

Understanding the exponential power of quantum computing

Classical computer bits can store information as either a 0 or 1. That the physical world maintains a fixed structure is in keeping with classical mechanics. But as scientists were able to explore subatomic matter, they began to see more probabilistic states: that matter took on many possible features in different conditions. The field of quantum physics emerged to explore and understand that phenomena.

The power of quantum computing rests on two cornerstones of quantum mechanics: interference and entanglement. The principle of interference allows a quantum computer to cancel unwanted solutions and enhance correct solutions. Entanglement means the combined state of the qubits contains more information than the qubits do independently. Together, these two principles have no classical analogy and modeling them on a classical computer would require exponential resources. For example, as the table below describes, representing the complexity of a 100-qubit quantum computer would require more classical bits than there are atoms on the planet Earth.

To the nth degree: The power of exponential

The building blocks of quantum computing are already emerging. Quantum computing systems are running on the cloud at an unprecedented scale, compilers and algorithms are rapidly advancing, communities of quantum-proficient talent are on the rise, and leading hardware and software providers are publishing technology roadmaps. The technology's applicability is no longer a theory but a reality to be understood, strategized about, and planned for. And good news: the steps you should take to prepare for future quantum adoption will begin to benefit your business now.

Quantum computing will not replace classical computing, it will extend and complement it. But even for the problems that quantum computers can solve better, we will still need classical computers. Because data input and output will continue to be classical, quantum computers and quantum programs will require a combination of classical and quantum processing.

The steps you should take to prepare for future quantum adoption will begin to benefit your business now.

It is precisely the advances in traditional classical computing, plus advances in AI, that are driving the most important revolution in computing since Moore’s Law almost 60 years ago. Quantum computing completes a trinity of technologies: the intersection of classical bits, qubits, and AI “neurons.” The synergies created by this triad—not quantum computing alone—are driving the future of computing.

The most exciting computing revolution in 60 years: Three major technologies converge

The IBM Institute for Business Value (IBV) has been deeply engaged in conducting more than a dozen industry- and practice-based studies on quantum computing. We’ve elevated that research here with new insights gleaned from interviews with more than 50 experts, including IBM quantum computing researchers as well as clients, partners, and academics. This report on the Quantum Decade provides executives with strategies to prepare for the upcoming business transformation from quantum computing. It identifies the most important factors, themes, and actions to take at this significant inflection point.

The path to Quantum Advantage

What makes this the Quantum Decade? What will the quantum-powered world look like? And what can and should farsighted leaders and organizations do now to educate and position themselves effectively? The key learnings revolve around three phases of organizational evolution.

Phase 1: Awareness

According to the IBV’s 2021 CEO study, 89% of the more than 3,000 chief executives surveyed did not cite quantum computing as a key technology for delivering business results over the next two to three years. For the short term, that’s understandable. But quantum computing with 1,000 qubits is projected to be available as early as 2023—just a few years away. Given the technology’s disruptive potential this decade, CEOs should start mobilizing resources to grasp early learnings and start the journey to quantum now. CEOs who ignore quantum’s potential are taking a substantial risk, as the consequences will be much greater than missing the AI opportunity a decade ago.

Phase 1 of the quantum computing playbook requires broad recognition that the landscape is changing. The primary shift is a computing paradigm that’s evolving from an age of analytics (looking back at established data and learning from it) to an age of discovery (looking forward and creating more accurate models for simulation, forecasting, and optimization). There’s real potential for uncovering solutions that were previously impossible.

Phase 2: Readiness 

Enterprises cannot use quantum computing to solve big problems yet. But quantum computing has shattered timelines and exceeded expectations at every phase of development. It’s not too soon for organizational leaders to explore how the advent of this new technology could alter plans and expectations. Phase 2 involves investigating big questions: How could your business model be disrupted and reshaped? How could quantum computing supercharge your current AI and classical computing workflows? What is the quantum computing “killer app” for your industry? How can you deepen your organization’s quantum computing capabilities, either internally or through ecosystems? Now is the time to experiment and iterate with scenario planning. Find or nurture talent who is fluent in quantum computing and capable of educating internal stakeholders about the possibilities, and partner for “deep tech” quantum computing resources.

But just as important is another critical question: What does your organization need to establish now to apply quantum computing when it’s production-ready? Indeed, laying the foundation for quantum computing also means upping your classical computing game. Enhanced proficiencies in data, AI, and cloud are necessary to provide the required fertile ground for quantum computing. Accelerating your digital transformation in the context of quantum computing readiness will provide a pragmatic path forward while delivering significant benefits now. After all, quantum computing doesn’t vanquish classical computing. The trinity of quantum computing, classical computing, and AI form a progressive, iterative partnership in which they’re more powerful together than separately.

