Classiq/classiq-library
Apr 2025 – Apr 2025
1 Month active
Languages Used
Jupyter NotebookPython
Technical Skills
Algorithm ImplementationBayesian OptimizationData VisualizationQuantum ChemistryQuantum Computing
Kazuki Tsuoka
PROFILE
Kazuki Tsuoka
Kazuki Tsuoka developed a Jupyter notebook for the Classiq/classiq-library repository, implementing Bayesian Phase Difference Estimation to calculate numerical energy gradients in quantum chemistry. The work focused on constructing quantum circuits and applying them to the hydrogen molecule, enabling the calculation and visualization of potential energy surfaces and gradients. By integrating Bayesian optimization, Kazuki facilitated data-driven parameter tuning to determine energy differences efficiently. The notebook’s design emphasized reproducibility, with a clear commit history supporting traceability. Leveraging Python and advanced data visualization techniques, Kazuki’s contribution provided a comprehensive workflow for energy gradient analysis, demonstrating depth in algorithm implementation and quantum computing.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
967
Activity Months1
Your Network
59 peopleSame Organization
@g.ecc.u-tokyo.ac.jp9
Work History
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025 (2025-04) - Monthly work summary for Classiq/classiq-library. Delivered a BPDE notebook demonstrating Bayesian Phase Difference Estimation for numerical energy gradient calculations, including quantum circuit construction, application to the hydrogen molecule, visualization of potential energy surfaces and gradients, and Bayesian optimization to determine energy differences. The notebook was added to the paper implementation challenge, supported by a reproducible commit history.
1 Commits • 1 Features
Apr 1, 2025April 2025 (2025-04) - Monthly work summary for Classiq/classiq-library. Delivered a BPDE notebook demonstrating Bayesian Phase Difference Estimation for numerical energy gradient calculations, including quantum circuit construction, application to the hydrogen molecule, visualization of potential energy surfaces and gradients, and Bayesian optimization to determine energy differences. The notebook was added to the paper implementation challenge, supported by a reproducible commit history.
April 2025
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance60.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
Algorithm ImplementationBayesian OptimizationData VisualizationQuantum ChemistryQuantum Computing
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