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The novel quantum data assimilation method can significantly reduce the computation time required for numerical weather prediction, enabling deeper understanding and improved predictions

Credit: Brett Jordan from Openverse https://openverse.org/image/563410ca-1385-475c-a7f6-fd521f910623

Data assimilation is a mathematical discipline that integrates observed data and numerical models to improve the interpretation and prediction of dynamical systems. It is a crucial component of earth sciences, particularly in numerical weather prediction (NWP). Data assimilation techniques have been widely investigated in NWP in the last two decades to refine the initial conditions of weather models by combining model forecasts and observational data. Most NWP centers around the world employ variational and ensemble-variational data assimilation methods, which iteratively reduce cost functions via gradient-based optimization. However, these methods require significant computational resources.

Recently, quantum computing has emerged as a new avenue of computational technology, offering a promising solution for overcoming the computational challenges of classical computers. Quantum computers can take advantage of quantum effects such as tunneling, superposition, and entanglement to significantly reduce computational demands. Quantum annealing machines, in particular, are powerful for solving optimization problems.

In a recent study, Professor Shunji Kotsuki from the Institute for Advanced Academic Research/Center for Environmental Remote Sensing/Research Institute of Disaster Medicine, Chiba University, along with his colleagues Fumitoshi Kawasaki from the Graduate School of Science and Engineering and Masanao Ohashi from the Center for Environmental Remote Sensing, developed a novel data assimilation technique designed for quantum annealing machines. "Our study introduces a novel quantum annealing approach to accelerate data assimilation, which is the main computational bottleneck for numerical weather predictions. With this algorithm, we successfully solved data assimilation on quantum annealers for the first time," explains Prof. Kotsuki. Their study has been published in the journal Nonlinear Processes in Geophysics on June 07, 2024.

In the study, the researchers focused on the four-dimensional variational data assimilation (4DVAR) method, one of the most widely used data assimilation methods in NWP systems. However, since 4DVAR is designed for classical computers, it cannot be directly used on quantum hardware. Prof. Kotsuki clarifies, "Unlike the conventional 4DVAR, which requires a cost function and its gradient, quantum annealers require only the cost function. However, the cost function must be represented by binary variables (0 or 1). Therefore, we reformulated the 4DVAR cost function, a quadratic unconstrained optimization (QUO) problem, into a quadratic unconstrained binary optimization (QUBO) problem, which quantum annealers can solve."

The researchers applied this QUBO approach to a series of 4DVAR experiments using a 40-variable Lorentz-96 model, which is a dynamical system commonly used to test data assimilation. They conducted the experiments using the D-Wave Advantage physical quantum annealer, or Phy-QA, and the Fixstars Amplify's simulated quantum annealer, or Sim-QA. Moreover, they tested the conventionally utilized quasi-Newton-based iterative approaches, using the Broyden-Fletcher-Goldfarb-Shanno formula, in solving linear and nonlinear QUO problems and compared their performance to that of quantum annealers.

The results revealed that quantum annealers produced analysis with comparable accuracy to conventional quasi-Newton-based approaches but in a fraction of the time they took. The D-Wave's Phy-QA required less than 0.05 seconds for computation, much faster than conventional approaches. However, it also exhibited slightly larger root mean square errors, which the researchers attributed to the inherent stochastic quantum effects. To address this, they found that reading out multiple solutions from the quantum annealer improved stability and accuracy. They also noted that the scaling factor for quantum data assimilation, which is important for regulating the analysis accuracy, was different for the D-Wave Phy-QA and the Sim-QA, owing to the stochastic quantum effects associated with the former annealer.

These findings signify the role of quantum computers in reducing the computational cost of data assimilation. "Our approach could revolutionize future NWP systems, enabling a deeper understanding and improved predictions with much less computational time. In addition, it has the potential to advance the practical applications of quantum annealers in solving complex optimization problems in earth science," remarks Prof. Kotsuki.

