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@quantumcomputingreport.com
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References:
Quantum Computing Report
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Poland is gearing up to enter the quantum computing arena with its first full-stack quantum computer, "Spark," slated for installation at the Wrocław University of Science and Technology (WUST). This initiative, spearheaded by Helsinki-based IQM, a leading European quantum hardware company, marks a significant advancement in Poland's technological landscape. Spark, a superconducting quantum computer, operates at ultra-low temperatures near absolute zero, leveraging superconducting circuits to manipulate quantum bits, or qubits. Professor Wojciech Bożejko, head of WUST’s faculty of ICT, emphasizes the importance of this milestone, noting that it is the first quantum computer in Poland and Eastern Europe to utilize low-temperature superconducting qubit technology.
This quantum computer, though equipped with only 5 qubits, making it less powerful than classical computers for most tasks, is strategically important for research and education. It will provide WUST students direct access to a real quantum computer for practical programming, allowing them to familiarize themselves with quantum mechanics and prepare for the era of quantum utility. Quantum utility refers to the point at which quantum computers can outperform classical computers in solving specific, real-world problems. WUST researchers will use Spark for computer science research, including doctoral candidates and members of the university's quantum computing club. IQM's broader strategy involves strengthening its presence in Central and Eastern Europe, building on the establishment of its Warsaw office in 2024. The company is committed to leading the region's quantum technology ecosystem through strategic partnerships, talent development, and localized solutions. The Spark system will be inaugurated at the Wrocław Centre for Networking and Supercomputing, coinciding with the center's 30th anniversary, accelerating research in computer science and enhancing student access to hands-on quantum programming. This deployment not only positions Poland as a new entrant in the global quantum computing landscape, but also underlines the nation's ambition to become a leader in next-generation computing technologies. Recommended read:
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@arstechnica.com
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References:
arstechnica.com
, www.techrepublic.com
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Microsoft researchers have achieved a significant breakthrough in AI efficiency with the development of a 1-bit large language model (LLM) called BitNet b1.58 2B4T. This model, boasting two billion parameters and trained on four trillion tokens, stands out due to its remarkably low memory footprint and energy consumption. Unlike traditional AI models that rely on 16- or 32-bit floating-point formats for storing numerical weights, BitNet utilizes only three distinct weight values: -1, 0, and +1. This "ternary" architecture dramatically reduces complexity, enabling the AI to run efficiently on a standard CPU, even an Apple M2 chip, according to TechCrunch.
The development of BitNet b1.58 2B4T represents a significant advancement in the field of AI, potentially paving the way for more accessible and sustainable AI applications. This 1-bit model, available on Hugging Face, uses a novel approach of representing each weight with a single bit. While this simplification can lead to a slight reduction in accuracy compared to larger, more complex models, BitNet b1.58 2B4T compensates through its massive training dataset, comprising over 33 million books. The reduction in memory usage is substantial, with the model requiring only 400MB of non-embedded memory, significantly less than comparable models. Comparisons against leading mainstream models like Meta’s LLaMa 3.2 1B, Google’s Gemma 3 1B, and Alibaba’s Qwen 2.5 1.5B have shown that BitNet b1.58 2B4T performs competitively across various benchmarks. In some instances, it has even outperformed these models. However, to achieve optimal performance and efficiency, the LLM must be used with the bitnet.cpp inference framework. This highlights a current limitation as the model does not run on GPU and requires a proprietary framework. Despite this, the creation of such a lightweight and efficient LLM marks a crucial step toward future AI that may not necessarily require supercomputers. Recommended read:
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@phys.org
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References:
phys.org
Recent research has spotlighted the diverse applications of mathematical and computational methods across multiple critical fields. One notable study, published in ACM Transactions on the Web, details the use of advanced mathematical techniques and software to investigate the collapse of the TerraUSD stablecoin and its associated currency, LUNA. Led by Dr. Richard Clegg at Queen Mary University of London, the research team employed temporal multilayer graph analysis to uncover suspicious trading patterns indicative of a coordinated attack, which led to the loss of $3.5 billion. The study highlights the power of mathematical tools in unraveling complex financial events.
Scientists have also made significant strides in fluid dynamics through the development of AI-powered simulation models. Researchers at Osaka Metropolitan University have created a machine learning model that dramatically reduces computation time for fluid simulations while maintaining accuracy. This innovation, which utilizes graph neural networks, has potential applications in offshore power generation, ship design, and real-time ocean monitoring, offering a scalable solution that balances accuracy with efficiency. The new model cuts simulation time from 45 minutes to just three minutes. The 23rd International Conference of Numerical Analysis and Applied Mathematics (ICNAAM 2025) also focuses on the integration of mathematical and computational methods across science and engineering. A session within the conference aims to unite researchers and practitioners in discussing novel ideas, methodologies, and applications that bridge the gap between mathematics and its practical implementations. The session welcomes contributions focusing on analytical and numerical techniques, algorithm development, and computational modeling, particularly those providing new insights into solving complex systems. Recommended read:
References :
Greg Bock@thequantuminsider.com
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Quantum computing has taken a significant leap forward with Phasecraft's development of a novel quantum simulation method called THRIFT (Trotter Heuristic Resource Improved Formulas for Time-dynamics). This breakthrough, detailed in a recent *Nature Communications* publication, drastically improves simulation efficiency and lowers computational costs, bringing real-world quantum applications closer to reality. THRIFT optimizes quantum simulations by prioritizing interactions with different energy scales within quantum systems, streamlining their implementation into smaller, more manageable steps.
This approach allows for larger and longer simulations to be executed without the need for increased quantum circuit size, thereby reducing computational resources and costs. In benchmarking tests using the 1D transverse-field Ising model, a widely used benchmark in quantum physics, THRIFT achieved a tenfold improvement in both simulation estimates and circuit complexities, enabling simulations that are ten times larger and run ten times longer compared to traditional methods. This development holds immense promise for advancements in materials science and drug discovery. Separately, mathematicians have achieved a breakthrough in understanding and modeling melting ice and other similar phenomena through a new proof that resolves long-standing issues related to singularities. A powerful mathematical technique used to model melting ice and other phenomena had been hampered by “nightmare scenarios.” A new proof has removed that obstacle. This new proof addresses concerns about "nightmare scenarios" that previously hindered the analysis of these processes, ensuring that singularities do not impede the continued evolution of the surface being modeled. The resolution, described in Quanta Magazine, allows mathematicians to more effectively assess the surface's evolution even after a singularity appears. Finally, researchers at Cornell University have introduced a novel data representation method inspired by quantum mechanics that tackles the challenge of handling big, noisy data sets. This quantum statistical approach simplifies large data sets and filters out noise, allowing for more efficient analysis than traditional methods. By borrowing mathematical structures from quantum mechanics, this technique enables a more concise representation of complex data, potentially revolutionizing innovation in data-rich fields such as healthcare and epigenetics where traditional methods have proven insufficient. Recommended read:
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