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Pipeline run

366b36e1-4602-4fad-b0f1-24d3b0696b78

Pipeline LLM cost (USD)
API 1: $0.0030 API 2: $0.0004 API 3: $0.0000 Total: $0.0034

Client output enrichment

v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description
SPARSE JD sources · ai_index: jd · nature_of_work: jd · tech_stack_maturity: jd
Nature of work · Quantum Algorithms / Optimization
Prototype VQE/QAOA quantum algorithms for combinatorial optimization in Qiskit and Cirq, then benchmark them on simulators and cloud QPUs like IBM Quantum, IonQ, and Quantinuum, using strong linear algebra and numerics.
""prototype VQE / QAOA variants in Qiskit and Cirq, benchmark on simulators and on cloud QPUs""
Tech stack maturity
Mainstream Modern
AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)
0.00 / 5
· Title match
· Has AI skill
· AI skill (primary)
· AI skill (secondary)
· On AI team
· Builds AI products
vocab breakdown (legacy)
Assistants (×1):
Frameworks (×2):
Models / concepts (×3):
Evidence — skills matched in JD (10)
Qiskit Cirq VQE QAOA IBM Quantum IonQ Quantinuum Linear Algebra Numerics Combinatorial Optimization
Skill cluster (1 dimension groups, role-scoped)
Cross-cutting / unaligned
Qiskit Cirq VQE QAOA IBM Quantum IonQ Quantinuum Linear Algebra Numerics Combinatorial Optimization
Show KRA description ↓
We're a small team building near-term quantum algorithms for combinatorial optimization. You'll prototype VQE / QAOA variants in Qiskit and Cirq, benchmark on simulators and on cloud QPUs (IBM Quantum, IonQ, Quantinuum). Strong linear algebra + numerics required. Background in chemistry, physics, or operations research is a big plus. Open to research scientists transitioning to engineering.

Signals

Skill
Alias quantum-computing-engineer
1.00
KRA ai-engineer
0.35

Post-classification

Centroidupdated · n=1
Alias collision log
New-role queue
New skills captured10
New KRA capturedyes

Captured for admin review

Qiskit primary Quantum Computing Engineer pending
Cirq primary Quantum Computing Engineer pending
VQE primary Quantum Computing Engineer pending
QAOA primary Quantum Computing Engineer pending
IBM Quantum primary Quantum Computing Engineer pending
IonQ primary Quantum Computing Engineer pending
Quantinuum primary Quantum Computing Engineer pending
Linear Algebra primary Quantum Computing Engineer pending
Numerics primary Quantum Computing Engineer pending
Combinatorial Optimization primary Quantum Computing Engineer pending
R&R fragment (sim 0.00) Quantum Computing Engineer pending

We're a small team building near-term quantum algorithms for combinatorial optimization. You'll prototype VQE / QAOA variants in Qiskit and Cirq, benchmark on simulators and on cloud QPUs (IBM Quantum…

Status: completed Created: 2026-05-23T23:34:45.566775Z Updated: 2026-05-23T23:34:59.493162Z API 3 duration: 1062 ms
Flow Current 3-step pipeline

1 POST /skills/extract-from-jd

2 POST /skills/extract-details

3 POST /skills/final-role-output

Role Chosen role & resolution

Quantum Computing Engineer

CASE A

slug: quantum-computing-engineer · id: 213 · source: db

Resolution: in_db — role exists in library; skill↔dim and role↔dim links saved when applicable.

0
New skills
0
Skill↔dim saved
0
Role↔dim saved
0
Skipped

Job description

Quantum Algorithms Engineer

We're a small team building near-term quantum algorithms for combinatorial optimization. You'll prototype VQE / QAOA variants in Qiskit and Cirq, benchmark on simulators and on cloud QPUs (IBM Quantum, IonQ, Quantinuum). Strong linear algebra + numerics required. Background in chemistry, physics, or operations research is a big plus. Open to research scientists transitioning to engineering.