Overall, the proposed innovative method holds great promise for inspiring future applications of quantum computers in advancing data assimilation, potentially leading to more accurate weather predictions.

About Professor Shunji Kotsuki

Dr. Shunji Kotsuki is currently a Professor at the Institute for Advanced Academic Research (IAAR), Chiba University, leading "Environmental Prediction Science." He received his B.S. (2009), M.S. (2011), and Ph.D. (2013) degrees in civil engineering from Kyoto University. He has over 40 publications and received over 500 citations. Dr. Kotsuki is a leading scientist in data assimilation, deep learning numerical weather prediction with over ten years of research experience in the development of the global atmospheric data assimilation system (a.k.a. NICAM-LETKF). His research interests include data assimilation mathematics, model parameter estimation, observation diagnosis including impact estimates, satellite data analysis, hydrological modeling, and atmospheric and hydrological disaster predictions. He is currently the project manager for Goal 8 of Japan's Moonshot Program, where he leads an interdisciplinary research team. This team includes experts in meteorology, disaster mathematics, information science, computer vision, ethics, and legal studies, all working together to achieve a weather-controlled society.

Nonlinear Processes in Geophysics

Computational simulation/modeling

Not applicable

Quantum Data Assimilation: A New Approach to Solve Data Assimilation on Quantum Annealers

7-Jun-2024

The authors have no competing interests to declare.

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

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Quantum data assimilation: A quantum leap in weather prediction - EurekAlert

Technology firms, both public and private, have been working hard to develop quantum computing technologies for decades. The reasons for that are straightforward. Quantum machines, which harness the quantum mechanics undergirding subatomic particles, have a number of advantages over classical computers. Portfolio optimization and climate predictive algorithms that improve with more complexity are better handled by quantum computers.

U.S. equities markets have surged with the rise of generative artificial intelligence (AI) and its potential to create enormous efficiencies and profits for firms across various industries. While AI has brought quantum computing back into the spotlight, a lack of practical ways to scale these complex products has severely dented the performance of pure-play quantum computing stocks, such as IonQ (NYSE:IONQ) and Rigetti Computing (NASDAQ:RGTI).

Fortunately, not every public company invested in quantum computing has seen doom and gloom. Below are the three best quantum computing stocks investors should buy in June.

Source: shutterstock.com/LCV

International Business Machines (NYSE:IBM) is a legacy American technology business. It has its hands in everything from cloud infrastructure, artificial intelligence, and technology consulting services to quantum computers.

The firm committed to developing quantum computing technologies in the early 2000s and tends to publish new findings in the burgeoning field frequently. In December 2023, IBM released a new quantum chip system, Quantum System Two, that leverages the firms Heron processor, which has 133 qubits. Qubits are analogous to bytes on a classical computer. But instead of being confined to states of 0s and 1s, qubits, by way of superposition, can assume both states at the same time.

Moreover, what makes Quantum System Two particularly innovative is its use of both quantum and classical computing technologies. In a press release, IBM states, It combines scalable cryogenic infrastructure and classical runtime servers with modular qubit control electronics. IBM believes the combination of quantum computation and communication with classical computing resources can create a scalable quantum machine.

IBMs innovations in quantum computing technologies as well as AI has not gone unnoticed either. Shares have risen 31.3% over the past 12 months. The computing giants relatively cheap valuation coupled with its exposure to novel, high-growth fields could boost the value of its shares in the long-term.

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Investors have given Nvidia (NASDAQ:NVDA) attention and praise over the past 12 months due to its critical role in AI computing technologies. The chipmakers advanced GPUs, including the H100 and H200 processors, are some of the most coveted chips on the market. The new Blackwell chips, coming to the market in the second half of 2024, bring to the table even better performance.

Though Nvidias prowess in the world of AI captures much of the headlines, the firm has already made inroads into the next stage of computing. In 2023, Nvidia announced a new quantum system in conjunction with startup Quantum Machines. It leverages what Nvidia calls the Grace Hoper Super Chip (GH200) as well as the chipmaker advanced CUDA Quantum (CUDA-Q) developer software.

