quantum chemistry
PulseAugur coverage of quantum chemistry — every cluster mentioning quantum chemistry across labs, papers, and developer communities, ranked by signal.
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LLM-driven agent simplifies quantum chemistry workflows
Researchers have developed SQD-Agent, an LLM-based framework designed to simplify the creation of quantum chemistry workflows. This agent translates natural language instructions into executable hybrid quantum-classical…
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Quantum computing scaling roadmap integrates classical HPC for millions of qubits
A new arXiv paper outlines a path to scaling quantum computers from hundreds to millions of qubits by leveraging semiconductor manufacturing techniques and integrating them with classical high-performance computing. The…
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New research reveals topological obstructions in neural quantum states
A new research paper published on arXiv details a topological obstruction that affects pure foundation neural quantum states. The study demonstrates that for gapped Hamiltonians with non-trivial ground-state bundles, co…
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Machine learning poised to revolutionize quantum chemistry research
A new position paper argues that machine learning is the most promising direction for advancing quantum chemistry. The paper posits that traditional methods like density functional theory and wavefunction methods are re…
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Heidelberg University Researchers Receive ERC Advanced Grants for AI and Quantum Chemistry Projects
The European Research Council (ERC) has awarded Advanced Grants to researchers at Heidelberg University. These prestigious grants will fund projects across various disciplines, including quantum chemistry, AI, and psych…
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LLMs Automate Quantum Circuit Design, New Gradient Estimators Boost Training Efficiency
Researchers have developed an LLM-driven system for autonomously designing quantum circuits, integrating knowledge acquisition, code generation, and experimental feedback. This framework has shown success in constructin…
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New adaptive ML framework slashes quantum chemistry data costs
Researchers have developed a new on-the-fly multifidelity machine learning framework designed to optimize data generation for quantum chemistry calculations. This adaptive approach dynamically selects training samples a…
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MōLe-Λ: AI model accelerates quantum chemistry calculations
Researchers have developed MōLe-Λ, a novel machine learning model designed to predict quantum chemistry properties more efficiently. This model extends the existing MōLe framework to learn both the right-hand (T) and le…
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AI agents discover new density functionals, improving accuracy by 9%
An AI agent has discovered a new exchange-correlation functional, SAFS26-a, which improves accuracy by 9% over the previous standard in quantum chemistry. Separately, a new algorithm called BALAR, a Bayesian Agentic Loo…
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Quantum-inspired eigensolver slashes parameters, boosts performance for quantum chemistry
Researchers have developed a new quantum-inspired eigensolver called GQKAE, designed to improve the efficiency of high-performance computing in quantum chemistry. This model replaces traditional feed-forward networks wi…
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Machine learning aids design of light-activated cancer drugs
Researchers have developed a computational method to design photoactive PARP1 inhibitors for cancer treatment. By screening 5 million hypothetical ligands using machine learning and atomistic simulations, they identifie…