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In an era marked by exponential technological advancements, the convergence of quantum computing and data science is a pivotal point of transformation. The synergy between these two fields promises to revolutionize how we process, analyze, and extract insights from massive datasets. With quantum computings unique ability to tackle complex computations at speeds previously considered unattainable, the future of data science is poised for unprecedented innovation.

Quantum computing, an intricate branch of computation that capitalizes on the principles of quantum mechanics, is redefining the limits of computation. At its core are quantum bits or qubits, which, unlike classical bits, can exist in multiple states simultaneously thanks to superposition.

Quantum entanglement, another fundamental property, allows qubits to become interconnected, irrespective of distance, enabling intricate computations.

Data science is a cornerstone of decision-making, predictive analytics, and pattern recognition across industries. However, processing vast amounts of data efficiently and effectively has posed challenges, with traditional computing methods struggling to keep up.

The algorithms powering data analysis, machine learning, and artificial intelligence have thrived but are constrained by the limitations of classical hardware.

The marriage of quantum computing and data science promises to overcome these limitations and drive innovation to unprecedented levels. Quantum computings potential to perform complex calculations exponentially faster than classical computers presents an opportunity to accelerate data science applications like data analysis and decision-making processes.

The synergy between quantum computing and data science encompasses quantum-enhanced machine learning algorithms, more efficient optimization techniques, and innovative data clustering and dimensionality reduction approaches.

The impacts of quantum computing on data science are already manifesting across various domains. Here are some notable real-world applications of quantum computing in data science:

Breaking current encryption standards. Current encryption standards, such as RSA and ECC, are based on mathematical problems that are believed to be difficult to solve for classical computers.

In 2016, a team of researchers from Google AI announced that they had used a quantum computer to break a weakened version of the RSA encryption standard. This was a significant milestone, showing that quantum computers could break current encryption standards.

Developing new, more secure encryption standards. Quantum computing could also be used to develop new, more secure encryption standards that are resistant to attack by quantum computers. These new standards are based on mathematical problems intractable for quantum computers.

Simulating the behavior of molecules. Quantum computers can be used to simulate the behavior of molecules with unprecedented accuracy. This could help scientists to design new drugs that are more effective and less toxic. Quantum computers can simulate the interactions of drugs with proteins, which is a critical step in drug discovery.

Image Credit: Matt Swayne/The Quantum Insider

In 2019, Google AI announced that it had used a quantum computer to simulate a molecule of hydrogen for the first time. This was a significant milestone, as it showed that quantum computers could be used to simulate the behavior of molecules, which is a critical task in drug discovery and materials science.

Finding new drug targets. Quantum computers could be used to find new drug targets, the molecules that drugs interact with, to produce their effects. This could help scientists develop new drugs for diseases without known treatment. For example, quantum computers could screen large libraries of molecules to find those that interact with a specific protein target.

In 2020, a team of researchers from the pharmaceutical company AstraZeneca used a quantum computer to find new drug targets for cancer. This was the first time a quantum computer had been used to find new drug targets.

Some of the best schools in health informatics, like Stanford University and Johns Hopkins University, are already using quantum computing for drug discovery in their curricula.

Portfolio optimization. Quantum computers could optimize investment portfolios by finding the best combination of assets to minimize risk and maximize return. This could help investors to make better investment decisions and to improve their returns.

In 2019, a team of University of Waterloo researchers used a quantum computer to develop a new algorithm for portfolio optimization. This algorithm found better investment portfolios than traditional algorithms and could be used to improve the returns of investment funds.

Financial trading. Quantum computers could be used to develop new financial trading strategies that are more efficient and profitable. For example, quantum computers could analyze large amounts of market data to identify trading opportunities that traditional methods would miss.

In 2020, a team of researchers from the Massachusetts Institute of Technology (MIT) used a quantum computer to develop a new algorithm for financial trading. This algorithm could identify trading opportunities that were missed by traditional methods and could be used to generate profits for financial institutions.

