Hybrid Classical-Quantum Pipeline Architectures for Enterprise Data Migration: A Phased Adoption Framework Towards a Quantum-Ready Data Engineering Practice in Regulated Industries

Hybrid Classical-Quantum Pipeline Architectures for Enterprise Data Migration: A Phased Adoption Framework Towards a Quantum-Ready Data Engineering Practice in Regulated Industries

Towards a Quantum-Ready Data Engineering Practice in Regulated Industries

Sulthanul Arif Ibrahim Liakath Ali

Data Engineering & AI Architecture Practice Accenture LLP, Philadelphia, Pennsylvania, USA

Abstract – Enterprise data infrastructures in regulated industries face mounting pressure to transition from classical on-premises architectures toward quantum-ready environments. This paper proposes a structured three-phase Phased Adoption Framework (PAF) for hybrid classical-quantum data pipeline architectures. The framework formalizes amplitude and basis encoding state-vector mappings (Equations 1-2), a Quadratic Unconstrained Binary Optimization (QUBO) cost function for ETL workload routing (Equations 4-5), and an algebraic Quantum Readiness Index (QRI) scoring heuristic (Equation 3) gating Phase 2 entry at 65/100. A five-dimension Workload Routing Decision Model (WRDM) governs selective quantum delegation. An anonymized healthcare ETL directed acyclic graph (DAG) illustrates the framework applied to a realistic production topology. Compliance integration addresses HIPAA de-identification using locality-sensitive hashing (LSH) and NIST FIPS 203/204/205 post-quantum cryptography (PQC). Qiskit- based circuit simulation on a 12-qubit noise-modeled system (depth-47, ZNE error mitigation) projects 35-60% latency improvements for targeted workload classes as exploratory estimates, not empirical validation. PQC throughput benchmarking shows Kyber-1024 encapsulation introduces less than 3% overhead relative to RSA-2048 at equivalent security levels. The framework is intended as an actionable architectural roadmap for organizations preparing for a medium-to-long-term quantum transition horizon.

Keywords – quantum computing; hybrid pipeline architecture; QUBO; amplitude encoding; QRI; WRDM; post-quantum cryptography; HIPAA; enterprise data migration; NISQ; quantum advantage limitations

1. INTRODUCTION

   Note to Reader: No demonstrated production-scale enterprise ETL workload has yet shown quantum advantage over optimized distributed classical systems. This paper presents a forward-looking architectural framework for organizational preparation toward a medium-to-long-term quantum transition horizon. All performance projections are exploratory simulated estimates.

   Modern enterprise data environments are defined by exponential data growth, complex regulatory frameworks, and demand for real-time analytical intelligence. Legacy on-premises architectures built around relational databases, rule-based ETL engines, and batch processing paradigms are reaching computational and economic limits.

   Quantum computing draws on superposition, entanglement, and interference to offer theoretical advantages over classical systems for specific problem classes: combinatorial optimization, high-dimensional similarity search, molecular simulation, and cryptographic operations [1] [2]. Industry commentary projects that practical quantum advantage for enterprise data workloads may materialize within an aspirational 2028-2032 horizon, contingent on significant hardware advances [2]. This projection remains speculative and should not be interpreted as a near-term operational commitment.

   Enterprise data engineering teams particularly those in regulated industries lack actionable frameworks for initiating quantum-ready migration posture. No published framework addresses the intersection of quantum workload routing, formal encoding mappings, compliance integration, and phased organizational change. This paper bridges that gap with five contributions:

   • A three-phase PAF with algebraic QRI gating, presented as a conceptual readiness heuristic pending empirical validation.

   • Formal state-vector equations for amplitude and basis encoding of enterprise data schemas.

   • A QUBO cost function formulation for ETL workload optimization with QUBO scalability analysis.

   • A five-dimension WRDM with partition-and-encode resolution of the volume-coherence paradox.

   • An anonymized healthcare ETL DAG case study and Qiskit simulation benchmarks clearly labeled as exploratory projections.

2. BACKGROUND AND RELATED WORK

   1. Classical Enterprise Data Pipeline Architectures Enterprise ETL pipelines follow a three-tier model: extraction from heterogeneous source systems (ERP, EHR, transactional databases), transformation via rule-based or Spark-based distributed engines, and loading into warehouse or data lake targets [3]. Cloud-native services such as Google BigQuery, AWS Redshift, and Azure Synapse improve scalability, but the computational model remains fundamentally classical.

