quantumlib/Cirq
Oct 2024 – Mar 2026
13 Months active
Languages Used
PythonprotobufProtocol BuffersJupyter NotebookYAML
Technical Skills
Circuit OptimizationQuantum ComputingSoftware EngineeringAPI DesignData SerializationGate Implementation
Noureldin
PROFILE
Noureldin
Noureldin Yosri contributed to the quantumlib/Cirq repository by engineering advanced quantum circuit tooling and optimization features, focusing on circuit transformation, benchmarking, and calibration workflows. He implemented parallelized benchmarking, unit-aware parameter serialization, and robust calibration transformers, leveraging Python, Protocol Buffers, and NumPy to enhance simulation fidelity and performance. His work included refactoring for maintainability, improving test reliability across CI pipelines, and resolving subtle bugs in symbolic mathematics and parallel processing. By introducing features like idle moment gauge transformations and stabilizer effect optimizations, Noureldin enabled more scalable, accurate, and maintainable quantum experiments, demonstrating depth in both algorithmic design and software engineering.
Overall Statistics
Feature vs Bugs
77%Features
Repository Contributions
23Total
Bugs
5
Commits
23
Features
17
Lines of code
6,490
Activity Months13
Your Network
4402 peopleSame Organization
@google.com4362
Benedict OdaiMember
Craig IngramMember
KayyuriMember
Scott SuarezMember
Agent2Agent (A2A) BotMember
Andreas AbelMember
Aadi KapurMember
Aadish GoelMember
Aahil MehtaMember
Work History
March 2026
1 Commits • 1 Features
Mar 1, 2026Monthly summary for 2026-03 focusing on business value and technical achievements for the quantumlib/Cirq workstream. Key features delivered: - Quantum Circuit Parallel Execution Refactor: Replaced multiprocessing pool with concurrent.futures.Executor in quantum circuit simulations to improve compatibility and resolve conflicts with duet. Commit: 1d14c8493424e4ceed2a1129f665435e74dc9f37. This change reduces synchronization issues and lays groundwork for scalable concurrency. Major bugs fixed: - Resolved conflict with duet via executor-based parallel execution; aligned with fixes for b/490175992, improving stability of parallel simulation workflows. Overall impact and accomplishments: - Improved reliability and stability of quantum circuit simulations under parallel workloads, increasing developer productivity and confidence in large-scale runs. - Enhanced cross-library compatibility, enabling smoother integration with related tooling and faster iteration on features. - Clearer commit traceability and maintainability through focused changes and bug-tracking alignment. Technologies/skills demonstrated: - Python concurrency: concurrent.futures.Executor in place of multiprocessing for safer parallelism - Code refactoring with minimal behavioral changes and improved error handling - Repository hygiene: consistent commits, issue linkage, and documentation of changes - Impact-driven development: focus on performance, compatibility, and maintainability.
1 Commits • 1 Features
Mar 1, 2026Monthly summary for 2026-03 focusing on business value and technical achievements for the quantumlib/Cirq workstream. Key features delivered: - Quantum Circuit Parallel Execution Refactor: Replaced multiprocessing pool with concurrent.futures.Executor in quantum circuit simulations to improve compatibility and resolve conflicts with duet. Commit: 1d14c8493424e4ceed2a1129f665435e74dc9f37. This change reduces synchronization issues and lays groundwork for scalable concurrency. Major bugs fixed: - Resolved conflict with duet via executor-based parallel execution; aligned with fixes for b/490175992, improving stability of parallel simulation workflows. Overall impact and accomplishments: - Improved reliability and stability of quantum circuit simulations under parallel workloads, increasing developer productivity and confidence in large-scale runs. - Enhanced cross-library compatibility, enabling smoother integration with related tooling and faster iteration on features. - Clearer commit traceability and maintainability through focused changes and bug-tracking alignment. Technologies/skills demonstrated: - Python concurrency: concurrent.futures.Executor in place of multiprocessing for safer parallelism - Code refactoring with minimal behavioral changes and improved error handling - Repository hygiene: consistent commits, issue linkage, and documentation of changes - Impact-driven development: focus on performance, compatibility, and maintainability.
