LLM Evaluation

Evaluated by: xiaomi/mimo-v2-flash:free

Last evaluated: March 29, 2026

Prompt Quality

3.0 /5

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Usefulness

3.0 /5

Evaluation error: RetryError[]

Overall Rating

3.0 /5

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Prompt Preview

---
name: pennylane
description: Hardware-agnostic quantum ML framework with automatic differentiation. Use when training quantum circuits via gradients, building hybrid quantum-classical models, or needing device portability across IBM/Google/Rigetti/IonQ. Best for variational algorithms (VQE, QAOA), quantum neural networks, and integration with PyTorch/JAX/TensorFlow. For hardware-specific optimizations use qiskit (IBM) or cirq (Google); for open quantum systems use qutip.
license: Apache-2.0...

Full prompt length: 7397 characters

Tools & Technologies

  • python