Photo by Brian McGowan on Unsplash

Improved weather forecasting. Quantum computers could simulate the weather more accurately than classical computers. This could help to improve forecasts of extreme weather events, such as hurricanes and tornadoes. It could also improve climate change forecasts, which could help us mitigate its effects.

In 2019, a UC Berkeley team of researchers used a quantum computer to develop a new algorithm for weather forecasting. This algorithm produced more accurate forecasts than traditional algorithms, and it could be used to improve preparedness for extreme weather events.

Mitigation of climate change. Quantum computers could be used to develop new ways to mitigate the effects of climate change. For example, quantum computers could design new materials more efficiently, capturing and storing carbon dioxide.

In 2020, a team of researchers from the National Center for Atmospheric Research (NCAR) used a quantum computer to develop a new algorithm for weather forecasting. This algorithm was able to produce more accurate forecasts than traditional algorithms.

Training machine learning models more quickly and efficiently. Quantum computers could train machine learning models more quickly and efficiently than classical computers. Quantum computers can perform tasks much faster than classical computers, such as searching large datasets.

In 2019, a team of researchers from Google AI used a quantum computer to train a machine learning model to classify images of handwritten digits. This was a significant milestone, showing that quantum computers could be used to train machine learning models.

Developing new machine learning algorithms. Quantum computers could be used to develop new machine learning algorithms that are more powerful and efficient than traditional algorithms. This is because quantum computers can exploit the inherent parallelism of quantum mechanics to solve certain problems more efficiently.

In 2020, a team of researchers from the University of Toronto used a quantum computer to develop a new machine learning algorithm for natural language processing. This algorithm achieved state-of-the-art results on a natural language processing task.

Despite the promises, challenges remain on the path to fully realizing the potential of quantum computing in data science. Current quantum computing technologies are still nascent, prone to errors, and require sophisticated error correction methods.

Integrating classical and quantum computing architectures poses significant technical hurdles, and ethical considerations loom over the implications of quantum-enhanced data analysis.

The future holds immense potential for the growth of quantum computing in data science. Continued advancements in quantum hardware, coupled with novel error mitigation techniques, are expected to improve the reliability of quantum systems.

Collaborations between quantum computing and data science communities will foster innovation in algorithm development, leading to more efficient quantum machine learning models. As we explore hybrid quantum-classical data analysis pipelines, the boundaries of what we can achieve in data science will continue to expand.

A new frontier of possibilities emerges in the interplay between quantum computing and data science. The transformational impacts of quantum computing on the future of data science are undeniable.

As we venture into this uncharted territory, researchers, scientists, and industry leaders must collaborate to harness the full potential of quantum computing to solve complex problems, redefine data analysis paradigms, and reshape decision-making across domains. The journey ahead involves innovation, exploration, and the relentless pursuit of uncovering hidden insights within vast datasets.

Link:
The Impacts of Quantum Computing on the Future of Data Science - Data Science Central

One of the most infamous cyberattacks on critical infrastructure, or CI, occurred in May 2021, when the Colonial Pipeline was hit with ransomware. The breach resulted in shutdown of pipeline operations, a gasoline shortage and a spike in fuel prices.

But the attack, which targeted billing systems, didnt cause the shutdown. Rather, the pipelines operators turned off pumping systems over concerns the attackers could gain control of operational technology, or OT, and place public safety at risk.

The incident illustrates the unique issues that are involved in cybersecurity for CI such as petroleum pipelines, power stations, electric utilities, water treatment plants, dams, ports, and mass transport systems. Exploits that target IT might result in exposed data or business disruption. Attacks involving OT could result in injury, illness, or worse across cities or regions.

Thats why operators of CI manage OT differently from how typical organizations handle IT. Most enterprises continually upgrade systems, with a focus on protecting data. CI operators deploy systems once and hope not to change them for years, with an emphasis on maintaining safety.