March 2026
December 2025
1 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary for quantumlib/Cirq: Delivered a targeted runtime optimization for stabilizer analysis in PhasedXZGate and added robust tests, resulting in a 9.7x speedup for cirq.has_stabilizer_effect (from 120.3s to 12.4s) on benchmark scenarios. Implemented _has_stabilizer_effect_ to accelerate stabilizer checks, with tests validating performance and correctness across scenarios. This work reduces analysis latency for large quantum circuits, improving CI feedback and user experience. No major bugs fixed this month; ongoing improvements to performance and test coverage across the repository.
December 2025
1 Commits • 1 Features
Dec 1, 2025December 2025 monthly summary for quantumlib/Cirq: Delivered a targeted runtime optimization for stabilizer analysis in PhasedXZGate and added robust tests, resulting in a 9.7x speedup for cirq.has_stabilizer_effect (from 120.3s to 12.4s) on benchmark scenarios. Implemented _has_stabilizer_effect_ to accelerate stabilizer checks, with tests validating performance and correctness across scenarios. This work reduces analysis latency for large quantum circuits, improving CI feedback and user experience. No major bugs fixed this month; ongoing improvements to performance and test coverage across the repository.
October 2025
1 Commits
Oct 1, 2025Month: 2025-10 — Stability-focused update for Cirq in quantumlib/Cirq. This period emphasized improving test reliability and cross-version NumPy compatibility, leveraging targeted test changes and commit-based traceability. Key features delivered: - Stabilized the test suite by removing xfail markers on quantum_shannon_decomposition_test.py to reflect updated NumPy compatibility, enabling reliable test outcomes with newer NumPy versions. Major bugs fixed: - Fixed test fragility caused by NumPy version differences; updated test expectations to align with current NumPy behavior, reducing false negatives and CI noise. This work supports the resolution of Cirq issue #6770 and related tracker #7712. Overall impact and accomplishments: - Improved CI reliability and developer productivity due to fewer flaky tests, faster feedback loops, and safer validation across NumPy versions. This reduces maintenance overhead and increases confidence for downstream users. Technologies/skills demonstrated: - Python testing and test maintenance, NumPy compatibility in a mature codebase, Git-based traceability, and CI pipeline discipline.
1 Commits
Oct 1, 2025Month: 2025-10 — Stability-focused update for Cirq in quantumlib/Cirq. This period emphasized improving test reliability and cross-version NumPy compatibility, leveraging targeted test changes and commit-based traceability. Key features delivered: - Stabilized the test suite by removing xfail markers on quantum_shannon_decomposition_test.py to reflect updated NumPy compatibility, enabling reliable test outcomes with newer NumPy versions. Major bugs fixed: - Fixed test fragility caused by NumPy version differences; updated test expectations to align with current NumPy behavior, reducing false negatives and CI noise. This work supports the resolution of Cirq issue #6770 and related tracker #7712. Overall impact and accomplishments: - Improved CI reliability and developer productivity due to fewer flaky tests, faster feedback loops, and safer validation across NumPy versions. This reduces maintenance overhead and increases confidence for downstream users. Technologies/skills demonstrated: - Python testing and test maintenance, NumPy compatibility in a mature codebase, Git-based traceability, and CI pipeline discipline.
October 2025
September 2025
2 Commits • 1 Features
Sep 1, 2025Summary for Sep 2025: Delivered the Idle Moments Gauge Transformer in quantumlib/Cirq to identify idle sequences and insert identity-preserving gauge gates at idle periods. This adds experimental structure around idle moments without altering the logical circuit behavior, enabling gauge-based experimentation and potential decoupling strategies while preserving results. Also fixed a bug in the Idle Moments Gauge by ensuring correct qubit association in single-qubit moment processing, guaranteeing accurate transformations. The work demonstrates capabilities in quantum circuit transformations, gauge-compiler concepts, and careful refactoring/debugging, aligning with business value by enabling experimental optimization and more robust idle-time handling.
September 2025
2 Commits • 1 Features
Sep 1, 2025Summary for Sep 2025: Delivered the Idle Moments Gauge Transformer in quantumlib/Cirq to identify idle sequences and insert identity-preserving gauge gates at idle periods. This adds experimental structure around idle moments without altering the logical circuit behavior, enabling gauge-based experimentation and potential decoupling strategies while preserving results. Also fixed a bug in the Idle Moments Gauge by ensuring correct qubit association in single-qubit moment processing, guaranteeing accurate transformations. The work demonstrates capabilities in quantum circuit transformations, gauge-compiler concepts, and careful refactoring/debugging, aligning with business value by enabling experimental optimization and more robust idle-time handling.