Phase 3: Advantage

Phase 3, Quantum Advantage, occurs when a computing task of interest to business or science can be performed more efficiently, more cost effectively, or with better quality using quantum computers. This is the point where quantum computers plus classical systems can do significantly better than classical systems alone. As hardware, software, and algorithmic advancements in quantum computing coalesce, enabling significant performance improvement over classical computing, new opportunities for advantage will emerge across industries. But prioritizing the right use cases—those that can truly transform an organization or an industry—is critical to attaining business value from quantum.

Getting to Quantum Advantage will not happen overnight. But while that advantage may progress over months and years, it can still trigger exponential achievements in usage and learning. From exploring the creation of new materials to personalized medical treatments to radical shifts in business models across the economy, change is coming. Organizations that enhance their classical computing capabilities and aggressively explore the potential for industry transformation will be best positioned to seize Quantum Advantage.

Key Takeaways

• Tackling the world’s problems. From discovering new drugs to managing financial risk to re-engineering supply chains, there is an urgency to accelerate solutions to increasingly complex societal, macroeconomic, and environmental problems on a global scale.
• The 1,000-qubit milestone. Quantum computing hardware is on a trajectory to scale from 127 qubits in 2021 to 1,000 qubits by 2023 to practical quantum computing, characterized by systems executing error-corrected circuits and widespread adoption, by 2030. Cloud-based open-source development environments will make using quantum computers “frictionless.”
• The hybrid multicloud future. Many quantum programs involve interactions between classical and quantum hardware. But these interactions introduce latencies, or delays, which must be reduced to optimize capacity. This makes hybrid multiclouds the most viable future for quantum computing.
• The power of ecosystems. Quantum computing ecosystems—with opportunities for collaborative innovation and open-source development—are fast becoming fertile grounds for training users to apply quantum computing to real problems.

Download the full report to learn how you can guide your organization to Quantum Advantage.

https://ibm.co/2URvrDe

This chart shows the connections between cybercrime groups

 CrowdStrike puts together a list of connections and how cybercrime groups cooperate with each other

By Catalin Cimpanu for Zero Day 

Cybersecurity reports often talk about threat actors and their malware/hacking operations as self-standing events, but, in reality, the cybercrime ecosystem is much smaller and far more interconnected than the layperson might realize.

Cybercrime groups often have complex supply chains, like real software companies, and they regularly develop relationships within the rest of the e-crime ecosystem to acquire access to essential technology that enables their operations or maximizes their profits.

According to cybersecurity firm CrowdStrike, these third-party technologies can be classified into three categories: services, distribution, and monetization.

Breaking down each, the services category usually includes:

• Access brokers - threat actors who breach corporate networks and sell access into a company's internal network to other gangs.
• DDoS attack tools - also known as DDoS booters or DDoS-for-hire, these groups provide access to web-based panels from where anyone can launch a DDoS attack against a target.
• Anonymity and encryption - threat actors who sell access to private proxy and VPN networks, so hackers can disguise their location and origin of their attacks.
• Phishing kits - threat actors who create and maintain phishing kits, web-based tools used to automate phishing attacks, and the collection of phished credentials.
• Hardware for sale - threat actors who sell custom-made hardware, such as ATM skimmers, network sniffing devices, and more.
• Ransomware - also known as Ransomware-as-a-Service, or RaaS, these groups sell access to ransomware strains or a web-based panel where other gangs can build their own custom ransomware.
• Crime-as-a-Service - similar to RaaS, but these groups provide access to banking trojans or other forms of malware.
• Loaders - also known as "bot installs," these are threat actors who already infected computers, smartphones, and servers with their own malware and offer to "load/install" another group's malware on the same system, so the other group can monetize it through ransomware, banking trojans, info-stealers, etc.
• Counter antivirus service/checkers - these are private web portals where malware devs can upload their samples and have them tested against the engines of modern antivirus systems without the fear of the malware's detection being shared with the AV maker.
• Malware packing services - these are web-based or desktop-based tools that malware developers use to scramble their malware strain's code and make it harder to detect by antivirus software.
• Credit/debit card testing services - these are tools that hackers use to test if the payment card numbers they acquired are in a valid format and if the card is (still) valid.
• Webinject kits - these are specialized tools, usually used together with banking trojans, to allow a banking trojan gang to insert malicious code inside a victim's browser while they visit an e-banking (or any other) site.
• Hosting & infrastructure - also known as bulletproof hosting providers, their name is self-evident as they provide private web hosting infrastructure specifically tailored for criminal gangs.
• Recruiting for criminal purposes - these are specialized groups that recruit, bribe, or trick normal citizens into participating in a cybercrime operation (e.g., someone who travels to the US in an attempt to bribe a Tesla employee to run a malicious tool inside the company's internal network).