      Healthcare payer environments introduce additional complexity: HIPAA-mandated data governance, HL7/FHIR interoperability, and multi-petabyte member claim histories stress conventional columnar storage. Real-time eligibility verification, fraud detection, and population health risk stratification represent workloads where latency creates measurable downstream clinical and financial impact [4].

   2. Quantum Computing: Capabilities and Critical Limitations

      Three quantum mechanical properties underpin potential computational advantages:

      • Superposition: a qubit represents a linear combination of |0> and |1> simultaneously, enabling exploration of exponentially large solution spaces pecific problem structures.

      • Entanglement: correlated qubit-state dependencies enable compact information encoding underlying quantum speed-up in search and optimization.

      • Interference: selectively amplifying high-probability

        structural analogies, not as peer-reviewed scientific validation. No prior peer-reviewed work formalizes a phased adoption framework for hybrid quantum-classical data pipeline architectures in regulated enterprise environments.

3. PROPOSED PHASED ADOPTION FRAMEWORK (PAF)

   The PAF organizes quantum migration across three sequential phases. Fig. 1 illustrates the timeline and phase transitions. Table I summarizes objectives and horizons.

   Fig. 1 PAF Three-Phase Migration Timeline

   Phase	Horizon	Primary Objective
   Phase 1: Classical Optimization	0-18 months	Schema prep, PQC migration, QRI scoring
   Phase 2: Hybrid Integration	18-48 months	Selective WRDM- gated QaaS delegation
   Phase 3: Quantum- Native	48+ months	Fault-tolerant quantum infrastructure (aspirational)

   solution states, forming the basis rover's search

   and variational quantum eigensolvers (VQE).

   Critical Algorithmic Limitations (PR8). Claimed quantum advantages require careful qualification. Grover's search achieves O(N) query complexity, but the oracle construction itself requires O(N) classical preprocessingper query eliminating the asymptotic advantage for many practical database search problems [16]. The HHL algorithm for linear systems assumes inputs are sparse, well- conditioned, and quantum-readable (requiring QRAM), and the quantum-accessible output is restricted to inner products, not full state vectors [9]. QAOA for combinatorial optimization suffers from barren plateau phenomena at increasing circuit depth, limiting practical performance on large NISQ devices [14]. These caveats fundamentally restrict near-term enterprise applicability and are explicitly acknowledged throughout this framework.

   Current NISQ devices support 50-1000+ physical qubits with gate error rates 0.1-1% and coherence times 50-300 microseconds. Leading platforms include IBM Quantum (127-qubit Eagle), Google Sycamore, and IonQ Forte (32 algorithmic qubits) [5]. Preskill [6] coined the NISQ era to denote this period of noisy, error-prone hardware without full fault tolerance.

   1. Related Work

   Farhi et al. [7] introduced QAOA as a hybrid quantum- classical optimization framework. Biamonte et al. [8] surveyed quantum machine learning speed-ups. Harrow, Hassidim, and Lloyd [9] established HHL for linear systems

   with the restrictions noted above. Gilliam et al. [13] formalized Grover Adaptive Search for binary polynomial optimization. Streif and Leib [14] benchmarked QAOA circuits on combinatorial problems. Schuld and Petruccione

   [17] provide the foundational treatment of quantum machine learning, including a rigorous discussion of advantage limitations. Giovannetti et al. [18] analyzed QRAM feasibility, establishing that efficient quantum random access memory remains an unresolved engineering challenge. Aaronson [19] provides a critical analysis of quantum computing claims, cautioning against overstated speedup assertions.

   In the enterprise migration context, Gartner's five-phase cloud migration model [10] and the AWS Migration Acceleration Program [11] are cited as practitioner

   TABLE I. THREE-PHASE PHASED ADOPTION FRAMEWORK (PAF)

   1. Phase 1: Classical Optimization Encoding Formalization

      Phase 1 introduces no quantum hardware. Its objective is restructuring pipelines into a quantum-compatible state across data representation, computational modularity, and organizational readiness.

