Hamiltonian operator
PulseAugur coverage of Hamiltonian operator — every cluster mentioning Hamiltonian operator across labs, papers, and developer communities, ranked by signal.
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FlowPET framework enhances low-count PET reconstruction with physics-informed approach
Researchers have developed FlowPET, a novel physics-informed framework for Positron Emission Tomography (PET) reconstruction, specifically designed to address challenges in low-count scenarios. Unlike traditional genera…
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New Superstate Quantum Mechanics theory bridges physics and AI
A new theoretical framework called Superstate Quantum Mechanics (SQM) has been introduced, which expands upon traditional quantum mechanics by considering states in Hilbert space with multiple quadratic constraints. Thi…
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Quantum ML shows provable learning separation over classical methods
Researchers have demonstrated a provable learning separation for predicting the time-evolution of quantum many-body systems. The study, published on arXiv, outlines a supervised learning problem where quantum machine le…
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Researchers quantize artificial neurons for enhanced machine learning capabilities
Researchers have developed a novel approach to quantize artificial neurons, drawing parallels between classical machine learning components and quantum physics principles. By treating neurons as a combination of energy …
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New benchmark HamQASBench evaluates quantum circuit design
Researchers have introduced HamQASBench, a new diagnostic benchmark designed to evaluate Quantum Architecture Search (QAS) methods. Unlike previous benchmarks that focus on molecular identity or qubit count, HamQASBench…
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New algorithm learns structure of open quantum systems
Researchers have developed a novel algorithm for learning the coefficients of an n-qubit Lindbladian, a mathematical model used to describe open quantum systems. This algorithm achieves several key desiderata, including…
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Quantum Neural Network Explores Groundwater Heat Prediction
Researchers have developed a Quantum Convolutional Neural Network (QCNN) to predict groundwater heat plume dynamics, a complex environmental modeling task. The QCNN was trained using reduced-dimension simulation outputs…
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AI model tunes quantum dots for Majorana modes
Researchers have developed a novel AI-enhanced method for tuning quantum dot simulators to achieve Majorana modes. This approach utilizes a deep vision-transformer network trained on synthetic data, incorporating a phys…
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Meta-learning structure-preserving dynamics for few-shot adaptation in physical systems
Researchers have developed a new meta-learning approach for discovering structure-preserving dynamics in physical systems. This method utilizes modulation techniques within a Hamiltonian learning framework, eliminating …
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Meta-learning framework HAML aids superconducting qubit Hamiltonian reduction
Researchers have developed HAML (Hamiltonian Adaptation via Meta-Learning), a new framework designed for the rapid online adjustment of effective Hamiltonian models in superconducting quantum processors. This system use…