But OT-specific approaches are no longer adequate for safeguarding CI, for two reasons. First, OT and IT are becoming interconnected as OT becomes digitized. Second, quantum computing could soon render existing password and data encryption strategies obsolete.

In response, CI operators should borrow approaches from IT security protocols but apply them in OT-specific ways. In particular, they need to conduct thorough risk assessments, embrace zero trust security, and implement micro-segmentation to safeguard CI.

Where IT people talk about 5 nines of uptime, or 99.999% availability, OT pros think in terms of 11 nines. Both groups use the term reliability, but the difference in degree becomes a difference in kind.

Its part of why OT managers adhere to the Purdue Model, a framework for industrial control system security, developed at Purdue University in the 1990s. The Purdue Model emphasizes segmentation of operations, processes, controls, and sensors to protect OT from cyberattacks. OT is completely isolated from IT, with the equivalent of a demilitarized zone between them.

The Purdue Model remains a bedrock of OT security. But its no longer sufficient, because OT properties are no longer truly separated from IT. OT systems rely on expanding networks of IoT devices. Theyre increasingly monitored over remote connections. Some are disconnected from the internet but connected to corporate IT. Others are cut off from IT but exposed to the internet.

Today, CI needs a holistic approach to OT security that adapts traditional IT cyber practices to overcome the shortcomings of piecemeal OT protections.

Strengthening OT security for the quantum era starts with risk assessment. Many organizations lack a clear picture of how their OT systems are vulnerable and the potential consequences of those vulnerabilities.

CI organizations can leverage assessment tools designed for IT security by using these tools to identify all the resources on the network, down to the firmware level, and uncover security gaps. Keep in mind that if an assessment tool can find a resource on the network, so can an attacker.

An effective tool should provide the organization with a risk score. But remember that the tool is likely designed for IT, not OT. The organization needs to understand how the tool calculated the risk score and then factor in OT requirements to gain a true understanding of the vulnerabilities. Now the CI organization can prioritize remediations based on the likelihood of attack, the sensitivity of the data, and the criticality of the infrastructure.

The federal government has mandated a zero trust approach to cybersecurity, and organizations like NIST have issued zero trust frameworks. While zero trust covers multiple pillars of cybersecurity, from identities to data, the basic idea is never trust, always verify.

That means authentication of any user or system that requests access to a resource should be temporary. Every entity should re-authenticate for every resource, every time it wants access. That way, a malicious actor cant break into the network and gain de facto access to everything.

Zero trust replaces perimeter-focused, defense-in-depth security that hardens the edges but leaves the center vulnerable. It shifts the focus of security to users, which typically are the most vulnerable component of the infrastructure.

Zero trust dovetails with risk assessment, because its risk-based. It tailors access control to each entity that wants access. Its ideal for centralized, mission-critical OT systems supported by a growing number of IoT devices at the edge.

The third piece of the OT security puzzle is micro-segmentation. Traditional segmentation involved roadblocks like firewalls and virtual LANs. Micro-segmentation is more sophisticated, enabling organizations to isolate any user, application, or device, no matter where it appears in the infrastructure.

Micro-segmentation is based on identity, with the assumption of least-privilege access. For example, a developer might be granted access to a portion of the system that requires upgrade but be prevented from accessing any other part of the infrastructure.

In the past, segmentation required extensive planning and system upgrades that could take months to realize. In contrast, a micro-segmentation solution based on software-defined networks can be rolled out in one or two weeks. Agencies can deploy either on-prem or in the cloud, with no need to replace hardware.

The OT systems that control critical infrastructure involve unique security and safety requirements. But theyll increasingly intersect with IT systems and face new vulnerabilities in the quantum computing era. By leveraging risk assessment, zero trust, and micro-segmentation, OT operators can adapt to these challenges while maintaining their traditional focus on CI safety and continuity.

Darren Pulsipher is Chief Solutions Architect Public Sector at Intel Corp.