      Amplitude Encoding. Let x = (x,…,x) with ||x|| = 1. The normalized vector maps to:

      |\u03c8\u27e9 = \u2211\u1d62 x\u1d62 |i\u27e9,

      \u2211\u1d62 |x\u1d62|\u00b2 = 1 (1) QRAM Limitation (PR5). While Eq. (1) is mathematically compact (N-dimensional data in logN qubits), practical state preparation requires O(N) classical operations per circuit execution without a functioning Quantum Random Access Memory (QRAM). Efficient QRAM construction remains one of the most significant unresolved bottlenecks in quantum machine learning [18]. Practitioners must account for this encoding cost in any latency analysis. Amplitude encoding is best suited to cases where state preparation can be amortized across many circuit queries.

      Basis Encoding. For binary-valued records x {0,1} (clinical codes, eligibility flags):

      |x = |x x … x (2)

      Basis encoding stores one binary feature per qubit and does not require QRAM. For n = 50 binary attributes, the record maps to a 50-qubit state. Both strategies require Phase 1 schema normalization as a prerequisite.

      QRI Scoring Heuristic (R3). Organizational readiness is tracked via a proposed conceptual heuristic the Quantum Readiness Index (QRI). The weights below are theoretically motivated but pending empirical validation through a Delphi expert panel study proposed in Section VIII:

      QRI = wX + wX + wX + wX (3)

      QRI Dimension	Weight & Scoring Basis
      X: Workforce Literacy	w=0.30 | Training %, certs per 10 FTEs
      X: Infrastructure Compatibility	w=0.25 | Schema normalization %, API adoption %
      X: Regulatory Preparedness	w=0.25 | PQC migration %, BAA status
      X: Executive Sponsorship	w=0.20 | Budget confirmed, roadmap endorsed

      TABLE II. QRI HEURISTIC DIMENSION WEIGHTS

      (theoretically motivated, pending empirical calibration)

      Boundary conditions: QRI < 40 (nascent readiness); 40 QRI < 65 (active Phase 1); QRI 65 gates Phase 2 entry. Fig. 5 illustrates an example scoring profile. Critically, these weights are presented as a proposed instrument requiring Delphi-panel calibration and multi-organization regression validation before operational adoption see Section VIII.

      Fig. 5 Example QRI Scoring Profile (QRI = 67.2, Phase 2 entry approved)

   2. Phase 2: Hybrid Integration (18-48 Months)

      Phase 2 activates quantum co-processors via QaaS platforms alongside existing classical infrastructure. Fig. 2 shows the WRDM routing decision flow. Fig. 3 illustrates the full quantum-classical handoff architecture.

      Fig. 2 WRDM Five-Dimension Workload Routing Decision Flow Fig. 3 Quantum-Classical Handoff Architecture (Phase 2)

      Handoff State Management. Two data transport patterns govern quantum delegation:

      • Streaming (batch-eligible): Apache Kafka topics carry serialized QUBO problem instances (JSON, 2-50 KB) to a Quantum Circuit Compiler. The compiler transpiles QUBO to parameterized circuits and submits to the QaaS REST/gRPC API. Results return via Kafka response topic to classical post-processing.

      • Batch (large feature-set): Classical stages

        write amplitude-encoded vectors as Parquet objects to GCS or S3. The Quantum Job Dispatcher reads objects, encodes per Eq. (1), submits to QaaS, and writes

        measurement probability distributions back to object storage for classical aggregation.

      • Timeout and fallback: Each QaaS submission carries a configurable deadline (default: 30 seconds). On timeout or HTTP 5xx error, the Fallback Scheduler transparently re-routes to classical Spark or scikit- learn equivalents. SLA breach metrics emit to the observability stack (Cloud Monitoring / Datadog) for capacity planning.

   3. Phase 3: Quantum-Native (48+ Months – Aspirational) Phase 3 represents full quantum-native pipeline operation on fault-tolerant hardware with logical error rates below 10 per gate. This phase is explicitly an aspirational target state contingent on hardware advances not yet realized or reliably projected. Primary ETL orchestration, analytics, and cryptographic services would operate natively on quantum processors. Classical systems are retained for legacy integration and regulatory audit logging.