August 2025
1 Commits • 1 Features
Aug 1, 2025In August 2025, delivered a key feature enhancement for randomized benchmarking (RB) fitting in the Cirq component of quantumlib/Cirq, focusing on accounting for RNG-induced variability. The update improves the robustness of RB curves by incorporating the standard deviation of the random number generator into the fitting process, enabling the fit to reflect a range of expected values rather than a single point. This leads to more accurate alignment with experimental data and stronger confidence in benchmarking results across devices and experiments.
1 Commits • 1 Features
Aug 1, 2025In August 2025, delivered a key feature enhancement for randomized benchmarking (RB) fitting in the Cirq component of quantumlib/Cirq, focusing on accounting for RNG-induced variability. The update improves the robustness of RB curves by incorporating the standard deviation of the random number generator into the fitting process, enabling the fit to reflect a range of expected values rather than a single point. This leads to more accurate alignment with experimental data and stronger confidence in benchmarking results across devices and experiments.
August 2025
July 2025
1 Commits • 1 Features
Jul 1, 20252025-07 monthly summary for quantumlib/Cirq: Delivered Randomized Benchmarking sequence optimization for compact circuits by merging historical Clifford gate products into a single PhasedXZGate, reducing circuit size and improving RB generation efficiency. No major bugs fixed this month. Overall impact includes improved benchmarking throughput and reduced resource usage with maintained accuracy. Technologies demonstrated include gate-level circuit design, Python refactoring, and performance optimization.
July 2025
1 Commits • 1 Features
Jul 1, 20252025-07 monthly summary for quantumlib/Cirq: Delivered Randomized Benchmarking sequence optimization for compact circuits by merging historical Clifford gate products into a single PhasedXZGate, reducing circuit size and improving RB generation efficiency. No major bugs fixed this month. Overall impact includes improved benchmarking throughput and reduced resource usage with maintained accuracy. Technologies demonstrated include gate-level circuit design, Python refactoring, and performance optimization.
May 2025
2 Commits • 2 Features
May 1, 2025May 2025 — Quantumlib/Cirq: Two high-impact features delivered to enhance benchmarking performance and data interoperability. No major bugs fixed this month. Business value: increased XEB benchmarking throughput for multi-gate circuits, improved NumPy interoperability for sweep serialization, and strengthened testing and documentation to support reliability and onboarding. Technologies/skills demonstrated: Python, Cirq core, parallelization, qubit mapping, input validation, software testing, and NumPy-aware serialization.
2 Commits • 2 Features
May 1, 2025May 2025 — Quantumlib/Cirq: Two high-impact features delivered to enhance benchmarking performance and data interoperability. No major bugs fixed this month. Business value: increased XEB benchmarking throughput for multi-gate circuits, improved NumPy interoperability for sweep serialization, and strengthened testing and documentation to support reliability and onboarding. Technologies/skills demonstrated: Python, Cirq core, parallelization, qubit mapping, input validation, software testing, and NumPy-aware serialization.
May 2025
April 2025
1 Commits
Apr 1, 2025April 2025 — Quantum circuit tooling (Cirq): Key robustness fix in Cirq resolver with a measurable impact on value resolution accuracy and reproducibility. The focus was a bug fix in the Cirq resolver's symbolic constant handling, replacing the use of the '==' operator with 'is' for SymPy singleton constants (e.g., pi) to improve robustness in the study module. This change reduces edge-case failures and enhances reliability for experiments involving symbolic constants. Commit aa3cc11842aa8f1892c7bbba9380387045dfc6a3.
April 2025
1 Commits
Apr 1, 2025April 2025 — Quantum circuit tooling (Cirq): Key robustness fix in Cirq resolver with a measurable impact on value resolution accuracy and reproducibility. The focus was a bug fix in the Cirq resolver's symbolic constant handling, replacing the use of the '==' operator with 'is' for SymPy singleton constants (e.g., pi) to improve robustness in the study module. This change reduces edge-case failures and enhances reliability for experiments involving symbolic constants. Commit aa3cc11842aa8f1892c7bbba9380387045dfc6a3.