On the other hand, distribution services include the likes of:

• Groups that run spam campaigns on social networks or instant messaging apps.
• Groups specialized in email spam distribution.
• Groups who develop and sell exploit kits.
• Groups who purchase traffic from hacked sites and distribute it to malicious web pages that usually host exploit kits, tech support scams, financial scams, phishing kits, and others.

As for monetization services, Crowdstrike says this category usually includes:

• Money mule services - groups who offer to physically show up and pick up money from hacked ATMs, receive money in their bank accounts, and then redirected to the hackers, their preferred money laundering or reshipping fraud service.
• Money laundering - groups who often operate networks of shell companies through which they move funds from hacked bank accounts, ATM cash-outs, or cryptocurrency heists. Some money laundering services also operate on the dark web as Bitcoin mixing services.
• Reshipping fraud networks - groups that take stolen funds, purchase real products, ship the products to another country. The products, usually luxury goods like cars, electronics, or jewelry, are then resold and converted into clean fiat currency that's transferred to the hackers who contracted their services.
• Dump shops - groups that sell data from hacked companies via specialized websites and social media channels.
• Ransom payments & extortion - groups specialized in extorting victims, and which can be contracted by other gangs in possession of stolen data.
• Collection and sale of payment card information - also known as carding shops, these are typically forums where cybercrime groups go to sell stolen payment card data.
• Cryptocurrency services - a form of money laundering, these services offer to "mix" stolen funds and help hackers lose the trail of stolen funds.
• Wire fraud - as the name says, groups that are specialized in performing wire fraud, such as BEC scams.

Image: CrowdStrike

Tracking all the connections between groups and their suppliers and who works with who is almost impossible today due to the broad use of encrypted communication channels between parties.

However, in the realm of malware attacks, some signs of cooperation can be observed by the way the malware moves from attackers to infected hosts.

Although these connections can never be fully verified, it's also pretty obvious that when the Emotet malware is downloading the TrickBot malware that the two gangs are cooperating as part of a "loader" mechanism provided by the Emotet crew for the TrickBot gang.

In its 2021 Global Threat Report, released on Monday, security firm CrowdStrike has, for the first time, summarized some of the connections that currently exist on the cybercrime underground between various e-crime operators.

The company uses its own nomenclature for e-crime groups, so some group names might sound different from what we've seen before. However, CrowdStrike also provides an interactive index so anyone can learn more about each group and link it to the names used by other companies.

mage: CrowdStrike

What the chart above shows is that enablers play just as important a role in cyber-intrusions as the groups executing the intrusion.

As Chainalysis pointed out in a separate report last month, law enforcement agencies are most likely to achieve better results in disrupting cybercrime operations when targeting these shared service suppliers, as they could end up disrupting the activities of multiple cybercrime groups at once.

Furthermore, there are also other benefits. For example, while top-tier cybercrime gangs often have top-notch operational security (OpSec) and don't reveal any details about their operations, targeting lower-tier enablers, who don't always protect their identities, could providing law enforcement agencies with data that could help them unmask and track down the bigger groups.

5 technology trends for the roaring 20s, part 1: Blockchain, cloud, open source

Data is changing the paradigm in everything from business to social interactions. Here is what will shape the data landscape for the years to come.

By George Anadiotis for Big on Data

The original roaring 20s were 100 years ago, but we may be about to see a new version of this... And if it's going to be "a period of economic prosperity with a distinctive cultural edge," then both the economic and the cultural aspects will be all about data. 

Data is shaping a new culture, bringing about a new way of doing business, a new way of decision making, new applications and infrastructure, and is an enabler for the transition to AI. Data is the focal point of our coverage on Big on Data, so following up on Andrew Brust and Tony Baer's predictions, here's our own round of things to keep an eye on in the 2020s.

This is the first part -- including trends 5, 4, and 3. The second part -- including trends 2, 1, and a bonus -- will follow suit in a few days.