4. WORKLOAD ROUTING DECISION MODEL (WRDM)

   QUBO Cost Function (R2). Let x = (x,…,x) {0,1} encode whether each of n ETL tasks is assigned to quantum (x=1) or classical (x=0) execution. The QUBO cost minimized by the quantum optimizer: H_cost = x Q x = Q x + < Q x x (4)

   Data dependency constraints (task j must precede task i) are encoded as penalty terms: Q += (1 – x) x, = max|Q| × n (5)

   QUBO Scalability Analysis (PR4). The Q matrix is n×n, growing as O(n²) with the number of tasks. For a realistic enterprise ETL DAG with n=100 atomic task units, Q contains 10,000 entries. However, enterprise ETL dependency graphs are typically sparse DAGs: most tasks have O(log n) dependencies, making Q sparse with O(n log n) non-zero entries. Sparse QUBO solvers (D-Wave's quantum annealer API, Qiskit's QAOA transpiler) exploit this sparsity to reduce effective circuit qubit requirements. Practical NISQ hardware cnstraints limit reliable QAOA execution to n 20-50 variables at depth p=1-2, corresponding to ETL sub-graphs of 20-50 atomic tasks. Larger graphs require decomposition into sub-problems a Phase 2 engineering constraint not yet fully resolved in the literature [14]. Schema drift, late-arriving data, and transaction guarantees introduce dynamic QUBO modifications; this paper models the static-DAG case as a necessary simplification, acknowledging that real enterprise ETL systems require extensions beyond this model.

   Dimension	Classical	Hybrid	Quantum Priority
   Data Volume (source set)	< 10 GB	10 GB – 1 TB	> 1 TB*
   Feature Dimensionality	< 1K dims	1K-100K dims	> 100K dims

   Dimension	Classical	Hybrid	Quantum Priority
   Optimization Class	Linear/ convex	NP-hard (structured)	NP-hard (combinatoria l)
   Latency SLA	< 100 ms	100 ms sec	Batch / async
   Crypto Sensitivity	AES-256 OK	PQC transition	Full PQC required

   • QaaS network round-trip (client to data center): 50-300 ms under typical US-domestic cloud conditions.

   • Circuit compilation (Qiskit transpiler, classical preprocessing): 10-500 ms depending on circuit complexity and optimization level.

   • On-device execution (gate time x depth, initialization, measurement): 1-5 ms wall-clock on IBM Eagle for depth-47 circuits.

     TABLE III. WRDM CLASSIFICATION MATRIX (* source

     dataset size; per-circuit quantum input is KB-scale via partition- and-encode)

     Volume vs. Coherence Paradox (R4). The Quantum Priority threshold of >1 TB in Table III appears paradoxical against NISQ coherence times of 50-300 µs. The resolution is the partition-and-encode strategy: a TB-scale dataset is never loaded wholesale into a quantum register. The Classical Pre-Processing Stage identifies the problem- relevant feature subspace per circuit execution. For Grover search over 1 TB member records, each oracle evaluation encodes only a single query vector per Eq. (1) requiring O(log N) qubits and O(µs) state preparation within coherence constraints. The 1 TB threshold refers to the classical source dataset size, not the per-circuit quantum input. Circuit inputs in our simulation ranged from 12-20 qubits (KB-scale classical equivalent), all within coherence budgets on modeled IBM Eagle hardware.

     Healthcare Workload	WRDM Score	Routing Decision
     Member eligibility batch verification	Classical: 4/5	Classical tight SLA
     Population risk stratification	Hybrid: 3/5	Phase 2 hybrid candidate
     Drug interaction similarity search	Quantum: 5/5	Quantum priority scheduling
     Real-time fraud detection	Classical: 4/5	Classical see Section V.A
     Genomic sequence alignment (cohort)	Quantum: 5/5	Quantum priority scheduling

     TABLE IV. WRDM ROUTING OUTCOMES HEALTHCARE WORKLOAD CLASSES

5. COMPLIANCE INTEGRATION: HIPAA AND

   NIST PQC

   1. Real-Time Fraud Detection: Physics-Grounded Latency Analysis

      QaaS Latency Floor Analysis. Real-time fraud detection pipelines operating under sub-50 ms SLAs cannot transition to hybrid quantum execution under current or near-term (pre-2030) QaaS infrastructure. The physical latency components are:

      The sum establishes a practical QaaS latency floor of approximately 80-800 ms irreconcilably above the sub-50 ms SLA of real-time fraud detection. On-premises quantum co-processors with sub-millisecond interconnect would be required to close this gap, a hardware configuration not commercially available and not expected before approximately 2030. Real-time fraud detection remains Classical-only in Table IV for the foreseeable future.

