Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add personamanagmentlayer/pcl --skill quantum-algorithms-expertgit clone --depth 1 https://github.com/personamanagmentlayer/pclWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/personamanagmentlayer/pcl/quantum-algorithms-expert)<a href="https://agentmods.dev/skills/personamanagmentlayer/pcl/quantum-algorithms-expert"><img src="https://agentmods.dev/badge/skills/personamanagmentlayer/pcl/quantum-algorithms-expert.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00068 | $0.01148 |
| Opus 5 | $0.00034 | $0.00574 |
| Sonnet 5 | $0.00014 | $0.00230 |
| Haiku 4.5 | $0.00007 | $0.00115 |
Grade A, and why
quantum-algorithms-expert scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 187 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Quantum Algorithms Expert
Develop production-ready quantum algorithms for optimization, simulation, and machine learning on near-term quantum devices.
Learning Objectives
- Master quantum algorithm design and implementation
- Build quantum circuits using Qiskit and quantum SDKs
- Implement variational quantum algorithms (VQE, QAOA)
- Apply quantum machine learning techniques
- Optimize quantum circuits for real quantum hardware
Prerequisites
- Strong linear algebra and complex numbers
- Understanding of quantum mechanics basics
- Python programming proficiency
- Knowledge of classical algorithms
Core Concepts
Quantum Bits (Qubits)
Fundamental unit of quantum information existing in superposition of |0⟩ and |1⟩ states. Enables exponential state space growth: n qubits represent 2^n states simultaneously.
Quantum Gates & Circuits
Unitary operations manipulating qubit states. Single-qubit gates (X, Y, Z, H, T) and multi-qubit gates (CNOT, Toffoli) compose quantum circuits implementing algorithms.
Quantum Entanglement
Quantum correlation where measuring one qubit instantly affects entangled qubits. Enables quantum parallelism and forms basis for quantum advantage over classical computing.
Variational Quantum Algorithms
Hybrid quantum-classical algorithms for near-term devices. Classical optimizer tunes quantum circuit parameters to minimize cost function, enabling practical applications despite hardware limitations.
Quantum Advantage
Scenarios where quantum algorithms outperform best known classical algorithms. Examples: Shor's factoring, Grover's search, quantum simulation, certain optimization problems.
Best Practices
Circuit Design
- Minimize circuit depth for NISQ devices
- Use native gate sets of target hardware
- Implement error mitigation techniques
- Reduce two-qubit gate count (main error source)
- Use ancilla qubits efficiently
- Design for specific quantum hardware topology
- Implement circuit optimization passes
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 187 lines · 68 tokens per session scan A 737f7639b608
quantum-algorithms-expert is a skill published in the GitHub repository personamanagmentlayer/pcl (40 stars, last pushed today), licensed Apache-2.0. It adds 68 tokens to every session and 1,148 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-05.
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