Nataraj Nagaratnam, IBM fellow and cloud security CTO, has been with the supplier for nearly 25 years. Security has been his forte throughout this time, whether it be cloud security, hybrid cloud security or technology strategy.

Natarajs interest in security started when he was studying for his masters and PhD. One good, fine day, my professor walks in and says there will be this new thing, called Java, he recalls. He was already working with the core Java engineering team, which created Java at the time. Intrigued, I started to work on the security aspects of Java, and then my PhD was in security in distributed systems.

Following his studies, when Nataraj was looking for fresh challenges, IBM approached him with an opportunity to help shape the future of security. Just as the internet was going to change the world and how business was conducted, IBM offered him the chance to develop systems for how businesses could securely operate over the internet.

IBMs offer to lead enterprise web security for IBM products appealed to the young Nataraj, as the new technologies promised to be both disruptive to markets and enabling to the world. I jumped right onto the opportunity. And, as they say, the rest is history, he says. I was fortunate enough to be part of the way, with WebSphere shaping the industry, and working with industry on standard security specifications, such as web services security.

Technology, especially enterprise IT, has expanded massively throughout Natarajs career. While this has created opportunities for enterprise solutions, it also carries certain risks. In the history of computing, there are three major chapters mainframes, then web, and now there is cloud, says Nataraj. This is a defining moment in the entire IT space, and I am fortunate enough to define and lead the work on security from web to cloud.

Relying on data and services in the cloud can be challenging, as organisations need to ensure that data remains sharable across networks, while having sufficient protections in place to ensure data is confidential and protected. This is especially the case for heavily regulated industries, such as the defence, healthcare and financial sectors. This has become a defining moment for such industries, which are concerned about risk, security and compliance.

Rather than relying on the subjective term of trust, which implies that one can have faith in or rely on someone or something, Nataraj prefers to use technical assurance. Technical assurance demonstrates that technological and human processes have been put in place to ensure data is being protected.

Part of this is ensuring that identity and access management (IAM) is uniformly addressed across all of the organisations cloud platforms, from their cloud storage capabilities to their on-premise services. Given that no two cloud platforms are ever the same, this can complicate matters, as more than one platform is typically used.

The rapid expansion of the tech sector means there is a growing security skills gap, which needs to be addressed. This has left organisations struggling to fill vitally important roles and relying on external contractors instead. This adds further cost, especially if a significant amount of work is required, as contractors are expensive for long-term projects.

To address such concerns, organisations are turning to IAM tools to act as an overlay across their existing cloud infrastructure. If we standardise the access management and security overlay, and enable them with automation and continuous monitoring, we can solve complex problems, says Nataraj. Taking a hybrid multicloud approach with security and compliance automation addresses this with consistency and continuous monitoring.

Government policy is also evolving, as regulators become ever more technologically aware, with additional demands on data protection when sharing data between regions. There has, however, been greater collaboration between countries in this regard. For example, the European Unions (EUs) General Data Protection Regulation (GDPR) has effectively become a de facto global standard for data protection, as countries realise that trade is reliant on an unimpeded flow of data.

Lawmakers and regulators are starting to understand the impact of technology, and that policies and standards need to evolve in a way that accommodates those technologies, while also providing a level of risk and regulatory compliance. Standardisation needs to happen

Nataraj Nagaratnam, IBM

Laws, regulations and policies are becoming much more technology aware, says Nataraj. Lawmakers and regulators are starting to understand the impact of technology, and that policies and standards need to evolve in a way that accommodates those technologies, while also providing a level of risk and regulatory compliance. Standardisation needs to happen, as opposed to every country having its own regulatory requirements, because that will have its own complexity.

With information interchange between different countries being dependent on data sharing agreements, organisations are looking at approaches that allow them to meet the regulatory and technical requirements.

A few weeks back, when I was in India, we talked about this notion of data embassies the fundamental concept is if you run services within these datacentres and service providers, you get immunity from certain laws, says Nataraj. A country can have a data embassy in one country, and in reciprocity, they can have a data embassy in their country. There are innovative and creative ideas coming up in different parts of the world. Thats a reflection of a policy and a practical approach to solve this data sharing problem, and that is going to evolve.

These data embassies are similar to TikToks proposed Project Texas, which would see the social media platform storing all data in the US under the watch of American firm Oracle. These data embassies could evolve into independent third-party organisations.

One of the most significant future concerns facing organisations relying on cloud services will be the risk posed by quantum computing, which could disrupt encryption security. Reliance on existing encryption technologies is not an option, as the processing speeds offered by quantum computers would enable them to swiftly break encryption, especially as certain public key algorithms have proven to be susceptible to quantum computer attacks.

The most common public key infrastructure (PKI) technology used across the world is transport layer security (TLS), which secures the data in transit. As such, that should be considered the greatest risk, because if data is captured in transit today, the encryption could be broken in five years time, if quantum computing becomes commercially available. As such, we need to rethink the way we approach hybrid cloud, secure connectivity and TLS.

