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U.S. Rep. Ed Case speaks with UH students about cybersecurity internships with the Navy.

U.S. Rep. Ed Case visited with students from Kapiolani Community College and the University of Hawaii at Mnoa on May 3, to hear how cybersecurity internships with the Naval Information Warfare Center (NWIC) are helping to prepare them for jobs in areas of critical need.

U.S. officials said there are more than 30,000 jobs open nationwide in cybersecurity.

The students in the cybersecurity program at the various UH campuses are learning skills and gaining experience in areas that will prepare them for a career in this fast growing field. Some of the students presented their research, which ranged from data security for healthcare to using quantum computing to provide additional layers of protection.

Case said he was impressed by what the students were learning and had access to through such a program.

Im trying to make sure that people come out of my school here with the skills, and to find jobs and can stay home, the congressman said. Im looking at how we can help further these efforts.

Eric Inouye, a division head at NWIC, said that of the 400 employees at the center, about 175 are engineers, and 60 are computer scientists. He said about 75% to 80% have degrees from a UH campus.

One of the students who presented, Jericho Macabante, a junior from UH Mnoa, said the opportunity has provided a lot of experience in issues facing cybersecurity.

Ive had the chance to work on risk assessment, gaining technical knowledge and studying different areas that are part of cybersecurity, Macabante said. He said he looks forward to a career that will involve some aspects of his internship.

David Stevens, a faculty member from Kapiolani CCs Information Technology Program, created the annual NWIC internship in 2020, which has since expanded systemwide. On most UH campuses, the internship counts toward an IT students internship requirements for their degree/certificate.

As teachers, were always looking for ways to help students overcome the barriers they often face when transitioning from academia to professional life.The NIWC Cybersecurity Internship provides the skills and real-world experiences that help students launch a career, Stevens said.

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Shor says that the toy quantum computers we have today are not reliable enough to run his algorithm. It will take several conceptual breakthroughs and a huge engineering effort before we can scale quantum computers to the necessary 1mn qubits.

His best guess as to when this might happen? I would predict between 20 and 40 years, he says. But he does not rule out the possibility that the physics challenges will prove too hard and we will never build workable quantum computers. Shor, who has worked as a maths professor at MIT for 20 years, has also published poetry on quantum computing.quantum computing.Transcript

The best quantum computers today, produced in countries like China and at Google, can do on the order of 100 operations before failure, explains Steve Brierley, founder and chief executive of Riverlane a company building operating systems for quantum computers. To implement Shors algorithm you need something like a trillion quantum operations before failure.

But researchers are employing all kinds of ingenious techniques to overcome these challenges. Scientific breakthroughs dont always come on a predictable time. But were looking at years and not decades for this level of innovation, says Julie Love, product leader for quantum computing at Microsoft.

For several years, the US government has been planning for a quantum world and has been running competitions to find the most secure communication protocols of the future that would forestall the threat of Q-day. The US National Institute of Standards and Technology is in the process of approving new cryptography systems based on problems other than factorisation that are secure against both quantum and classical computers. Its really a race between quantum computers and the fix which is to stop using RSA, says Brierley.

But whatever new security protocols are finally approved, it will take years for governments, banks and internet companies to implement them. That is why many security experts argue every company with sensitive data should be preparing for Q-day today.

However, the obstacles to developing 1mn-qubit quantum computers remain daunting, with some private sector investors predicting a quantum winter as they lose faith in how quickly a quantum advantage can be achieved.

Even if private sector investment slows, the escalating geopolitical rivalry between the US and China will provide added impetus to develop the worlds first robust quantum computer. Neither Washington nor Beijing wants to come second in that particular race.

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Quantum computing could break the internet. This is how - Financial Times

Rarely are things just as they seem, and the world of quantum computing lends itself better than most to that description. Described as a fundamental shift in our processing capabilities, quantum computing's development has accelerated incredibly in the past few years. Yet according to a research paper published in the journal of the Association for Computing Machinery, relevant quantum computing (the one that's usually referred to as running circles around even the most powerful classical computers) still requires groundbreaking discoveries in a number of areas before it can dethrone a mere graphics card.

The most surprising element in the paper is the conclusion that a number of applications will remain better suited for classical computing (rather than quantum computing) for longer than previously thought. The researchers say this is true even for quantum systems running across more than a million physical qubits, whose performance the team simulated as part of their research.

