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A version of this story appeared in the daily Threat Status newsletter from The Washington Times. Click here to receive Threat Status delivered directly to your inbox each weekday.

The National Security Agency fears a quantum computing breakthrough by Americas adversaries would jeopardize the security of the global economy and allow foes to peer inside top-secret communications systems.

The agencys concern is that an unforeseen advance in quantum technology would crack encryption systems used to protect everything from financial transactions to sensitive communications involving nuclear weapons, according to NSA Director of Research Gil Herrera.

Speaking at an Intelligence and National Security Alliance event last week, Mr. Herrera said no country has a quantum computer that he would consider useful yet.

He said there are a lot of teams around the world building with different technologies and someone could achieve a development representing a black swan event, an extremely unexpected occurrence with profound and dangerous consequences for U.S. national security.

If this black swan event happens, then were really screwed, Mr. Herrera said.

Americans could suffer consequences from such a quantum leap in several ways. Mr. Herrera said the world economy, and the U.S. market in particular, are vulnerable because most financial transactions are secured by encryption systems that cant be cracked by non-quantum means.

If quantum tech weakens or eliminates such encryption walls, then financial institutions may have to resort to older transaction methods and banks would look for other means to protect their dealings with other banks, according to Mr. Herrera.

And, he warned, other industries may be even less resilient in the face of the threat. Mr. Herrera said the threat of a quantum computer is not limited to its immediate potential damage, but to the fallout from obtaining encrypted information that was previously recorded.

Drawing on his decades of experience at Sandia National Laboratories, Mr. Herrera said a quantum advance may be able to help people find information on weapons systems that have been in the U.S. arsenal for a significant period of time.

There are ways that we can communicate with our various partners in nuclear weapon production where public key encryption is utilized to share keys, Mr. Herrera said. And now, what if somebodys recorded that information and they crack it?

Details on foreign adversaries advanced computing capabilities are closely guarded, Federal policymakers are worried in particular about Chinas efforts to achieve computing breakthroughs.

Reflecting on supercomputers at a House Armed Services Committee hearing last year, Rep. Morgan Luttrell said he worried Beijing may have already surpassed the U.S. in its supercomputing prowess.

China should have on board or online another computer that would have trumped us and pushed us back some, the Texas Republican said at the March 2023 hearing. So the amount of money theyre spending in that space as compared to us would make me think that theyre ahead of us.

Retired Gen. Paul Nakasone, then in charge of U.S. Cyber Command, cautioned Mr. Luttrell against assuming that outspending America would guarantee an adversarys technological success.

Spending money doesnt necessarily mean that youre the best in what you do and being able to integrate that kind of capability is what really matters, Gen. Nakasone said at the hearing. So being able to take the intelligence, integrate it within maneuver force to have an outcome is where I clearly see United States has the lead.

But experts agree that quantum computing breakthroughs would dramatically outdo existing supercomputers. The NSA is not waiting to find out.

Mr. Herrera said the NSA believes the algorithms it is deploying will withstand a quantum attack.

One thing NSA has done about it is we actually started research in quantum-resistant algorithms not too long after we started funding academic programs to come up with what a quantum computer would look like, Mr. Herrera said. So we have a lot of maturity within the NSA, we have been deploying quantum-resistant encryption in certain key national security applications for a while now.

Efforts to better understand the quantum capabilities of Americas adversaries are underway as well. The congressionally chartered U.S.-China Economic and Security Review Commission is scrutinizing the communist countrys push to transform its military through the application of quantum and emerging technologies to its weapons systems and logistics.

Last month, the commission conducted a hearing that included an examination of Chinas quest for teleportation technology.

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NSA fears quantum computing surprise: 'If this black swan event happens, then we're really screwed' - Washington Times

Quantum computing has gained significant attention for its potential to revolutionize computational processes across various industries. In the CAE field, quantum speed-up using quantum computers holds promise as a crucial application area. However, the practical impact and challenges of implementing quantum computing in the industry are still under research.