When it comes to quantum safe, I believe the first thing to fix is connectivity. Two years ago, we introduced support for quantum safe algorithms in IBM cloud, says Nataraj. When you do application transactions over the wire, that link can be quantum safe. You prepare for the threat. That has to be one of the first things, when it comes to cloud security, that one needs to work through.

With the increasing levels of functionality offered by artificial intelligence (AI) and machine learning (ML), automation will become a growing part of an organisations security posture. Automated monitoring of security and compliance posture allows for continuous security.

Furthermore, security deployment will become automated, thereby bridging the gap between the CISOs and CIOs and IT teams. This will ensure they are all consistent with each other and aligned with the organisations global security and compliance requirements.

There is more to be done in continuous security and compliance infused with automation, and how we change from a reference architecture that may be in a Visio diagram to something prescriptive, deployable and automated, says Nataraj.

Concerns surrounding data sovereignty and data privacy residency are likely to increase, given the regulatory compliance and geopolitical aspects of dealing with data. As such, there will be a need for more demonstrable controls and technologies that can help in protecting data and privacy, which will become infused with confidential computing.

Applications of confidential computing are still in their infancy and there is more to be done, because its not just a technology, but its use cases in confidential AI, says Nataraj. IBM has leveraged confidential computing technology to enable unique approach use cases around encryption key management called Keep Your Own Key, where a customer has technical assurance that only they have access to the keys, where keys are protected within hardware as well as within secure enclaves. This is now extended to hybrid multicloud key management through Unified Key.

The IT sector is undergoing a fundamental shift, as it transforms from a web-based model to one reliant on cloud services. This is being compounded by technological and regulatory issues coming to the fore. A multicloud system can enhance adaptability to shifting market trends, but this brings certain challenges. Automating network management policies enables swift and effective sharing of information within networks, regardless of location, while ensuring that compliance with shifting regulatory compliance is maintained.

We can help industry, governments and others move forward, concludes Nataraj. We will collaborate with governments and their policies to make that happen.

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IBMs Nataraj Nagaratnam on the cyber challenges facing cloud ... - ComputerWeekly.com

Quantum computing uses quantum mechanics to perform operations. Quantum mechanics is a physics theory that describes the physical environment at an atomic and subatomic scale, compared to traditional physics, which looks at the macroscopic scale.

Bits denote data in classical computing. These bits are two-state, the familiar 1 or 0. With quantum computing, quantum bits qubits measure computing power. These exist in multiple states at the same time, which can include combining 0 and 1 simultaneously.

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The benefits of this new computing technology include storing massive amounts of information in fewer computers while using less energy. And, by operating outside the traditional laws of physics, quantum computers can offer processing speeds millions of times faster than traditional computers.

In 2019, for example, Googles latest quantum computer performed a calculation in four minutes. The worlds most powerful supercomputer at the time would have needed 10,000 years to finish that same calculation. With 300 qubits, a quantum computers calculations at a given time are greater than the atoms in the universe.

The speed of quantum computers brings many use cases, including faster and smarter artificial intelligence (AI) platforms, advanced pharmaceutical modeling, more accurate weather predictions and the creation of new materials.

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Research firms like Contrive Datum Insights see massive quantum computing market growth. The company projects a compound annual growth rate of 36.89% from 2023 to 2030, with the market reaching $125 billion annually. Where there is that kind of growth and money involved, startups are sure to follow. With quantum computing still in the early stages, startups are tackling multiple fronts, including different computer production methods, advanced quantum algorithms and other innovations.

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Here are some of the quantum computing startups making noise in the space:

Maryland-based quantum computing hardware and software firm IonQ Inc. (NYSE: IONQ). The company partners with various firms like Hyundai Motor Co. to create better machine learning algorithms to improve safety and bring about self-driving automobiles. Hyundai is also leveraging IonQ to study lithium chemistry and find new reactive solutions for future electric vehicles (EVs).

PSIQuantum is a company developing a method of quantum computing that uses photos that represent qubits. The startup is on the CB Insights list of unicorn companies with a current valuation of $3.15 billion as of March 10. The firm completed a $450 million investment round in the summer of 2021 and continues toward its stated goal of developing a 1 million qubit computer.

French startup PASQAL offers quantum computers built with 2D and 3D arrays of ordered neutral atoms, enabling its clients to solve challenging problems. These include improving weather forecasting, boosting auto aerodynamics for greater efficiency and finding relationships between chemical compounds and biological activity for the healthcare industry.

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Established technology giants are also pushing forward quantum computing. IBM remains at the forefront. In November 2022, the company announced the creation of a 430-qubit machine named Osprey, which has the largest qubit count of any processor. IBMs breakthroughs in quantum computing mirror the trajectory of innovation for traditional computers as processing speed increased year over year.