Skills from this JD

Each row merges API 1 extraction, API 2 library match / v3 orchestration (dimensions + locked dims), and API 3 persistence tags.

Qiskit Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Quantum Computing Frameworks
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Cirq Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Quantum Computing Frameworks
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
VQE Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Quantum Algorithms
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
QAOA Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Quantum Algorithms
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
IBM Quantum Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Quantum Computing Platforms
Sub-category
general
Skill nature
PLATFORM
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
IonQ Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Quantum Computing Platforms
Sub-category
general
Skill nature
PLATFORM
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Quantinuum Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Quantum Computing Platforms
Sub-category
general
Skill nature
PLATFORM
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Linear Algebra Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Mathematics Concepts
Sub-category
general
Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Version strategy
UNVERSIONED
Numerics Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Mathematics Concepts
Sub-category
general
Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Version strategy
UNVERSIONED
Combinatorial Optimization Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Optimization Concepts
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED

Library artifacts (this run)

Kind Detail DB id
canonical_skill_proposed Qiskit | type=Quantum Computing Frameworks subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Cirq | type=Quantum Computing Frameworks subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed VQE | type=Quantum Algorithms subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed QAOA | type=Quantum Algorithms subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed IBM Quantum | type=Quantum Computing Platforms subtype=general nature=PLATFORM lifespan=MULTI_YEAR
canonical_skill_proposed IonQ | type=Quantum Computing Platforms subtype=general nature=PLATFORM lifespan=MULTI_YEAR
canonical_skill_proposed Quantinuum | type=Quantum Computing Platforms subtype=general nature=PLATFORM lifespan=MULTI_YEAR
canonical_skill_proposed Linear Algebra | type=Mathematics Concepts subtype=general nature=CONCEPT lifespan=EVERGREEN
canonical_skill_proposed Numerics | type=Mathematics Concepts subtype=general nature=CONCEPT lifespan=EVERGREEN
canonical_skill_proposed Combinatorial Optimization | type=Optimization Concepts subtype=general nature=CONCEPT lifespan=MULTI_YEAR
nano JD Parser — gpt-4.1-nano click to toggle
RoleQuantum Algorithms Engineer
DomainOther
JD type pass
Show raw JSON
{
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  "certifications": [],
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    "max": null,
    "min": null,
    "raw": null
  },
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  "roles_and_responsibilities": [
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      "word_count": 56
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  ],
  "urls": []
}
API 1 — extract-from-jd click to toggle
{
  "final_skills": [
    {
      "is_primary": true,
      "skill_name": "Qiskit"
    },
    {
      "is_primary": true,
      "skill_name": "Cirq"
    },
    {
      "is_primary": true,
      "skill_name": "VQE"
    },
    {
      "is_primary": true,
      "skill_name": "QAOA"
    },
    {
      "is_primary": true,
      "skill_name": "IBM Quantum"
    },
    {
      "is_primary": true,
      "skill_name": "IonQ"
    },
    {
      "is_primary": true,
      "skill_name": "Quantinuum"
    },
    {
      "is_primary": true,
      "skill_name": "Linear Algebra"
    },
    {
      "is_primary": true,
      "skill_name": "Numerics"
    },
    {
      "is_primary": true,
      "skill_name": "Combinatorial Optimization"
    }
  ],
  "jd_role": {
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    ],
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  },
  "nano_parsed": {
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    "certifications": [],
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    "experience": {
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      "min": null,
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    },
    "job_locations": [],
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    "role_aliases": [
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    "role_archetype": "Engineering",
    "roles_and_responsibilities": [
      {
        "bullet_count": 0,
        "heading": "Role Overview",
        "heading_was_present": false,
        "source_marker": {