5. It's the end of blockchain as we know it, and I feel fine

Do you remember blockchain? 2017, 2018, and the Bitcoin and ICO craze seem like a lifetime ago. Back in early 2017, we noted that blockchain is, in essence, a distributed database, with some unique properties that databases typically lack: 

Immutability, where written data is "forever" tamper-resistant. The ability to create and transfer assets on the network, without reliance on a central entity, based on a decentralized consensus mechanism. This is what powered Bitcoin, which opened up the realm of possibilities, but also spurred an immense wave of speculation, ignorance, and fraud. 

Today, blockchain seems to have hit rock bottom. Gartner is placing blockchain at the bottom of the Trough of Disillusionment. Scams such as Onecoin and Bitfinex - Tether exemplify the Wild West that blockchain has become, and Facebook's Libra is not getting traction either -- which is a good thing. 

Blockchain will have a transformational impact across industries in five to 10 years.

Even leaving speculators out of the picture. However, there still are technical issues to resolve. A database with poor performance, inability to interface with the outside world, and no query language to speak of is not exactly a solid substrate for applications. This is where blockchain is today. But here comes the good news: Yes, blockchain will have a transformational impact across industries in five to ten years.

Behind the scenes, people are working to resolve issues. To name some examples: BigchainDB is still working on performance. Chainlink launched an Oracle service to connect Ethereum blockchain to the world. The Graph is using GraphQL to provide an open-source query protocol for blockchains. Alternatives such as IOTA are exploring different data structures.

Although enterprise blockchain implementations do exist -- check JP Morgan's Quorum or Oracle'blockchain Platform -- the blockchain landscape in the 2020s will probably be very different. It may not even go by the same name: Distributed Ledger Technology is a better match for a technology, which may not really be a blockchain anymore. DLTs will be front and center in the 2020s.

4. On cloud No. 9... or, on how many clouds actually?

This one should be rather familiar. The gradual move of applications and data to the cloud has been a recurring theme on Big on Data. A decade ago, for most businesses, the cloud was not really an option for data and compute at scale. Hadoop was all the rage - a synonym for Big Data, for all intents and purposes. Today's world is very different.

The cloud is now the de facto data lake. Hadoop itself has gone cloud and consolidated. In the 2020s, as Tony Baer put it, hybrid cloud and multi-cloud will be the default option. Cloud providers have been adding databases and data management tools in their offerings at a staggering rate.

In addition, cloud storage, which is where it all started, is becoming more sophisticated, too. Amazon Athena has added SQL query capabilities on top of AWS S3. The open-source Delta Lake project unifies cloud storage and data warehouses. So cloud storage looks more like a database, and cloud providers offer data management solutions, too. 

Cloud adoption is growing.

(Image: maxsattana, Getty Images/iStockphoto)

But mobility goes both ways. Every database today has a managed offering in the cloud or is in the process of getting one. With databases getting increasingly complex to manage, and the ability to get on-demand storage and compute for their needs offering major advantages, managed databases in the cloud are a natural evolution.

Today, when choosing a data management solution, the shortlist almost always includes an offering by a cloud vendor. Being cloud-native, managed, and billed via a single control plane, and having multi-region availability makes cloud vendor offerings attractive. On the other hand, those offerings are not multi-cloud and are not always the best of the breed.

3. Cloud does not pay; open source is not free

The future of databases is the cloud. But the future of databases is also something else: it is open source. By 2022, more than 70% of new in-house applications will be developed on an open-source database, and 50% of existing proprietary relational database instances will have been converted or be in the process of converting.

In other words, open-source is winning, in databases and beyond. There are some very good reasons why this is happening: low barrier to entry, community, innovation, interoperability. But the fact that open source is becoming the norm in enterprise software has side effects, too. To put it simply: AWS is eating open-source software because it can.

Following customer demand, AWS has, over the last few years, added every single top open=source database to its arsenal as a managed service. This is a rather complicated issue, which we first touched on in May 2019. We are glad to see it been taken up by the likes of The New York Times, and the debate around the issue is heating up. 

Monetizing open source is complicated, but getting a viable open source business model in place is vital for open source sustainability.

(Image: 451 Group)

This is really about much more than databases and vendors. What this really is about is business models around shared resources and fair contribution and reward around those shared resources. Open-source software is free as in speech, but not free as in beer. Someone has to build the software, and then someone has to maintain, run, and manage it.

So, it all comes down to how much each actor gives and takes, and whether this should somehow be accounted for. Clearly, this is a much broader topic than what we could possibly address here, so a piece crystallizing thoughts and exchanges on the topic is due. In the meanwhile, let us be reminded of another example of a "free" shared resource -- the web.

Much of the trouble with the web and the monopolies built around it today stem from the failure to operate a workable business model around it. In its absence, the Googles and Facebooks of the world have been eager to step up and fill that void, dominating the web and installing ad-based empires in the process. Making the same mistake twice would not be wise, and this deserves to be a top priority for the 2020s. 