Considering how today's top system, IBM's Osprey, still "only" packs in 433 qubits (with an IBM-promised 4,158-qubit system launch for 2025), the timescale towards a million qubits extends further ahead than expected.

The problem, say the researchers, is not with the applications or workloads themselves drug discovery, materials sciences, scheduling, and optimization problems in general are still very much in quantum computing's crosshairs. The issue is with the quantum computing systems themselves their architectures, and their current and future inability to intake the egregious amounts of data some of these applications require before a solution is even found. It's a simple I/O problem, not unlike the one we all knew from before NVMe SSDs became the norm, when HDDs bottlenecked CPUs and GPUs left and right: data can only be fed so quickly.

Yet how much data is sent, how fast it reaches its destination, and how long it takes to process are all elements of the same equation. In this case, the equation is for quantum advantage the moment where quantum computers offer performance that's beyond anything possible for classical systems. And it seems that in workloads that require the processing of large datasets, quantum computers will have to watch as GPUs such as Nvidia's A100 run by likely for a long, long while.

Quantum computing might have to make do with solving big compute problems on small data, while classical will have the unenviable task of processing the "big data" problems a hybrid approach to quantum computing that's been gaining ground for the last few years.

According to a blog post (opens in new tab) by Microsoft's Matthias Troyer, one of the researchers involved in the study, this means that workloads such as drug design and protein folding, as well as weather and climate prediction would be better suited for classical systems after all, while chemistry and material science perfectly fit the bill for the "big compute, small data" philosophy.

While this may feel like an ice bucket challenge flop for the hopes of quantum computing, Troyer was quick to emphasize that that isn't the case: "If quantum computers only benefited chemistry and material science, that would be enough. Many problems facing the world today boil down to chemistry and material science problems," he said. "Better and more efficient electric vehicles rely on finding better battery chemistries. More effective and targeted cancer drugs rely on computational biochemistry."

But there's another element to the researchers thesis, one that's harder to ignore: it seems that current quantum computing algorithms would be insufficient, by themselves, to guarantee the desired "quantum advantage" result. Rather than the systems engineering complexity of a quantum computer, here it's a simple performance problem: quantum algorithms in general just don't provide enough of an acceleration. Grover's algorithm, for instance, offers a quadratic speedup over classical algorithms; but according to the researchers, that's not nearly enough.

"These considerations help with separating hype from practicality in the search for quantum applications and can guide algorithmic developments," the paper reads. "Our analysis shows it is necessary for the community to focus on super-quadratic speeds, ideally exponential speedups, and one needs to carefully consider I/O bottlenecks."

So, yes, it's still a long road toward quantum computing. Yet the IBMs and Microsofts of the world will steadily carry on their research to enable it. Many of the issues facing quantum computing today are the same we faced in developing classical hardware the CPUs, GPUs, and architectures of today just had a much earlier and more impactful start. But they still had to undergo the same design and performance iterations as quantum computing eventually will, within its own brave new timeframe. The fact that the paper was penned by scientists with Microsoft, Amazon Web Services (AWS) and the Scalable Parallel Computing Laboratory in Zurich all parties with vested interests into the development and success of quantum computing just makes that goal all the more likely.

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Single-GPU Systems Will Beat Quantum Computers for a While: Research - Tom's Hardware

How does quantum computing help the finance industry?

QCs are only in the developmental stage; experiments are already showing their great potential in the finance industry.

Based on the World Economic Forums estimate from 2022, national governments have invested more than $25 billion in quantum computing research, and over $1 billion in venture capital deals were closed in the previous year. Quantum computers (QCs) are in the early stages of development, and there are many technical challenges that need to be overcome before they can become practical tools for everyday use.

Nevertheless, they have already demonstrated great potential for applications in a wide range of fields. QCs have the ability to solve complex mathematical problems exponentially faster than classical computers, making them ideal for several complex tasks. The finance industry is one of the first runners in testing the technology. However, from the military to pharmaceuticals, logistics and manufacturing companies, several industries are experimenting with QC.

The mentioned features of QCs can have an enormous impact on the future of financial services. There are several tasks where financial forecasting and financial modeling can be supported by QCs for faster and more accurate processes. Notably, portfolio optimization, risk management and asset pricing are some of the most mentioned examples. However, their potential advantages and threats to cryptography make it important for financial service providers to monitor the technology.