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          "last_5_words": "transitioning to engineering."
        },
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        "word_count": 56
      }
    ],
    "urls": []
  },
  "rejected": false,
  "rejection_reason": null,
  "run_id": "366b36e1-4602-4fad-b0f1-24d3b0696b78",
  "stage3_signals": {
    "alias_found": true,
    "alias_match_roles": [
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        "matched_count": null,
        "matched_skills": null,
        "role_id": 213,
        "score": 1.0,
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        "total_count": null
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    ],
    "kra_match_roles": [
      {
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            "similarity": 0.3989
          },
          {
            "kra_text": "Designs and implements prompt engineering workflows, few-shot examples, chain-of-thought patterns, and structured output parsing for AI feature pipelines.",
            "sentence": "We\u0027re a small team building near-term quantum algorithms for combinatorial optimization.",
            "similarity": 0.3485
          },
          {
            "kra_text": "Designs and implements prompt engineering workflows, few-shot examples, chain-of-thought patterns, and structured output parsing for AI feature pipelines.",
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            "similarity": 0.3129
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 13,
        "score": 0.3534,
        "slug": "ai-engineer",
        "total_count": null
      },
      {
        "display_name": "ML Engineer",
        "kra_matches": [
          {
            "kra_text": "Supports offline experimentation, hyperparameter tuning, and online A/B experiments to improve model quality and investigate production performance issues.",
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            "similarity": 0.3281
          },
          {
            "kra_text": "Translates product requirements into machine learning system specifications including feature definitions, model architecture choices, and success metric definitions.",
            "sentence": "Strong linear algebra + numerics required.",
            "similarity": 0.3072
          },
          {
            "kra_text": "Prepares, cleans, and transforms training datasets, manages feature stores, and builds feature engineering pipelines for model training.",
            "sentence": "Open to research scientists transitioning to engineering.",
            "similarity": 0.2913
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 3,
        "score": 0.3089,
        "slug": "ml-engineer",
        "total_count": null
      },
      {
        "display_name": "Cyber Security Engineer",
        "kra_matches": [
          {
            "kra_text": "Defines secure engineering standards, secure coding guidelines, threat intelligence feeds, and compliance requirements for the organization.",
            "sentence": "Open to research scientists transitioning to engineering.",
            "similarity": 0.3213
          },
          {
            "kra_text": "Performs threat modeling, security architecture reviews, and quantitative risk analysis for new product features and infrastructure changes.",
            "sentence": "You\u0027ll prototype VQE / QAOA variants in Qiskit and Cirq, benchmark on simulators and on cloud QPUs (IBM Quantum, IonQ, Quantinuum).",
            "similarity": 0.3174
          },
          {
            "kra_text": "Performs threat modeling, security architecture reviews, and quantitative risk analysis for new product features and infrastructure changes.",
            "sentence": "We\u0027re a small team building near-term quantum algorithms for combinatorial optimization.",
            "similarity": 0.2705
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 5,
        "score": 0.303,
        "slug": "cybersecurity-engineer",
        "total_count": null
      },
      {
        "display_name": "Fullstack Developer",
        "kra_matches": [
          {
            "kra_text": "Designs and queries relational databases like PostgreSQL and document stores like MongoDB, writing migrations, indexes, and optimized queries.",
            "sentence": "We\u0027re a small team building near-term quantum algorithms for combinatorial optimization.",
            "similarity": 0.3246
          },
          {
            "kra_text": "Designs and queries relational databases like PostgreSQL and document stores like MongoDB, writing migrations, indexes, and optimized queries.",
            "sentence": "Strong linear algebra + numerics required.",
            "similarity": 0.3023
          },
          {
            "kra_text": "Designs and queries relational databases like PostgreSQL and document stores like MongoDB, writing migrations, indexes, and optimized queries.",
            "sentence": "You\u0027ll prototype VQE / QAOA variants in Qiskit and Cirq, benchmark on simulators and on cloud QPUs (IBM Quantum, IonQ, Quantinuum).",