In 2024, Nvidia released its Quantum Cloud platform, which allows users to build and test quantum computing algorithms in the cloud. The chipmakers GPUs and its open-source CUDA platform will likely be essential to scaling up the quantum computing space.

Nvidias share price has surged 214.2% over the past 12 months.

Source: Bartlomiej K. Wroblewski / Shutterstock.com

Quantum computers are complex machines that require all kinds of components. Furthermore, it is vital for quantum systems to operate at extremely low temperatures in order to operate efficiently.

FormFactor (NASDAQ:FORM) specializes in developing cryogenic systems or systems that are meant to deal with low temperatures. Everything from wafer testing probes to low-vibration probe stations as well as sophisticated refrigerators call cryostats, FormFactor provides. Also, the firms analytical probe tools are useful for developing advanced chips, such as NAND flash memory.

With quantum computing systems and advanced memory chips in greater demand these days, FormFactor could see revenues and earnings rise in the near and medium terms. FormFactors share price has surged 77.5% over the past 12 months, underscoring that investors are taking notice of the companys long-term value.

At the beginning of May, FormFactor released first quarter results for fiscal year 2024 and topped revenue estimates while EPS came in line with market expectations. The firm expects strong demand for advanced memory chips, such as DRAM, will help propel revenue growth in the following quarters.

On the date of publication, Tyrik Torresdid not have (either directly or indirectly) any positions in the securities mentioned in this article.The opinions expressed in this article are those of the writer, subject to the InvestorPlace.comPublishing Guidelines.

Tyrik Torres has been studying and participating in financial markets since he was in college, and he has particular passion for helping people understand complex systems. His areas of expertise are semiconductor and enterprise software equities. He has work experience in both investing (public and private markets) and investment banking.

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The 3 Best Quantum Computing Stocks to Buy in June 2024 - InvestorPlace

Capitalize on the synergy between AI and quantum with these millionaire-maker quantum computing stocks

Quantum computing, with its unparalleled data processing speed, has the potential to usher in a new era in tech. Moreover, the synergy between AI and quantum computing will elevate millionaire-maker quantum computing stocks to new heights. The industry is likely to achieve these kinds of returns as a result of becoming a new critical technology at the center of data processing and connection.

Moreover, quantum tech is leaving traditional silicon-based systems in the dust. Beyond this, some of the most influential companies in the tech world are driving the industry, promising exciting opportunities for investors. However, backing the right horses in the race for quantum supremacy is important to maximize your upside potential.

That said, here are three millionaire-maker quantum computing stocks worth investing in for the long haul. Thats because the industry still operates on the fringes of science and technology, making it a long-term play for those looking for generous returns.

IonQ (NYSE:IONQ) is the top pure-play quantum computing stock, perhaps the most promising among its peers. It has made some impressive strides of late, achieving ion stability for an hour, a feat that far comfortably outpaces its competition. Its promise is reflected in its recent strong financial performance. It recently reported its first-quarter (Q1) results, where sales soared 77.2% on a year-over-year (YOY) basis to $7.6 million. Additionally, its loss of 19 cents per share beat expectations by six cents. For the full year, it expects sales between $37 million and $41 million, over 70% growth at the mid-point on a YOY basis. Moreover, the company recently partnered with Oak Ridge National Laboratory (ORNL) to leverage quantum technology to modernize the power grid. This stellar partnership, along with others, demonstrates IonQs ability to innovate and expand its applications, offering healthy long-term upside ahead for its investors.

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Investing in quantum computing can be complicated and speculative at the same time. To simplify the process, the Defiance Quantum ETF (NYSEARCA:QTUM) works best, with it investing in AI stocks to provide a balanced cushion.