This event will feature discussions on computational challenges in the CAE field and explore potential applications and impact of quantum computing and comparison to classical computing under two paradigms: the NISQ (Noisy Intermediate-Scale Quantum) era and the FTQC (Fault-Tolerant Quantum Computing) era.

Our intention for this workshop is to foster vibrant discussions by inviting industry experts from global leading companies such as JX Nippon Oil & Gas Exploration, Murata Manufacturing Co., Bridgestone Corporation and others.

"We are excited about the potential impact that quantum computing can bring to augment our efforts in oil and natural gas development, as well as our CCS/CCUS projects. Specifically, we have high expectations for the benefits derived from applying quantum computing to calculations related to fields such as fluid dynamics and electromagnetics," said a participant from JX Nippon Oil & Gas Exploration.

"There is a series of processes related to the manufacture of electronic components, such as material development, product design, and solution engineering. We extensively utilize computational science and technology including simulation of these processes. We would like to explore together how we can incorporate quantum computing in our business and leverage its potential," said a participant from Murata Manufacturing Co., Ltd.

"In the field of CAE, including fluid simulation, structural analysis, and similar processes, the use of quantum computing is anticipated to enable the solution of exponentially complex problems within realistic timeframes. However, despite these expectations in theory, numerous challenges must still be addressed to realize industrially valuable applications. In this workshop, we aim for participants to gain a thorough understanding of the challenges associated with applying quantum computing to CAE. Additionally, we seek insights from participants regarding the potential utilization of quantum computing based on its characteristics in their respective businesses. By doing so, we hope to accelerate research and development geared towards industrial applications. We are excited about the prospect of advancing this meaningful initiative, forging collaborations with experts and stakeholders. If you have an interest in applying quantum computing to the field of CAE, please feel free to reach out to us," said Keita Kanno, CTO of QunaSys.

The workshop is open to professionals, researchers, and executives in the CAE field interested in harnessing the potential of quantum computing.

SOURCE QunaSys Inc.

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QunaSys to Host Workshop on Exploring Quantum Computing Applications in the CAE Field - PR Newswire

Quantum computing, and how its revolutionizing our world, is the focus of this years Begeman Lecture in Physics at the University of Northern Iowa.

The lecture, titled Building a Quantum Computer, One Atom at a Time, will be presented by UNI Department of Physics alum Justin Bohnet on Wednesday, April 3 at 7 p.m. in the Lang Hall Auditorium. The event is free and open to the public.

Justin is in the vanguard of efforts to develop quantum computers for widespread use, said Paul Shand, head of the UNI Department of Physics. Were excited for him to share more about quantum computers and how they will turbocharge computing in the future.

Bohnet is the research & development manager at Quantinuum a quantum computing company whose mission is to accelerate quantum computing and use its power to achieve unprecedented breakthroughs in drug discovery, health care, materials science, cybersecurity, energy transformation and climate change.

In this lecture, Bohnet will share his personal journey from a student at UNI to building the worlds most powerful quantum computers, powered by control over single atoms. Along the way, youll get a crash course on quantum computers what they are, how they work and why were standing on the brink of a technological revolution that will let us explore uncharted territories of science and technology.

If you need a reasonable accommodation in order to participate in this event, please contact the UNI Department of Physics by calling 319-273-2420 or by emailing physics@uni.edu prior to the event.

See more here:
UNI's Begeman Lecture to explore how quantum computing is revolutionizing our world - Cedar Valley Daily Times

Optimization problems cover a wide range of applications and are often cited as good candidates for quantum computing. However, the execution time for constrained combinatorial optimization applications on quantum devices can be problematic. Researchers from Waseda University report developing a new algorithm post-processing variationally scheduled quantum algorithm (pVSQA) that speeds performance.