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        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 15,
        "score": 0.3,
        "slug": "full-stack-engineer",
        "total_count": null
      },
      {
        "display_name": "MLOps Engineer",
        "kra_matches": [
          {
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            "similarity": 0.3078
          },
          {
            "kra_text": "Maintains model versioning, experiment lineage, and artifact tracking using MLflow, DVC, or Weights \u0026 Biases for reproducibility and auditability.",
            "sentence": "Open to research scientists transitioning to engineering.",
            "similarity": 0.2952
          },
          {
            "kra_text": "Maintains model versioning, experiment lineage, and artifact tracking using MLflow, DVC, or Weights \u0026 Biases for reproducibility and auditability.",
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            "similarity": 0.2792
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 16,
        "score": 0.2941,
        "slug": "ml-ops-engineer",
        "total_count": null
      }
    ],
    "skill_match_roles": []
  },
  "stage4_decision": {
    "alias_collision_detected": false,
    "case": "A",
    "chosen_role": {
      "display_name": "Quantum Computing Engineer",
      "kra_matches": null,
      "matched_count": null,
      "matched_skills": null,
      "role_id": 213,
      "score": 1.0,
      "slug": "quantum-computing-engineer",
      "total_count": null
    },
    "confidence": 1.0,
    "is_new_role": false,
    "llm2_fired": false,
    "llm2_reasoning": null,
    "matched_dimensions": [],
    "matched_kras": [],
    "matched_skills": [],
    "new_role_display_name": null,
    "new_role_slug": null,
    "queued": false,
    "reasoning": "Exact alias hit on quantum-computing-engineer (1.0) \u2014 no other alias at this confidence; skill_top absent does not contradict",
    "sub_role": null
  },
  "stage5_updates": {
    "centroid_n_after": 1,
    "centroid_updated": true,
    "collision_log_id": null,
    "new_kra_attached": {
      "best_kra_similarity": 0.0,
      "queue_id": 116,
      "r_and_r_preview": "We\u0027re a small team building near-term quantum algorithms for combinatorial optimization. You\u0027ll prototype VQE / QAOA variants in Qiskit and Cirq, benchmark on simulators and on cloud QPUs (IBM Quantum",
      "role_display_name": "Quantum Computing Engineer",
      "role_slug": "quantum-computing-engineer",
      "status": "pending"
    },
    "new_skills_attached": [
      {
        "is_primary": true,
        "queue_id": 3349,
        "role_display_name": "Quantum Computing Engineer",
        "role_slug": "quantum-computing-engineer",
        "skill_name": "Qiskit",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 3350,
        "role_display_name": "Quantum Computing Engineer",
        "role_slug": "quantum-computing-engineer",
        "skill_name": "Cirq",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 3351,
        "role_display_name": "Quantum Computing Engineer",
        "role_slug": "quantum-computing-engineer",
        "skill_name": "VQE",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 3352,
        "role_display_name": "Quantum Computing Engineer",
        "role_slug": "quantum-computing-engineer",
        "skill_name": "QAOA",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 3353,
        "role_display_name": "Quantum Computing Engineer",
        "role_slug": "quantum-computing-engineer",
        "skill_name": "IBM Quantum",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 3354,
        "role_display_name": "Quantum Computing Engineer",
        "role_slug": "quantum-computing-engineer",
        "skill_name": "IonQ",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 3355,
        "role_display_name": "Quantum Computing Engineer",
        "role_slug": "quantum-computing-engineer",
        "skill_name": "Quantinuum",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 3356,
        "role_display_name": "Quantum Computing Engineer",
        "role_slug": "quantum-computing-engineer",
        "skill_name": "Linear Algebra",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 3357,
        "role_display_name": "Quantum Computing Engineer",
        "role_slug": "quantum-computing-engineer",
        "skill_name": "Numerics",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 3358,
        "role_display_name": "Quantum Computing Engineer",
        "role_slug": "quantum-computing-engineer",
        "skill_name": "Combinatorial Optimization",
        "status": "pending"
      }
    ],
    "queue_entry_id": null,
    "v3_pipeline_triggered": false,
    "v3_role_slug": null,
    "v3_run_id": null
  }
}
API 2 — extract-details
{
  "alias_matches": [],
  "candidate_roles": [],
  "chosen_role": {
    "display_name": "Quantum Algorithms Engineer",
    "id": null,
    "rationale": null,
    "role_archetype": "Engineering",