The QTUM ETF offers investors exposure to some of the leading global businesses in transformative technologies such as machine learning, quantum computing, and cloud platforms. It holds investments in 70 different stocks, with its top 10 holdings representing just 20% of its $252 million net assets. Hence, its holdings are highly diversified, with an expense ratio of just 0.40%. Some of the companies in its investment portfolio are MicroStrategy(NASDAQ:MSTR),Nvidia(NASDAQ:NVDA), andMKS Instruments(NASDAQ:MKSI) to name a few.

Moreover, QTUM stock has been a smashing success for its investors in the past five years, generating a total return of over 175%, 361% higher than the median of all ETFs. In the past year alone, its up 30% and is positioned for healthy long-term gains.

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Microsoft (NASDAQ:MSFT), a tech giant, has tentacles in virtually every major tech vertical, and quantum computing is no different. The AI revolution took Microsofts business up a notch or two last year, and it is eyeing quantum computing as the next frontier. Its partnership with quantum computing pure-play Quantinuum could be a breakthrough for the entire sector. According to a recent statement from one of Microsofts executives, the company has made massive progress in reducing qubit error rates, which is critical for commercializing quantum technology. Its qubit-virtualization system applied to Quantinuums ion-trap hardware, led to more than 14,000 error-free experiments. The breakthrough will set the stage for Quantinuums Helios H-Series quantum computer by next year. Moreover, the collaboration between the two tech companies aims to go from 100 reliable logical qubits to a whopping 1,000 qubits. If these lofty plans come to fruition, I wont be surprised if MSFT stock goes on another monumental run like last year.

On thedate of publication, Muslim Farooque did not have (eitherdirectly or indirectly) any positions in the securities mentioned in this article.The opinions expressed in this article are those of the writer, subject to the InvestorPlace.comPublishing Guidelines

Muslim Farooque is a keen investor and an optimist at heart. A life-long gamer and tech enthusiast, he has a particular affinity for analyzing technology stocks. Muslim holds a bachelors of science degree in applied accounting from Oxford Brookes University.

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3 Quantum Computing Stocks to Turn $100000 Into $1 Million: June Edition - InvestorPlace

By U.S. Department of Energy June 14, 2024

Artists representation of the formation pathway of vacancy complexes for spin-based qubits in the silicon carbide host lattice and to the right the associated energy landscape. Credit: University of Chicago

Quantum computers, leveraging the unique properties of qubits, outperform classical systems by simultaneously existing in multiple states. Focused research on silicon carbide aims to optimize qubits for scalable application, with studies revealing new methods to control and enhance their performance. This could lead to breakthroughs in large-scale quantum computing and sensor technologies.

While conventional computers use classical bits for calculations, quantum computers use quantum bits, or qubits, instead. While classical bits can have the values 0 or 1, qubits can exist in a mix of probabilities of both values at the same time. This makes quantum computing extremely powerful for problems conventional computers arent good at solving. To build large-scale quantum computers, researchers need to understand how to create and control materials that are suitable for industrial-scale manufacturing.

Semiconductors are very promising qubit materials. Semiconductors already make up the computer chips in cell phones, computers, medical equipment, and other applications. Certain types of atomic-scale defects, called vacancies, in the semiconductor silicon carbide (SiC) show promise as qubits. However, scientists have a limited understanding of how to generate and control these defects. By using a combination of atomic-level simulations, researchers were able to track how these vacancies form and behave.

Quantum computing could revolutionize our ability to answer challenging questions. Existing small scale quantum computers have given a glimpse of the technologys power. To build and deploy large-scale quantum computers, researchers need to know how to control qubits made of materials that make technical and economic sense for industry.

The research identified the stability and molecular pathways to create the desired vacancies for qubits and determine their electronic properties.

These advances will help the design and fabrication of spin-based qubits with atomic precision in semiconductor materials, ultimately accelerating the development of next-generation large-scale quantum computers and quantum sensors.

The next technological revolution in quantum information science requires researchers to deploy large-scale quantum computers that ideally can operate at room temperature. The realization and control of qubits in industrially relevant materials is key to achieving this goal.