Collaboration is crucial in the area of QCs due to the fact that technology and software development enable the revolution. Accelerating programs are initiated by the largest tech companies for experimentation with their hardware, software or cloud solutions, such as IBM, Microsoft, Google or Amazon.

Goldman Sachs has partnered with Microsoft Azure Quantum to explore the use of QCs for pricing. JPMorgan is experimenting with quantum solutions for optimization and risk management. HSBC announced its collaboration with IBM in 2022 to explore the use of QCs for pricing, portfolio optimization and risk mitigation.

QCs are new machines that can perform calculations much faster than classical computers, based on the principles of quantum mechanics.

The expression of QCs refers to a new type of machine based on the principles of quantum mechanics. Quantum mechanics is a division of physics that deals with the behavior of matter and light on the atomic and subatomic scales. The most valued property of QCs is that they perform certain types of calculations much faster than classical computers.

Classical computers store and process information in the unit of bits while QCs use quantum bits (or qubits). Bits represent information in a binary format and can have only two possible values: zero or one. Every piece of information going through a classical computer is essentially a long string of zeros and ones.

Qubits can exist in multiple states at once, a property known as superposition. This means that a single qubit can represent numerous possible combinations of zeros and ones; therefore, it can process a much larger amount of information than a classical bit.

Another exciting feature of qubits is the potential of entanglement, where qubit pairs are created. Modifying the state of one in the pair will change the state of the other qubit in a predictable way. This property gives extra power to QCs. Increasing the number of bits in a classical computer has a linear effect on the processing power, while adding an extra qubit to a quantum machine causes an exponential increase in the processing power.

Despite the great potential of QC, the technology and its applications need to overcome several challenging barriers.

Working with qubits is an enormously challenging scientific task because they need to be isolated in a controlled quantum state, which is extremely fragile. The smallest change in the physical environment (vibration or temperature) can cause an imbalance, which is the collapse of the superposition. Complex preventive actions are required, such as supercooled refrigerators, insulation or vacuum chambers to protect the system from losing its equilibrium.

Another aspect of the challenge is that as a different paradigm, QCs require not only completely new hardware and software but also algorithmic solutions. Numerous articles discuss the potential of QCs in machine learning, artificial intelligence or cryptography. Less often emphasized that it does not only mean using QCs to run algorithms designed for classical computers (quantum-enhanced) but building completely new algorithms, which are leveraging the features of QCs.

QCs in banking can be a game changer due to the potential of multiplying the speed and volume of calculations and transactions. However, different financial institutions only started to experiment with their own quantum algorithms and the limits of those potentials are not clear yet. Quantum algorithms are algorithms that take advantage of the unique properties of quantum systems, such as superposition and entanglement.

One example of quantum algorithms is Grovers algorithm, which can be used to search large, unstructured databases of financial data more quickly than classical algorithms. For example, it could be used to search for specific financial transactions or to identify patterns in financial data. Another example is Shors algorithm, which enables one to factor in large numbers more quickly than classical algorithms.

The finance industry is optimistic about quantum computing. Tasks such as portfolio optimization, risk management and asset pricing have a great chance to be beneficiaries.

Grovers and Shors algorithms can be applied to portfolio optimization. Portfolio optimization involves finding the optimal combination of investments to maximize returns while minimizing risk. Besides providing faster and more accurate calculations the technology can enable more flexible optimization strategies that take into account a wider range of factors, such as environmental, social and governance factors.

Another example could be asset pricing. Asset pricing is the process of estimating the value of financial assets such as stocks, bonds and derivatives. Traditional methods for pricing financial assets rely on complex mathematical models, such as Monte Carlo simulations, which involve simulating a large number of possible outcomes for a given financial asset and then using these simulations to estimate its value. Quantum Monte Carlo (QMC) can handle, for example, complex financial instruments, such as options, that have non-linear payoffs.

Heres the billion-dollar question: Can quantum computers predict the stock market? While QCs may have some advantages over classical computers in certain financial modeling tasks, it is unlikely that they will be able to predict the stock market with complete accuracy. Additionally, as with any new technology, quantum computing also poses its own unique challenges and limitations that need to be addressed before its full potential in financial applications can be realized.

Many financial services companies have high expectations of QCs effect on risk management. It involves identifying, assessing, prioritizing risks and taking actions to mitigate or manage those risks. Every step involves mathematical modeling and simulations for predicting risk outcomes, and time and accuracy play a crucial role in the process. Cybersecurity is an important part of risk management that can be enhanced by enabling more advanced encryption methods.