    "slug": "",
    "source": "llm"
  },
  "dimensions": [],
  "input_final_skills": [
    "Qiskit",
    "Cirq",
    "VQE",
    "QAOA",
    "IBM Quantum",
    "IonQ",
    "Quantinuum",
    "Linear Algebra",
    "Numerics",
    "Combinatorial Optimization"
  ],
  "input_llm_skills": [
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    "IBM Quantum",
    "IonQ",
    "Quantinuum",
    "Linear Algebra",
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    "Combinatorial Optimization"
  ],
  "new_aliases_persisted": 0,
  "run_id": "366b36e1-4602-4fad-b0f1-24d3b0696b78",
  "skills_detail": [
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Qiskit",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Quantum Computing Frameworks",
          "skill_nature": "TOOL",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "qiskit",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Cirq",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Quantum Computing Frameworks",
          "skill_nature": "TOOL",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "cirq",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "VQE",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Quantum Algorithms",
          "skill_nature": "CONCEPT",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "vqe",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "QAOA",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Quantum Algorithms",
          "skill_nature": "CONCEPT",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "qaoa",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "IBM Quantum",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Quantum Computing Platforms",
          "skill_nature": "PLATFORM",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "ibm-quantum",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "IonQ",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Quantum Computing Platforms",
          "skill_nature": "PLATFORM",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "ionq",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Quantinuum",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Quantum Computing Platforms",
          "skill_nature": "PLATFORM",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "quantinuum",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Linear Algebra",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Mathematics Concepts",
          "skill_nature": "CONCEPT",
          "sub_category": "general",
          "typical_lifespan": "EVERGREEN",
          "version_strategy": "UNVERSIONED",
          "volatility": "STABLE"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "linear-algebra",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Numerics",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Mathematics Concepts",
          "skill_nature": "CONCEPT",
          "sub_category": "general",
          "typical_lifespan": "EVERGREEN",
          "version_strategy": "UNVERSIONED",
          "volatility": "STABLE"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "numerics",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Combinatorial Optimization",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Optimization Concepts",
          "skill_nature": "CONCEPT",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "combinatorial-optimization",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    }
  ],
  "unmatched_skills": [
    "Qiskit",
    "Cirq",
    "VQE",
    "QAOA",
    "IBM Quantum",
    "IonQ",
    "Quantinuum",
    "Linear Algebra",
    "Numerics",
    "Combinatorial Optimization"
  ]
}
API 3 — final-role-output
{
  "chosen_role": {
    "display_name": "Quantum Computing Engineer",
    "id": 213,
    "rationale": null,
    "role_archetype": "Engineering",
    "slug": "quantum-computing-engineer",
    "source": "db"
  },
  "chosen_role_resolution": "in_db",
  "final_input_skills": [
    {
      "skill": "Qiskit",
      "tag": "new"
    },
    {
      "skill": "Cirq",
      "tag": "new"
    },
    {
      "skill": "VQE",
      "tag": "new"
    },
    {
      "skill": "QAOA",
      "tag": "new"
    },
    {
      "skill": "IBM Quantum",
      "tag": "new"
    },
    {
      "skill": "IonQ",
      "tag": "new"
    },
    {
      "skill": "Quantinuum",
      "tag": "new"
    },
    {
      "skill": "Linear Algebra",
      "tag": "new"
    },
    {
      "skill": "Numerics",
      "tag": "new"
    },
    {
      "skill": "Combinatorial Optimization",
      "tag": "new"
    }
  ],
  "llm_cost_api1_usd": null,
  "llm_cost_api2_usd": null,
  "llm_cost_api3_usd": null,
  "llm_cost_total_usd": null,
  "persistence": {
    "items": [],
    "new_skills_created": 0,
    "role_dimension_saved": 0,
    "skill_dimension_saved": 0,
    "skipped": 0
  },
  "planner_output": null,
  "run_id": "366b36e1-4602-4fad-b0f1-24d3b0696b78"
}

LLM Calls

Every model call made for this run, in pipeline order. Click a card to see the model's response.

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