In the work reported here, researchers studied qubits built from vacancies in silicon carbide (SiC) using various theoretical methods. Until now, researchers knew little about how to control and engineer the selective formation process for the vacancies. The involved barrier energies for vacancy migration and combination pose the most difficult challenges for theory and simulations.

In this study, a combination of state-of-the-art materials simulations and neural-network-based sampling technique led researchers at the Department of Energys (DOE) Midwest Center for Computational Materials (MICCoM) to discover the atomistic generation mechanism of qubits from spin defects in a wide-bandgap semiconductor.

The team showed the generation mechanism of qubits in SiC, a promising semiconductor with long qubit coherence times and all-optical spin initialization and read-out capabilities.

MICCoM is one of the DOE Computational Materials Sciences centers across the country that develops open-source, advanced software tools to help the scientific community model, simulate, and predict the fundamental properties and behavior of functional materials. The researchers involved in this study are from Argonne National Laboratory and the University of Chicago.

Reference: Stability and molecular pathways to the formation of spin defects in silicon carbide by Elizabeth M. Y. Lee, Alvin Yu, Juan J. de Pablo and Giulia Galli, 3 November 2021,Nature Communications. DOI: 10.1038/s41467-021-26419-0

This work was supported by the Department of Energy (DOE) Office of Science, Office of Basic Energy Sciences, Materials Sciences and Engineering Division and is part of the Basic Energy Sciences Computational Materials Sciences Program in Theoretical Condensed Matter Physics. The computationally demanding simulations used several high-performance computing resources: Bebop in Argonne National Laboratorys Laboratory Computing Resource Center; the Argonne Leadership Computing Facility (ALCF), a DOE Office of Science user facility; and the University of Chicagos Research Computing Center. The team was awarded access to ALCF computing resources through DOEs Innovative and Novel Computational Impact on Theory and Experiment (INCITE) program. Additional support was provided by NIH.

Link:
Better Qubits: Quantum Breakthroughs Powered by Silicon Carbide - SciTechDaily

image:

Kaushalya Jhuria in the lab testing the electronics from the experimental setup used to make qubits in silicon.

Credit: Thor Swift/Berkeley Lab

Quantum computers have the potential to solve complex problems in human health, drug discovery, and artificial intelligence millions of times faster than some of the worlds fastest supercomputers. A network of quantum computers could advance these discoveries even faster. But before that can happen, the computer industry will need a reliable way to string together billions of qubits or quantum bits with atomic precision.

Connecting qubits, however, has been challenging for the research community. Some methods form qubits by placing an entire silicon wafer in a rapid annealing oven at very high temperatures. With these methods, qubits randomly form from defects (also known as color centers or quantum emitters) in silicons crystal lattice. And without knowing exactly where qubits are located in a material, a quantum computer of connected qubits will be difficult to realize.

But now, getting qubits to connect may soon be possible. A research team led by Lawrence Berkeley National Laboratory (Berkeley Lab) says that they are the first to use a femtosecond laser to create and annihilate qubits on demand, and with precision, by doping silicon with hydrogen.

The advance could enable quantum computers that use programmable optical qubits or spin-photon qubits to connect quantum nodes across a remote network. It could also advance a quantum internet that is not only more secure but could also transmit more data than current optical-fiber information technologies.

To make a scalable quantum architecture or network, we need qubits that can reliably form on-demand, at desired locations, so that we know where the qubit is located in a material. And that's why our approach is critical, said Kaushalya Jhuria, a postdoctoral scholar in Berkeley Labs Accelerator Technology & Applied Physics (ATAP) Division. She is the first author on a new study that describes the technique in the journal Nature Communications. Because once we know where a specific qubit is sitting, we can determine how to connect this qubit with other components in the system and make a quantum network.