Бесплатные курсы программирования от самых лучших ВУЗов мира: подборка

Подборка из 21 бесплатного курса от Гарвардского, Принстонского, Колумбийского, Брауновского и других лучших университетов США.

РБК опубликовал подборку из ста с лишним бесплатных образовательных курсов самых престижных вузов США. Мы отобрали те их них, которые касаются IT. Само обучение ничего не стоит, но для некоторых курсов есть возможность получения за плату сертификата о прохождении. Со знанием английского открыты все пути.

Курс № 1: введение в разработку игр

Чему научитесь: разрабатывать интерактивные 2D и 3D-игры

Вуз: Гарвардский университет

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Курс № 2: веб-программирование с использованием Python и JavaScript

Чему научитесь: делать web-приложения на Python, JavaScript и SQL с использованием фреймворков Flask, Django и Bootstrap

Вуз: Гарвардский университет

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Курс № 3: разработка мобильных приложений с помощью React Native

Чему научитесь: работать с платформой React Native от Facebook, создавать кроссплатформенные нативные приложения с использованием JavaScript без задействования Java или Swift

Вуз: Гарвардский университет

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Курс № 4: использование Python для исследований

Чему научитесь: работать с Python 3

Вуз: Гарвардский университет

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Курс № 5: основы программирования и компьютерных наук

Чему научитесь: алгоритмическому мышлению, основным концепциям и понятиям в программировании

Вуз: Гарвардский университет

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Курс № 6: основы R

Чему научитесь: программировать на языке R на начальном уровне

Вуз: Гарвардский университет

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Курс № 7: инструменты для повышения производительности

Чему научитесь: управлять файлами и хранилищами с помощью Unix / Linux, использовать GitHub

Вуз: Гарвардский университет

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Курс № 8: машинное обучение

Чему научитесь: разрабатывать системы рекомендаций, используя алгоритмы

Вуз: Брауновский университет (начальный курс машинного обучения можно пройти также в Гарвардском и Колумбийском университетах)

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Курс № 9: обучение с подкреплением

Чему научитесь: одному из способов машинного обучения (надо уже уметь программировать на Java и сначала пройти курс машинного обучения)

Вуз: Брауновский университет

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Курc № 10: обучение без учителя

Чему научитесь: методам рандомизированных алгоритмов, кластеризации, а также отбора и преобразования признаков.

Вуз: Брауновский университет

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Курс № 11: искусственный интеллект

Чему научитесь: основным методам построения интеллектуальных компьютерных систем

Вуз: Колумбийский университет

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Курс № 12: анимация и CGI-графика

Чему научитесь: создавать реалистичные анимации с акцентом на технические аспекты CGI-графики

Вуз: Колумбийский университет

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Курс № 13: статистическое мышление

Чему научитесь: собирать данные, извлекать информацию из визуализации данных, строить модели для прогнозирования

Вуз: Колумбийский университет

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Курс № 14: модульное программирование

Чему научитесь: работать с концепцией модульного программирования на примере языка Си

Вуз: Дартмутский колледж

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Курс № 15: расширенные типы данных

Чему научитесь: определять тип данных в языке Си и на основе этого оптимизировать хранение и обработку данных

Вуз: Дартмутский колледж

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Курс № 16: знакомство с Linux

Чему научитесь: использовать интерфейс командной строки и основные команды Linux

Вуз: Дартмутский колледж

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Курс 17: анализ алгоритмов

Чему научитесь: делать количественные прогнозы комбинаторных структур (рекомендуется сначала пройти первый и второй вводный курс)

Вуз: Принстонский университет

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Курс № 18: биткоин и криптовалютные технологии

Чему научитесь: разрабатывать безопасное ПО для взаимодействия с сетью биткоин

Вуз: Принстонский университет

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Курс № 19: web-программирование на JavaScript

Чему научитесь: делать веб-страницы с использованием HTML и CSS, разрабатывать динамические интерактивные веб-страницы

Вуз: Пенсильванский университет

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Курс № 20: введение в электронные таблицы и модели

Чему научитесь: работать с Excel и Sheets, сопоставлять данные и прогнозировать с помощью электронных таблиц,

Вуз: Пенсильванский университет

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Курс № 21: основы разработки ПО на Java

Чему научитесь: лучшим практикам современной разработки ПО, объектно-ориентированному программированию на Java

Вуз: Пенсильванский университет

https://bit.ly/38cMfG5