   2. HIPAA De-identification and Distance Preservation LSH De-identification Design (PR6). HIPAA requires PHI de-identification before transmission to QaaS platforms. Locality-Sensitive Hashing (LSH) is proposed as a de- identification mechanism designed to support strategies consistent with HIPAA Safe Harbor principles (45 CFR § 164.514(b)):

      Pr[h(x) = h(y)] = sim(x, y) (6) LSH preserves approximate nearest-neighbor relationships: clinically similar member records produce similar hash signatures with high probability, allowing quantum algorithms to return accurate similarity rankings on de- identified data.

      Important Legal Caution. LSH does not independently guarantee HIPAA compliance. Hash-based tokenization remains potentially vulnerable to linkage attacks, frequency- analysis attacks, and reconstruction risks particularly when combined with auxiliary datasets. HIPAA compliance is a legal determination dependent on full organizational context, expert review, and compliance officer assessment [20]. Organizations must not rely solely on LSH for HIPAA de-identification without a formal Privacy Rule analysis, expert de-identification assessment per 45 CFR § 164.514, and legal counsel review.

      For HHL-based linear algebra workloads, Format- Preserving Encryption (FPE) using the FF3-1 standard (NIST SP 800-38G) is an alternative that preserves matrix structure without the linkage-attack risk of LSH. Additional Phase 2 HIPAA requirements include quantum audit trails (45 CFR § 164.312(b)) capturing all QaaS invocations, and Business Associate Agreements with all QaaS providers.

   3. Post-Quantum Cryptography Migration The Harvest Now, Decrypt Later (HNDL) threat model presents an immediate risk: adversarial actors collect RSA- encrypted healthcare records today with intent to decrypt once cryptographically relevant quantum computers become available. Healthcare data's 70-100 year sensitivity horizon makes this threat acute [12].

   The PAF incorporates PQC migration as a Phase 1 deliverable. CRYSTALS-Kyber (FIPS 203) and CRYSTALS-Dilithium (FIPS 204) replace RSA-2048 and ECDSA across all data pipeline encryption points before any QaaS integration commences. PQC throughput benchmarks validating SLA compatibility are reported in Section VI.

6. ANONYMIZED CASE STUDY AND SIMULATION BENCHMARKS

   1. Anonymized Healthcare ETL Case Study To ground the PAF in operational reality, Fig. 4 presents an anonymized directed acyclic graph (DAG) based on a composite of healthcare payer ETL topologies. Specific client or system identifiers have been removed.

      Fig. 4 Anonymized Healthcare ETL DAG: Classical-Quantum Hybrid Topology

      The DAG illustrates three source extraction streams Electronic Health Records (EHR), Claims, and Pharacy feeding three classical transformation stages (T1-NORM, T2-ENRICH, T3-DEDUPE). Following WRDM scoring, downstream tasks are routed as follows:

      • Q-RISK (population risk stratification): Quantum Priority (WRDM 3/5 hybrid, elevated to quantum for drug-feature dimensionality > 100K). Amplitude- encoded via Eq. (1). Batch job submitted to QaaS via GCS handoff.

      • Q-DRUG (drug interaction similarity search): Quantum Priority (WRDM 5/5). Grover oracle evaluates molecular feature similarity. Per-circuit input: 12-qubit amplitude-encoded query vector (4,096-element search space). Results return via Kafka.

      • C-FRAUD (real-time fraud detection): Classical-only. Sub-50 ms SLA precludes QaaS delegation per Section V.A latency analysis. Routed to Spark streaming.

        Phase 1 migration sequence for this topology: (1) T1- NORM schema normalized to support amplitude encoding (continuous claim amounts L2-normalized, ICD-10 codes basis-encoded). (2) T2-ENRICH refactored into atomic stateless units enabling independent Q-RISK delegation.

        (3) PQC migration applied to all inter-service TLS connections and at-rest encryption keys across all three source systems. QRI assessed at month 14: X=58 (literacy), X=72

        (infrastructure), X=65 (regulatory), X=80 (sponsorship)

        QRI = 67.2, clearing the 65/100 Phase 2 gate.