This article is an Op-Ed and the opinions expressed are those of the author. If you would like to respond, or have an editorial of your own you would like to submit, please email C4ISRNET and Federal Times Senior Managing Editor Cary OReilly.

Originally posted here:
How to protect critical infrastructure in the quantum-computing era - Federal Times

There is a revolution coming that is as big as it is tiny. While advances in artificial intelligence have dominated headlines recently, quantum computing could change our lives in ways that are even more far-reaching. VICE News and HSBC have teamed up to explore a series of questions that underpin internationalism as well as the benefits and opportunities from greater global connection and collaboration. In this piece, we explore how tiny subatomic particles are set to unleash a huge number of possibilities. Can we do great things by thinking smaller? Read on to find out.

What Is Quantum Computing?

The device that you are currently reading this on remains remarkably unchanged from the proto-computers developed in the 20th century. While they may have gotten smaller and faster, the way in which information is processed is still fundamentally the same. Quantum computing, however, is set to upend this status quo and could be as transformational to the global economy as the Internet, TV, and telephone.

So what makes it so revolutionary? First, you need to understand how that device you are reading this on works. Your computer uses the binary system, which is a coding language that helps it to understand the world around us. Every single piece of data - such as images or text - that gets entered, or produced by, a computer is translated into unique codes made up of 0s and 1s. For example, the word cat would translate to 01000011 0100001 01010100 in binary.

Quantum computing is tearing up this system and ushering in a new type of processing using ideas from subatomic physics. The units of information inside a quantum computer known as qubits can be a zero or one at the same time. In fact, they can be in every possible state between zero and one too. Imagine a coin being flipped in the air. It will land either up or down, but while it is in the air, it is in every possible position. In quantum computing, this is known as superposition and it opens up vast new processing possibilities and that means these machines can perform computations in an exponentially faster way.

If that seems complicated, bear in mind that American theoretical physicist Richard Feynman said if you think you understand quantum mechanics, then you dont understand quantum mechanics.

What Can Quantum Computers Do?

If this all sounds a bit Sci-Fi, you are not alone. For a long time, experts believed that we would never be able to get the qubits into the perfect state that we needed them to be in to make useful quantum computers. However, recent breakthroughs are finding ways to correct these issues enough to make them more reliable and accurate. Small scale quantum computers are already in operation but even the largest contains just 433 qubits. To put that into perspective, it is believed that a million qubits is required to realize the technologys full potential.

Creating Efficiencies

One of the most exciting uses of quantum computing is the part that they could play optimizing current processes to reduce carbon emissions. A Japanese software company and real estate developer recently used quantum computing to develop sustainable cities via optimized waste management and lower CO2 emissions. The route for collecting waste was reduced from 2,300 km to 1,000 km, and as a result, CO2 emissions were cut by 57%.

As quantum computers become more mainstream, they could also be used to run energy grids more efficiently. Today, research has already begun into designing new catalysts to make industrial processes less energy intensive or removing carbon dioxide from the atmosphere.

Speeding Up Business

It could also have a major impact on businesses, helping them to run in a more efficient manner or streamline processes to improve profitability. It is thought that there is huge potential for the automotive industry, from supply chain management to the development of new high capacity batteries for electric vehicles.

The move to hyper fast computers would likely have a large impact on the financial services sector too. The ability to crunch such large volumes of data would likely mean that banks would be able to create ever more accurate market and scenario calculations. HSBC has already begun investigating the potential of applying quantum technologies to real world problems across the bank. Joining forces with leading technology providers and research labs, HSBC is looking into areas such as pricing and collateral optimization.

In the future, it is hoped that quantum computers could massively speed up the discovery of vital new treatments and vaccines. Quantum computers could also be used to help with early intervention in cancer patients by helping to predict when otherwise healthy cells might develop into malignant tumors.

A New Internet?

