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@Google DeepMind Blog
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Google DeepMind has introduced AlphaEvolve, a revolutionary AI coding agent designed to autonomously discover innovative algorithms and scientific solutions. This groundbreaking research, detailed in the paper "AlphaEvolve: A Coding Agent for Scientific and Algorithmic Discovery," represents a significant step towards achieving Artificial General Intelligence (AGI) and potentially even Artificial Superintelligence (ASI). AlphaEvolve distinguishes itself through its evolutionary approach, where it autonomously generates, evaluates, and refines code across generations, rather than relying on static fine-tuning or human-labeled datasets. AlphaEvolve combines Google’s Gemini Flash, Gemini Pro, and automated evaluation metrics.
AlphaEvolve operates using an evolutionary pipeline powered by large language models (LLMs). This pipeline doesn't just generate outputs—it mutates, evaluates, selects, and improves code across generations. The system begins with an initial program and iteratively refines it by introducing carefully structured changes. These changes take the form of LLM-generated diffs—code modifications suggested by a language model based on prior examples and explicit instructions. A diff in software engineering refers to the difference between two versions of a file, typically highlighting lines to be removed or replaced. Google's AlphaEvolve is not merely another code generator, but a system that generates and evolves code, allowing it to discover new algorithms. This innovation has already demonstrated its potential by shattering a 56-year-old record in matrix multiplication, a core component of many machine learning workloads. Additionally, AlphaEvolve has reclaimed 0.7% of compute capacity across Google's global data centers, showcasing its efficiency and cost-effectiveness. AlphaEvolve imagined as a genetic algorithm coupled to a large language model.
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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.
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Stephen Ornes@Quanta Magazine
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A novel quantum algorithm has demonstrated a speedup over classical computers for a significant class of optimization problems, according to a recent report. This breakthrough could represent a major advancement in harnessing the potential of quantum computers, which have long promised faster solutions to complex computational challenges. The new algorithm, known as decoded quantum interferometry (DQI), outperforms all known classical algorithms in finding good solutions to a wide range of optimization problems, which involve searching for the best possible solution from a vast number of choices.
Classical researchers have been struggling to keep up with this quantum advancement. Reports of quantum algorithms often spark excitement, partly because they can offer new perspectives on difficult problems. The DQI algorithm is considered a "breakthrough in quantum algorithms" by Gil Kalai, a mathematician at Reichman University. While quantum computers have generated considerable buzz, it has been challenging to identify specific problems where they can significantly outperform classical machines. This new algorithm demonstrates the potential for quantum computers to excel in optimization tasks, a development that could have broad implications across various fields.
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