Practical Quantum Advantage in Finance: A Multi-Method Assessment of Gate-Based Quantum Computing Applications (Repository)

Frozen supplementary archive for the MSc thesis Quantum Computing in Financial Services (Copenhagen Business School, 2026), authored by Aleix Moreño Telesforo and Vincent Wallerich. This deposit accompanies the thesis manuscript submitted via Digital Exam and contains the full audit trail, configurations, prompts, derived artifacts, and reproduction tooling for every claim made in the manuscript. No external archive is required. The archive is organised as a four-phase research pipeline plus shared contracts and verification tooling: P1 — Framework synthesis. Inductive problem-domain × solution-approach taxonomy and codebook over an exploratory paper sample. P2 — Systematic review. PRISMA-conformant SLR producing 777 processed papers (755 active downstream); LLM-assisted classification with documented screening recall and calibration κ. P3 — Thematic synthesis. Eight active finance silos; 501 quantitative-extraction files / 1,046 experiments; quantum-advantage triangulation (4-core + 1-veto rubric) over 936 active rows; per-silo themes (S4), cross-silo synthesis (S5), and finance-framing briefs (S6). P4 — Experiments. Canonical 71-label cohort across 8 silos; 2,556 Phase-8 Microsoft Azure Quantum Resource Estimator records (2,519 OK + 37 documented engine failures) under fault-tolerant cost models. No quantum hardware was used. The archive is frozen as a hand-in artifact: headline numbers, taxonomies, cohorts, and audit ledgers are pinned by FREEZE.md at the root and per-folder FREEZE.md files. All LLM-assisted steps (Azure OpenAI) are disclosed under the Copenhagen Business School GenAI policy; raw call logs from both researchers' production sessions are merged under logs and indexed by AUDIT_INDEX.md. Verification (no LLM credentials, no quantum hardware, no Azure account required): python -m venv .venv .\.venv\Scripts\Activate.ps1 pip install -r requirements-verify.txt pwsh .\verify.ps1 python -m pytest Requires Python 3.12 or 3.13 and PowerShell. See README.md (Examiner Quickstart), REPRODUCIBILITY.md, and ARTIFACT_CLAIM_LEDGER.md (per-claim source files and verification commands). Materials excluded (with rationale in FAIR_USE.md): copyrighted source PDFs of corpus papers (obtain via DOI from paper_id_bridge.csv), reference PDFs of the quantum-advantage frameworks (Rønnow, Beverland, Dalzell, Hoefler, Babbush, Chakrabarti, Stilck França), the thesis manuscript itself (submitted on Digital Exam), and operational caches. License: MIT for code and configurations; see LICENSE. Third-party content retains its original licensing. Keywords: quantum computing, quantum finance, financial services, systematic literature review, PRISMA, thematic synthesis, quantum advantage, resource estimation, Microsoft Azure Quantum Resource Estimator, Qiskit, portfolio optimization, derivative pricing, risk management, quantum machine learning, Monte Carlo simulation, amplitude estimation, HHL, QAOA, VQE, LLM-assisted research, AI use disclosure, reproducibility, MSc thesis, Copenhagen Business School