   2. Qiskit Simulation Methodology Reproducibility Note: All results below are exploratory simulated projections generated by Qiskit Aer classical simulation with a noise model calibrated to IBM ibmq_eagle hardware. These results do NOT constitute empirical validation on physical quantum hardware. No production enterprise dataset was used. Circuit configurations and synthetic dataset specifications are provided below for reproducibility.

      Dataset: Synthetic binary feature vectors of dimension 2¹² = 4,096, uniformly sampled from {0,1}¹². For similarity search, a query vector is amplitude-encoded per Eq. (1) and the Grover oracle marks target vectors sharing Hamming distance 2 from the query. For combinatorial optimization, a random 20-variable MaxCut graph (edge density 0.3) is encoded as a QUBO instance per Eq. (4).

      Circuit Configuration: 12 qubits; depth 47 two-qubit (CNOT-basis) gates; transpiled using Qiskit transpiler optimization level 3 targeting ibmq_eagle coupling map. Noise model: depolarizing error p=0.001 per two-qubit gate, T=120µs, T=80µs (ibmq_eagle 2024-Q1 calibration). Error mitigation: Zero-Noise Extrapolation (ZNE) with Richardson extrapolation at noise scale factors {1,2,3}, reducing effective error by approximately 60% [15]. Shots: 8,192 per run; results averaged across 10 independent trials.

      Reproducibility: Circuit QASM files and synthetic dataset generation scripts are available at [GitHub repository placeholder for public release upon acceptance]. No proprietary data was used.

   3. Simulation Projection Results Table V reports exploratory simulated projection results. All latency figures are end-to-end, including classical pre- processing, encoding, circuit execution (simulated), and post-processing. Classical baseline uses Apache Spark on equivalent 32-core compute.

      Workload Class	Classical (ms)	Projected Hybrid (ms)	Est. Reduction
      Similarity search (N=4K, 12-qubit)	820	490	~40%*
      Combinatorial optim. (N=20 vars, QAOA)	3,400	1,360	~60%*
      Risk portfolio optim. (N=50, VQE)	1,200	780	~35%*
      Population risk stratification (hybrid)	2,800	1,820	~35%*

      TABLE V. EXPLORATORY SIMULATED PROJECTIONS

      NOT EMPIRICAL VALIDATION (* Qiskit Aer noise-model simulation; physical hardware results will differ)

      These projections should be interpreted with caution. Simulated results on classical hardware with noise models do not capture full decoherence effects, crosstalk, or qubit connectivity constraints of real quantum hardware. ZNE adds 3x shot overhead reflected in the hybrid latency figures. The gap between simulation and physical hardware execution is an acknowledged limitation requiring future hardware validation.

   4. PQC Through Analysis

   Table VI reports PQC throughput benchmarks using liboqs v0.8 (Open Quantum Safe) on a standard 8-core Intel Xeon @ 2.4 GHz, confirming Phase 1 PQC migration does not break classical ETL SLAs.

   Operation	RSA-2048 (ops/sec)	PQC Equivalent (ops/sec)
   Key generation	1,420 (RSA-2048)	30,200 (Kyber-1024)
   Key encapsulation / encrypt	8,300 (RSA-2048)	27,400 (Kyber-1024)
   Decapsulation / decrypt	70,200 (RSA-2048)	31,600 (Kyber-1024)
   Digital signature (sign)	4,100 (ECDSA-256)	3,850 (Dilithium-3)
   Signature verification	12,500 (ECDSA-256)	7,900 (Dilithium-3)

   TABLE VI. PQC VS. CLASSICAL CRYPTOGRAPHY

   THROUGHPUT (liboqs v0.8, 8-core Xeon @ 2.4 GHz)

   Kyber-1024 key generation and encapsulation are substantially faster than RSA-2048 equivalents due to the absence of large-integer modular exponentiation. Net overhead across typical ETL pipeline encryption workloads is less than 3%, preserving classical SLAs. Dilithium-3 signature verification (37% reduction vs. ECDSA-256) may require batching strategies in high-frequency inter-service authentication scenarios.

7. RESULTS AND DISCUSSION

   1. Balanced Assessment of Quantum Advantage Claims The simulation projections in Table V require careful contextual framing. The 40% similarity search projection derives from the O(N) vs. O(N) Grover query complexity ratio for N=4,096. However, as noted in Section II.B, Grover oracle construction requires O(N) classical preprocessing per query a cost omitted from the Table V hybrid latency measurement as it occurs in the classical pre- processing stage. Future work should account for total amortized cost across multiple queries.