This could carve out a potential new pathway for industry to overcome challenges in qubit fabrication and quality control, said principal investigator Thomas Schenkel, head of the Fusion Science & Ion Beam Technology Program in Berkeley Labs ATAP Division. His group will host the first cohort of students from the University of Hawaii in June as part of a DOE Fusion Energy Sciences-funded RENEW project on workforce development where students will be immersed in color center/qubit science and technology.

Forming qubits in silicon with programmable control

The new method uses a gas environment to form programmable defects called color centers in silicon. These color centers are candidates for special telecommunications qubits or spin photon qubits. The method also uses an ultrafast femtosecond laser to anneal silicon with pinpoint precision where those qubits should precisely form. A femtosecond laser delivers very short pulses of energy within a quadrillionth of a second to a focused target the size of a speck of dust.

Spin photon qubits emit photons that can carry information encoded in electron spin across long distances ideal properties to support a secure quantum network. Qubits are the smallest components of a quantum information system that encodes data in three different states: 1, 0, or a superposition that is everything between 1 and 0.

With help from Boubacar Kant, a faculty scientist in Berkeley Labs Materials Sciences Division and professor of electrical engineering and computer sciences (EECS) at UC Berkeley, the team used a near-infrared detector to characterize the resulting color centers by probing their optical (photoluminescence) signals.

What they uncovered surprised them: a quantum emitter called the Ci center. Owing to its simple structure, stability at room temperature, and promising spin properties, the Ci center is an interesting spin photon qubit candidate that emits photons in the telecom band. We knew from the literature that Ci can be formed in silicon, but we didnt expect to actually make this new spin photon qubit candidate with our approach, Jhuria said.

The researchers learned that processing silicon with a low femtosecond laser intensity in the presence of hydrogen helped to create the Ci color centers. Further experiments showed that increasing the laser intensity can increase the mobility of hydrogen, which passivates undesirable color centers without damaging the silicon lattice, Schenkel explained.

A theoretical analysis performed by Liang Tan, staff scientist in Berkeley Labs Molecular Foundry, shows that the brightness of the Ci color center is boosted by several orders of magnitude in the presence of hydrogen, confirming their observations from laboratory experiments.

The femtosecond laser pulses can kick out hydrogen atoms or bring them back, allowing the programmable formation of desired optical qubits in precise locations, Jhuria said.

The team plans to use the technique to integrate optical qubits in quantum devices such as reflective cavities and waveguides, and to discover new spin photon qubit candidates with properties optimized for selected applications.

Now that we can reliably make color centers, we want to get different qubits to talk to each other which is an embodiment of quantum entanglement and see which ones perform the best. This is just the beginning, said Jhuria.

The ability to form qubits at programmable locations in a material like silicon that is available at scale is an exciting step towards practical quantum networking and computing, said Cameron Geddes, Director of the ATAP Division.

Theoretical analysis for the study was performed at the Department of EnergysNational Energy Research Scientific Computing Center (NERSC) at Berkeley Lab with support from the NERSC QIS@Perlmutterprogram.

The Molecular Foundry and NERSC are DOE Office of Science user facilities at Berkeley Lab.

This work was supported by the DOE Office of Fusion Energy Sciences.

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Lawrence Berkeley National Laboratory (Berkeley Lab) is committed to delivering solutions for humankind through research in clean energy, a healthy planet, and discovery science. Founded in 1931 on the belief that the biggest problems are best addressed by teams, Berkeley Lab and its scientists have been recognized with 16 Nobel Prizes. Researchers from around the world rely on the Labs world-class scientific facilities for their own pioneering research. Berkeley Lab is a multiprogram national laboratory managed by the University of California for the U.S. Department of Energys Office of Science.

DOEs Office of Science is the single largest supporter of basic research in the physical sciences in the United States, and is working to address some of the most pressing challenges of our time. For more information, please visitenergy.gov/science.

Nature Communications

Experimental study

Not applicable

Programmable quantum emitter formation in silicon

27-May-2024

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

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New technique could help build quantum computers of the future - EurekAlert