cirq

Google quantum computing framework. Use when targeting Google Quantum AI hardware, designing noise-aware circuits, or running quantum characterization experiments. Best for Google hardware, noise modeling, and low-level circuit design. For IBM hardware use qiskit; for quantum ML with autodiff use pennylane; for physics simulations use qutip.

1.24x

Quality

81%

Does it follow best practices?

Impact

82%

1.24x

Average score across 3 eval scenarios

Securityby

Passed

No known issues

Evaluation results

90%

Quantum Circuit Noise Analysis

Noisy simulation with noise channels and parameter sweeps

Criteria

Without context

With context

DensityMatrixSimulator used

100%

Depolarizing noise channel

100%

Amplitude damping channel

100%

Noise application method

100%

run_sweep for noise sweep

Descriptive measurement keys

100%

Shallow circuit design

100%

ConstantQubitNoiseModel or custom NoiseModel

100%

Results saved to file

100%

Script produced

100%

Comparison across noise levels

100%

90%

18%

Quantum Circuit Optimization for Hardware Deployment

Circuit optimization with transformer pipeline

Criteria

Without context

With context

merge_single_qubit_gates_to_phxz

33%

100%

drop_negligible_operations

100%

eject_z or eject_phased_paulis

100%

drop_empty_moments

100%

Chained transformations

100%

Original depth reported

100%

Optimized depth reported

100%

Gate count reported

100%

optimize_for_target_gateset used

Results written to file

100%

Script produced

100%

66%

22%

Energy Landscape Mapping for a Variational Quantum Circuit

Variational algorithm with parameterized circuits and COBYLA optimization

Criteria

Without context

With context

sympy.Symbol for parameters

100%

cirq.Linspace sweep

run_sweep for landscape exploration

COBYLA optimizer

100%

scipy.optimize.minimize used

100%

Descriptive measurement keys

simulate() for state vector access

100%

Results written to file

100%

Script produced

100%

Energy landscape data written

100%

cirq.resolve_parameters used

Repository: K-Dense-AI/claude-scientific-skills
Commit: b58ad7e

Evaluated: 27 days ago
Agent: Claude Code
Model: Claude Sonnet 4.6

Table of Contents

Quantum Circuit Noise Analysis Quantum Circuit Optimization for Hardware Deployment Energy Landscape Mapping for a Variational Quantum Circuit

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