Amazon Inc. Braket is the companys managed quantum computing service and part of its overall growth strategy with Amazon Web Services (AWS). Bracket offers users a place to build, test and run quantum algorithms. It provides them with access to different types of quantum hardware, encourages software development through the Braket SDK and to create open-source software.

Microsoft Corp., Alphabet Inc.s Google, Intel Corp. and Nvidia Corp. also offer quantum computing solutions and investment. As the biggest tech firms increase participation in quantum computing, more startups should become acquisition and merger targets as the market moves toward consolidation.

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This article Is 2023 The Year Of Quantum Computing Startups And A 1 Million Qubit Machine? originally appeared on Benzinga.com

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Is 2023 The Year Of Quantum Computing Startups And A 1 Million Qubit Machine? - Yahoo Finance

Quantum Computing Inc. (QCI) reported 2022 total revenue at $135,648 versus no revenue in 2021. Operating expenses were $36.5 million versus $17.1 million in the prior year due to impact of its merger with QPhoton, increase in engineering personnel, non-stock based compensation, and other factors. The net loss was $38.5 million versus $10.7 million in the prior year. The company ended the year with Cash and Cash Equivalents of $5.3 million versus $16.7 million at the end of 2021. After the end of the year, the company has received $6.4 million from sales of $3 million of their shares via an at-the-market facility managed by Ascendiant Capital.

2022 was a pivotal year for the company due to their acquisition of QPhoton which allowed them to offer Quantum Computing as a Service (QCaaS) with a full-stack quantum computing capability. The company has been working on several proof-of-concept projects including projects to optimize sensor placement on a BMW automobile, optimize flight trajectories with VIPC, detect fraudulent banking transactions with Rabobank, optimize windmill placement, optimize nuclear fuel rod replacements, and predict stock performance. They also created a new subsidiary QI Solutions, Inc. to pursue government business.

The company also indicated their roadmap for product development including a Dirac-2 follow-on to the existing Dirac-1 that supports calculations based upon Qudits (0-53 variables) instead of Qubits, a Reservoir Quantum Computer, a Quantum Random Number generator, and other products based upon quantum photonics. The companys goal is to hit EBITDA and cashflow breakeven within 2 years at a revenue level of about $30 million.

For more information about QCIs financial report, you can view their press release posted on their website here.

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Quantum Computing Inc. Announces 2022 Financial Results and Starts Transition to Commercialization - Quantum Computing Report

Quantum Computing Concept Image.

Quantum machine learning models can achieve quantum advantage by solving a complex class of mathematical problems impossible to crack with a classical computer, according to new research by UBC material scientists.

UBC Blusson Quantum Mater Institute (Blusson QMI) investigator Professor Roman Krems said the results rigorously prove that quantum machine learning does indeed offer the quantum advantage.

The key goal now is to find a real-world machine learning application thatwould benefit from this quantum advantage in practice, said Professor Krems, senior author on the Nature Communications study.

Quantum advantage refers to the instances where quantum computers outperform their classical counterparts when scaling to enormous datasets containing countless variables.

Blusson QMI PhD student and first author of the paper Jonas Jger said the models have universal expressiveness in that they solve not just one problem, but capture the complexity of an entire class of problems that are too complicated to solve with classical machine learning.

While quantum machine learning is often considered to be one of the most promising use cases of quantum computing, there are only a few rigorous results about its real computational advantages, Jger said. Our results offer theoretical guarantees that such advantages indeed exist.

The study proves a quantum advantage exists for two of the most popular quantum machine learning classification models: Variational Quantum Classifiers (also known as quantum neural networks) and Quantum Kernel Support Vector Machines.

We can now confidently explore important real-world applications and develop effective approaches for building informative data encoding quantum circuits that could unlock the full potential of quantum machine learning, said Jger.

The advantages reported in the study are somewhat subject to the quality of the datasets presented to the system. As quantum computing is still in the experimental stage, a challenge faced by researchers is encoding the classical data for processing by a quantum device.

The mathematical problem that weve solved using these models is quite abstract and doesnt have many practical applications. But, because it presents such special properties under the complexity theory, it can be used by others as a benchmark to test how different quantum machine learning models perform, Jger said.

Jger joined UBC in Sept 2022 to commence his PhD studies under the supervision of Professor Roman Krems from UBCs Department of Chemistry and Professor Michael Friedlander from UBCs Computer Science Department.

Professor Krems and his team work at the intersection of quantum physics, machine learning and chemistry on problems of relevance to quantum materials and quantum technologies, including quantum computing, quantum sensing and quantum algorithms.Meanwhile, Professor Friedlander and his research group develop theories and algorithms for mathematical optimization and its applications in machine learning, signal processing and operations research.

Jger hopes to take advantage of their combined expertise to push the limits of quantum computing and develop algorithms that can harness its power for practical applications.

We can now confidently explore important real-world applications and develop effective approaches for building informative data encoding quantum circuits that could unlock the full potential of quantum machine learning.

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New evidence that quantum machine learning outperforms classical ... - UBC Faculty of Science