Therea brief account of the work posted today on the Waseda University website. Constrained combinatorial problems (COP) are common in logistics, supply chain management, machine learning, material design, and drug discovery. The researchers report the novelty of their algorithm is its use of a post-processing technique combined with variational scheduling to achieve high-quality solutions to COPs in a short time.

The two main methods for solving COPs with quantum devices are variational scheduling and post-processing. Our algorithm combines variational scheduling with a post-processing method that transforms infeasible solutions into feasible ones, allowing us to achieve near-optimal solutions for constrained COPs on both quantum annealers and gate-based quantum computers, said Tatsuhiko Shirai, a leader in the work, which was published in EEE Transactions on Quantum Engineering this month.

Heres a brief excerpt from the article:

The innovative pVSQA algorithm uses a quantum device to first generate a variational quantum state via quantum computation. This is then used to generate a probability distribution function which consists of all the feasible and infeasible solutions that are within the constraints of the COP. Next, the post-processing method transforms the infeasible solutions into feasible ones, leaving the probability distribution with only feasible solutions. A classical computer is then used to calculate an energy expectation value of the cost function using this new probability distribution. Repeating this calculation results in a near-optimal solution.

The researchers analyzed the performance of this algorithm using both a simulator and real quantum devices such as a quantum annealer and a gate-type quantum device. The experiments revealed that pVSQA achieves a near-optimal performance within a predetermined time on the simulator and outperforms conventional quantum algorithms without post-processing on real quantum devices.

Given the limits of current quantum devices (adiabatic annealers and gate-based systems), the researchers suggest the new algorithm is a significant step forwards particularly given the wider applicability of constrained combinatorial optimization.

They note in the papers abstract:

COPs are typically transformed into ground-state search problems of the Ising model on a quantum annealer or gate-based quantum device. Variational methods are used to find an optimal schedule function that leads to high-quality solutions in a short amount of time. Post-processing techniques convert the output solutions of the quantum devices to satisfy the constraints of the COPs.

pVSQA combines the variational methods and the post-processing technique. We obtain a sufficient condition for constrained COPs to apply pVSQA based on a greedy post-processing algorithm. We apply the proposed method to two constrained NP-hard COPs: the graph partitioning problem and the quadratic knapsack problem. pVSQA on a simulator shows that a small number of variational parameters is sufficient to achieve a (near-)optimal performance within a predetermined operation time. Then building upon the simulator results, we implement pVSQA on a quantum annealer and a gate-based quantum device. The experimental results demonstrate the effectiveness of our proposed method.

Link to Waseda University article, https://www.waseda.jp/top/en/news/80146

Link to IEEE paper, https://ieeexplore.ieee.org/document/10472069

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Waseda U. Researchers Reports New Quantum Algorithm for Speeding Optimization - HPCwire

What is the problem?

Accurately predicting the intensity of tropical cyclones, defined as the maximum sustained windspeed over a period of time, is a critical yet challenging task. Rapid intensification (RI) events are still a daunting problem for operational intensity forecasting.

Better forecasts and simulation of tropical cyclone (TC) intensities and their track can significantly improve the quality of Moodys RMS tropical cyclone modeling suite. RMS has helped clients manage their risk during TC events in the North Atlantic for almost 20 years. Real time TCs can significantly impact a companys financial, operational, and overall solvency state. Moodys RMS Hwind product helps (re)insurers, brokers, and capital markets understand the range of potential losses across multiple forecast scenarios, capturing the uncertainty in of how track and intensity will evolve.

With the advances in Numerical Weather Prediction (NWP) and new meteorological observations, forecasts of TC movement have progressively improved in global and regional models. However, the model accuracy in forecasting the intensities of TCs remains challenging for operational weather forecasting and consequential assessment of weather impacts such as high winds, storm surges, and heavy rainfall.

Since the current spatial resolution of the NWP model is insufficient for resolving convective scale processes and inner core dynamics of the cyclone, forecast intensities of TCs from operational models are mostly underestimated or low biased. Yet, accurate TC intensity guidance is crucial not only for assessing the impact of the TC, but also for generating realistic projections of storms and their associated hazards. This is essential for effective risk evaluation. Conventional TC intensity forecasting mainly relies on three approaches: statistical, dynamical, and statistical-dynamical methods.