March 2025
2 Commits • 2 Features
Mar 1, 2025Summary for 2025-03: Two key features delivered for Cirq in quantumlib, with accompanying tests and documentation, driving scalability and richer classical control workflows. Implemented dynamic 64-dimension support for linear algebra transformations by removing the hardcoded 32-dimension limit and performing NumPy capability checks; added unit tests including handling of negative dimensions. Introduced BitMaskKeyCondition to enable bitwise operations on measurement results before comparison, enabling more expressive classical control workflows; documentation and integration tests updated accordingly. Major bugs fixed: none reported this month. Overall impact: expands the framework's applicability to larger matrices and more complex control logic, enabling bigger simulations and more flexible experimentation. Technologies/skills demonstrated: Python development, NumPy interoperability, dynamic capability checks, test-driven development, documentation, and cross-team collaboration.
2 Commits • 2 Features
Mar 1, 2025Summary for 2025-03: Two key features delivered for Cirq in quantumlib, with accompanying tests and documentation, driving scalability and richer classical control workflows. Implemented dynamic 64-dimension support for linear algebra transformations by removing the hardcoded 32-dimension limit and performing NumPy capability checks; added unit tests including handling of negative dimensions. Introduced BitMaskKeyCondition to enable bitwise operations on measurement results before comparison, enabling more expressive classical control workflows; documentation and integration tests updated accordingly. Major bugs fixed: none reported this month. Overall impact: expands the framework's applicability to larger matrices and more complex control logic, enabling bigger simulations and more flexible experimentation. Technologies/skills demonstrated: Python development, NumPy interoperability, dynamic capability checks, test-driven development, documentation, and cross-team collaboration.
March 2025
January 2025
1 Commits • 1 Features
Jan 1, 2025January 2025: Focused on aligning the Cirq repository with the current project state by removing obsolete tooling and configurations linked to a removed service, improving maintainability, onboarding, and CI confidence. Delivered a single cleanup commit that removes outdated notebooks and YAML files associated with calibration FAQs and test configurations; this reduces stale references and ensures the repository reflects the live service set.
January 2025
1 Commits • 1 Features
Jan 1, 2025January 2025: Focused on aligning the Cirq repository with the current project state by removing obsolete tooling and configurations linked to a removed service, improving maintainability, onboarding, and CI confidence. Delivered a single cleanup commit that removes outdated notebooks and YAML files associated with calibration FAQs and test configurations; this reduces stale references and ensures the repository reflects the live service set.
December 2024
4 Commits • 3 Features
Dec 1, 2024December 2024 (Month: 2024-12) — Cirq development focused on serialization fidelity, experiment observability, Z-phase calibration, and numerical robustness to strengthen business value and scientific reliability. Key investments targeted unit-aware sweeps, granular operation tagging in XEB, Z-phase cancellation mechanisms, and simulator stability.
4 Commits • 3 Features
Dec 1, 2024December 2024 (Month: 2024-12) — Cirq development focused on serialization fidelity, experiment observability, Z-phase calibration, and numerical robustness to strengthen business value and scientific reliability. Key investments targeted unit-aware sweeps, granular operation tagging in XEB, Z-phase cancellation mechanisms, and simulator stability.