Encryption became a crucial measure in the banking industry that protects sensitive information from unauthorized access. It is used to secure communication channels between banking systems, websites and mobile apps and protect data on servers, databases and backups. Additionally, encryption is used to generate digital signatures that help ensure the authenticity of documents and prevent unauthorized modification or tampering of sensitive documents.

Cryptography and blockchain technology will surely not stay untouched by quantum computing; however, the direction remains a question.

Quantum computing presents both a threat and an opportunity for cryptography. While it has the potential to break many of the current encryption methods, it also has the potential to create new and more secure methods that are immune to attacks by classical computers.

QCs are exponentially faster than classical computers, which means they can quickly solve mathematical problems that classical computers would take years, decades or even centuries to solve. This includes the mathematical problems that underlie many of the encryption schemes used to secure digital communication and transactions.

For example, Shors algorithm can be used to efficiently factor large numbers, which is the basis for many public-key encryption algorithms such as RSA (the abbreviation refers to the name of the creators, RivestShamirAdleman).

However, quantum cryptography can also be used to create new cryptographic methods that are securer than classical methods. For example, quantum key distribution is a method to generate and distribute a secret key between two parties, the confidentiality and integrity of the information being exchanged can be ensured, even if a malicious entity intercepts the communication.

The mentioned features create some uncertainty in the future of QCs in blockchain technologies. It has the potential to break current encryption methods used in blockchain, which could compromise the security of digital assets and transactions. At the same time, researchers are working on developing quantum-resistant encryption methods for blockchains to counter this threat, such as CRYSTALS-Kyber public-key encryption by IBM. Additionally, QCs can enhance blockchains by increasing their processing speed and scalability, which can lead to more efficient and secure transactions.

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How does quantum computing impact the finance industry? - Cointelegraph

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Large numbers can only be factorized with a great deal of computational effort. Physicists at the University of Innsbruck, Austria, led by Wolfgang Lechner are now providing a blueprint for a new type of quantum computer to solve the factorization problem, which is a cornerstone of modern cryptography. The research was recently published in Communications Physics.

Today's computers are based on microprocessors that execute so-called gates. A gate can, for example, be an AND operation, i.e., an operation that adds two bits. These gates, and thus computers, are irreversible. That is, algorithms cannot simply run backwards. "If you take the multiplication 2x2=4, you cannot simply run this operation in reverse, because 4 could be 2x2, but likewise 1x4 or 4x1," explains Wolfgang Lechner, professor of theoretical physics at the University of Innsbruck. If this were possible, however, it would be feasible to factorize large numbers, i.e., divide them into their factors.

Martin Lanthaler, Ben Niehoff and Wolfgang Lechner from the Institut fr Theoretische Physik at the University of Innsbruck and the quantum spin-off ParityQC have now developed exactly this inversion of algorithms with the help of quantum computers. The starting point is a classical logic circuit, which multiplies two numbers. If two integers are entered as the input value, the circuit returns their product. Such a circuit is built from irreversible operations. "However, the logic of the circuit can be encoded within ground states of a quantum system," explains Martin Lanthaler from Wolfgang Lechner's team. "Thus, both multiplication and factorization can be understood as ground-state problems and solved using quantum optimization methods."

"The core of our work is the encoding of the basic building blocks of the multiplier circuit, specifically AND gates, half and full adders with the parity architecture as the ground state problem on an ensemble of interacting spins," says Martin Lanthaler.

The coding allows the entire circuit to be built from repeating subsystems that can be arranged on a two-dimensional grid. By stringing several of these subsystems together, larger problem instances can be realized. Instead of the classical brute force method, where all possible factors are tested, quantum methods can speed up the search process: To find the ground state, and thus solve an optimization problem, it is not necessary to search the whole energy landscape, but deeper valleys can be reached by "tunneling."

The current research work provides a blueprint for a new type of quantum computer to solve the factorization problem, a cornerstone of modern cryptography. This blueprint is based on the parity architecture developed at the University of Innsbruck and can be implemented on all current quantum computing platforms.

More information: Martin Lanthaler et al, Scalable set of reversible parity gates for integer factorization, Communications Physics (2023). DOI: 10.1038/s42005-023-01191-3

Journal information: Communications Physics

Read more from the original source:
A blueprint for a quantum computer in reverse gear - Phys.org