Dynamical models, also known as numerical models, are the most complex and use high performance computing (HPC) to solve the physical equations of motion governing the atmosphere. While statistical models do not explicitly consider the physics of the atmosphere, they are based on historical relationships between storm behavior and storm-specific details such as location and intensity.

The rise of Machine Learning (ML) and Deep Learning (DL) has led to attempts to create breakthroughs in climate modeling and weather forecasting. Recent advances in computational capabilities and the availability of extensive reanalysis of observational or numerical datasets have reignited interest in developing various ML methods for predicting and understanding the dynamics of complex systems.

One of our key objectives is to build a quantum reservoir computing-based model, capable of processing climate model outputs and storm environment parameters, to provide more accurate forecasting, will improve short-term and real-time TC risk analysis.

Official modeling centers use consensus or ensemble-based dynamical models and represent the state of the art in tropical cyclone forecasting. However, these physics-based models may be subject to bias derived from high wind shear, low sea surface temperatures, or the storms location in the basin. By learning from past forecasting errors, we may be able to identify and correct past model biases, thereby greatly enhancing the quality of future forecasting and risk modeling products. The long-term aim is to integrate ML-based elements into coarse global climate models to improve their resolution and include natural dynamical processes currently absent in these models.

Reservoir Computing (RC) is a novel machine-learning algorithm particularly suited to quantum computers and has shown promising results in early non-linear time series prediction tests. In a classical setting, RC is stable and computationally simple. It works by mapping input time series signals into a higher dimensional computational space through the dynamics of a fixed, non-linear system known as a reservoir. This method is efficient, trainable, and has a low computational cost, making it a valuable tool for large-scale climate modeling.

While quantum machine learning has been considered a promising application for near-term quantum computers, current quantum machine learning methods require large quantum resources and suffer from gradient vanishing issues. Quantum Reservoir Computing (QRC) has the potential to combine the efficient machine learning of classical RC with the computing power of complex and high-dimensional quantum dynamics. QRC takes RC a step further by leveraging the unique capabilities of quantum processing units (QPUs) and their exponentially large state space, resulting in rich dynamics that cannot be simulated on a conventional computer. In particular, the flexible atom arrangements and tunability of optical controls within QuEras neutral atom QPU enable the realization of a rich class of Hamiltonians acting as the reservoir.

Recent studies on quantum computing simulators and hardware suggest that certain quantum model architectures used for learning on classical data can achieve results similar to that of classical machine learning models while using significantly fewer parameters. Overall, QRC offers a promising approach to resource-efficient, noise-resilient, and scalable quantum machine learning.

In this project, we are collaborating with QuEra Computing, the leading provider of quantum computers based on neutral-atoms , to explore the benefits of using quantum reservoir computing in climate science and to investigate the potential advantages that the quantum layer from QuEra can bring. QuEra's neutral atom QPU and the types of quantum simulations it can perform give rise to different quantum reservoirs. This unique capability can potentially enhance the modeling of tropical cyclone intensity forecasts and data.

This collaboration involves multiple stakeholders and partners, including QuEra Computing Inc., Moodys RMS technical team, and Moodys Quantum Taskforce. The work is supported by a DARPA grant award, underscoring its significance and potential impact in tropical cyclone modeling and forecasting.

In summary, combining quantum machine learning methods, reservoir computing, and the quantum capabilities of QuEra's technology offers a promising approach to addressing the challenges in predicting tropical cyclone intensity. This collaboration aims to enhance the quality and efficiency of tropical cyclone modeling, ultimately aiding in better risk assessment and decision making in the face of these natural disasters.

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Exploring the potential of quantum reservoir computing in forecasting the intensity of tropical cyclones - Moody's Analytics