As with artificial intelligence, there are growing concerns about the potential downsides to the mind-boggling power set to be unleashed. A big concern is that quantum computers will eventually be able to crack practically all of the current encryption protocols that we have. A full-scale quantum computer could do in a day what would take a conventional computer millions of years to decipher. With cryptographic keys protecting some of our most important online communication services - think group chats, emails, and even your crypto account - it has the power to break the Internet as we know it.

The flip side of this, of course, is that quantum communication could protect data in a far more secure way than we do today, making it near impossible for hackers to operate. Quantum communication could ultimately herald a new era in cybersecurity and transform the financial services industry. This could lead to even greater levels of security for payments and transactions. HSBC is already looking at how quantum physics can be used to create ultra secure encryption systems using technology called Quantum Key Distribution.

What Stage Are We At Now?

While already being put to good use, quantum computing is still in its early days. However, the point in history where quantum computers overtake existing supercomputers is known as quantum supremacy. You might even hear the terms Y2Q or Q-Day thrown around. We are still some way off that today, however, with experts estimating it could be another 20-40 years until we have a machine with the necessary million qubits.

What is not in doubt, however, is the attention and funding the area is already receiving. Quantum computing is seeing big funding pledges by governments and industry alike. Some $24 B has been earmarked by governments around the world and the sector received over $1 B of venture capital investment in 2021.9 It is estimated that, by 2035, the four sectors most likely to be impacted by the development of quantum computing - automotive, chemicals, financial services, and life sciences - could stand to gain $1.3 T in value.

It is unsurprising then that the race is on for quantum supremacy by governments and private industry around the world. Large international projects are helping to bring together research and development to scale up the rate of discovery. The European Next Applications of Quantum Computing (NEASQC), of which HSBC is a key partner, is bringing together twelve European companies and research laboratories who are working on possible use cases in fields ranging from drug discovery and breast cancer detection to carbon capture and energy infrastructure risk assessments.

As the world hurtles toward Y2Q, it is likely we will hear much more about quantum computing in the years to come. Can we do great things by thinking smaller? With quantum computing, there is no question about it. But how, what, and with who? The possibilities are truly infinite!

Link:
Can We Do Great Things By Thinking Smaller? This Is How ... - VICE

Driven by the government, energy, and transportation verticals, the global market for quantum computing hardware, according to a new report by Research and Markets, is projected to exceed $9.1 billionby 2028. These market segments will use quantum computing for various applications, including simulation, optimization, and sampling.

The research firm forecasts that managed Quantum Computing services will reach $328 million by 2028 with a 47.3% CAGR. Deployment, maintenance, and consulting are critical professional services in the quantum computing market.

Likewise, Quantum computing based on superconducting (cooling) loops technology is forecast to reach $4.5 billion during the same period.

Quantum computing uses the unique capabilities of quantum bits (qubits) to perform computational feats that are orders of magnitude greater than conventional methods. Qubits can exist in multiple states simultaneously due to the principles of quantum physics, enabling greater processing power than the binary representation of data used in classical computing.

Quantum computing is expected to support various capabilities, including:

Ultra-Secure Data and Communications: Quantum teleportation allows encrypted data to follow multiple paths, enhancing security.

Super-Dense Data and Communications: Significantly denser encoding will send more information from point A to point B.

The impact of quantum computing on data processing, communications, digital commerce, security, and the internet is anticipated to be transformative for the ICT sector, wrote the research firm. Quantum computing is also expected to disrupt various industries, from government and defense to logistics and manufacturing.

While Quantum computing is showing potential, the near-term challenge is to maintain qubit stability, as they are affected by molecules' constant motion. Some solutions to these issues involve super-cooling methods like cryogenics, but room-temperature quantum computers using photonic qubits are also being explored.

The research firm said, "Once these challenges are overcome, quantum computing will become more mainstream for solving specific problems.

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Global Quantum Computing hardware market to surpass $9.1B by ... - LightWave Online