December 2024
November 2024
5 Commits • 3 Features
Nov 1, 2024November 2024 performance summary for quantumlib/Cirq focusing on business value and technical achievements. Delivered robust parameter handling, improved calibration workflows, API enhancements, and strengthened code correctness, enabling more reliable experiments and easier integration with external toolchains. Key features delivered: - Circuit/Program Parameter Serialization Enhancements: added unit-aware parameter serialization (TUnits) and encoding/decoding of parameters with units; extended program protocol to support function-based gate arguments. Commit references: ae6e4e4a501ffa6695af25e1875ecbc42b54a3a6, 30f5b48295eac196918b9e195d47bf8445756276. - Z-Phase Calibration Workflow for Excitation-Preserving Two-Qubit Gates: introduced a new Z-phase calibration workflow with statistical estimates to improve fidelity of excitation-preserving two-qubit gates. Commit: 59be462bb878918091bfbbbce394923266a4999c. - DynamicalDecouplingTag support in Cirq-Google API: added DynamicalDecouplingTag to specify dynamical decoupling protocols; updated protobuf definitions and Python bindings. Commit: d1b0430d27faa75efe933f8dc4bfe1a08b8fed09. - InternalGate Equality Enhancement with Custom Arguments: improved InternalGate value equality to account for custom arguments; added tests to ensure equivalence behavior aligns with argument values. Commit: 326df25fe2257eb7492681f6e04b05eb7ffca617. Major outcomes and impact: - Improved interoperability and automation through unit-aware parameter handling and function-based gate arguments, reducing manual conversions and enabling more complex parameterized experiments. - Higher-fidelity two-qubit gates via the new Z-phase calibration workflow, supported by statistically robust estimates. - Clearer and more expressive API for hardware-aware configurations with DynamicalDecouplingTag, enabling more precise control of decoupling protocols. - Stronger correctness guarantees and test coverage for gate equality with custom arguments, reducing regressions and improving caching/selection logic. Technologies/skills demonstrated: - Python, Cirq codebase enhancements, protobuf bindings, and API design - Parameter serialization, unit handling, and workflow development - Gate calibration workflows, statistical estimation, and test-driven development - Code correctness, test coverage, and regression safety for gate argument handling.
November 2024
5 Commits • 3 Features
Nov 1, 2024November 2024 performance summary for quantumlib/Cirq focusing on business value and technical achievements. Delivered robust parameter handling, improved calibration workflows, API enhancements, and strengthened code correctness, enabling more reliable experiments and easier integration with external toolchains. Key features delivered: - Circuit/Program Parameter Serialization Enhancements: added unit-aware parameter serialization (TUnits) and encoding/decoding of parameters with units; extended program protocol to support function-based gate arguments. Commit references: ae6e4e4a501ffa6695af25e1875ecbc42b54a3a6, 30f5b48295eac196918b9e195d47bf8445756276. - Z-Phase Calibration Workflow for Excitation-Preserving Two-Qubit Gates: introduced a new Z-phase calibration workflow with statistical estimates to improve fidelity of excitation-preserving two-qubit gates. Commit: 59be462bb878918091bfbbbce394923266a4999c. - DynamicalDecouplingTag support in Cirq-Google API: added DynamicalDecouplingTag to specify dynamical decoupling protocols; updated protobuf definitions and Python bindings. Commit: d1b0430d27faa75efe933f8dc4bfe1a08b8fed09. - InternalGate Equality Enhancement with Custom Arguments: improved InternalGate value equality to account for custom arguments; added tests to ensure equivalence behavior aligns with argument values. Commit: 326df25fe2257eb7492681f6e04b05eb7ffca617. Major outcomes and impact: - Improved interoperability and automation through unit-aware parameter handling and function-based gate arguments, reducing manual conversions and enabling more complex parameterized experiments. - Higher-fidelity two-qubit gates via the new Z-phase calibration workflow, supported by statistically robust estimates. - Clearer and more expressive API for hardware-aware configurations with DynamicalDecouplingTag, enabling more precise control of decoupling protocols. - Stronger correctness guarantees and test coverage for gate equality with custom arguments, reducing regressions and improving caching/selection logic. Technologies/skills demonstrated: - Python, Cirq codebase enhancements, protobuf bindings, and API design - Parameter serialization, unit handling, and workflow development - Gate calibration workflows, statistical estimation, and test-driven development - Code correctness, test coverage, and regression safety for gate argument handling.
October 2024
1 Commits • 1 Features
Oct 1, 2024Concise monthly summary for 2024-10 focusing on Cirq repository work.
1 Commits • 1 Features
Oct 1, 2024Concise monthly summary for 2024-10 focusing on Cirq repository work.
October 2024
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Quality Metrics
Correctness95.6%
Maintainability93.4%
Architecture94.0%
Performance88.2%
AI Usage20.8%
Skills & Technologies
Programming Languages
Jupyter NotebookProtocol BuffersPythonYAMLprotobuf
Technical Skills
API DesignAlgorithm ImplementationBenchmarkingCI/CDCircuit DesignCircuit OptimizationCircuit SimulationCircuit TransformationClassical ControlCode CleanupCode RefactoringData AnalysisData SerializationDocumentationDocumentation Management
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