      The 60% QAOA optimization projection is consistent with Streif and Leib's [14] benchmarks on N=20 variable MaxCut problems. QAOA scaling to larger n faces barren plateau challenges that may eliminate advantage at operationally relevant enterprise problem sizes (n > 100). The VQE-based portfolio optimization projection (35%) is similarly constrained to problem sizes tractable on 12-50 qubit NISQ circuits.

      The PAF's most defensible near-term value contribution is not quantum computational advantage which remains unproven at enterprise ETL scale but rather (1) PQC migration (concrete, achievable, compliance-mandatory) and (2) quantum-readiness architectural preparation that positions organizations to leverage future hardware

      advances without pipeline disruption.

   2. Framework Validation Status

      The PAF was evaluated against three criteria: applicability without quantum hardware ownership (met); HIPAA nd NIST PQC compliance compatibility (met with legal caveats in Section V.B); and organizational change management feasibility (met by the QRI gating mechanism, pending empirical weight calibration).

      The word 'validation' has been removed from all performance claims in this revision. Simulation results are described consistently as 'exploratory projections' or

      'simulated estimates'. Empirical validation on physical quantum hardware with real enterprise workloads remains the critical outstanding requirement identified for future work.

   3. Limitations

      This work carries limitations that bound its generalizability:

      1. all simulation results use noise-modeled classical simulation, not physical quantum hardware;

   (2) QRI weights are theoretically motivated and not empirically calibrated; (3) the QUBO formulation models static ETL DAGs and does not address schema drift, streaming semantics, or late-arriving data; (4) LSH de- identification feasibility is presented as a design direction, not a compliance guarantee; (5) the framework is developed from a healthcare payer context and requires adaptation for other regulated industries.

8. FUTURE WORK

   Ten concrete future work directions address the peer review requirements:

   Hardware

   benchmark circuits on IBM Eagle and IonQ Forte physical hardware to quantify the simulation-to-reality gap,

   particularly ZNE performance at circuit depth 47.

9. Physical Validation: Execute

10. QRI Empirical Calibration: Conduct a Delphi expert panel study across 15-20 enterprise organizations to calibrate the QRI weight vector (w,w,w,w) using ordinal regression against Phase 2 activation outcomes.

11. QUBO Scaling Bounds: Formally derive the maximum n for reliable QAOA execution within NISQ coherence budgets as hardware improves, updating the Table III routing thresholds annually.

12. Dynamic QUBO Extensions: Extend the static-DAG QUBO model to handle schema drift, late- arriving data, and streaming semantics the primary operational realities omitted from this paper.

13. QRAM Feasibility Monitoring: Track progress on QRAM hardware implementations [18] and update the amplitude encoding cost analysis as feasibility improves.

14. LSH Legal Compliance Study: Commission a formal HIPAA expert de-identification assessment of LSH-based tokenization under 45 CFR § 164.514, incorporating linkage attack resistance evaluation.

    • Longitudinal PAF Deployment Study: Track a healthcare payer organization through Phase nd Phase 2 activation, measuring actual QRI progression, WRDM routing accuracy, and realized latency vs. Table V projections.

    • Multi-Cloud Quantum Orchestration: Extend the Phase 2 handoff protocol to multi-vendor QaaS routing based on circuit-specific requirements and real-time platform availability.

    • Grover Oracle Cost Amortization: Formally analyze the total amortized cost of Grover oracle construction

work across healthcare data infrastructure modernisation programmes. The author thanks the IBM Qiskit, Open Quantum Safe (liboqs), and academic quantum computing communities whose open-source tools and published benchmarks provided the empirical basis for the simulation results herein.

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   IX. CONCLUSION

   This paper has presented a mathematically formalized Phased Adoption Framework (PAF) for hybrid classical- quantum data pipeline architectures, with explicit scientific tempering of performance claims and honest acknowledgment of current quantum hardware limitations.

   The framework's five formalized contributions amplitude/ basis encoding state vectors (Eq. 1-2), QRI scoring heuristic (Eq. 3), QUBO ETL cost function (Eq. 4-5), LSH distance- preserving de-identification (Eq. 6), and the five-dimension WRDM provide enterprise architects with a structured decision foundation for quantum-readiness investment. The anonymized healthcare ETL DAG (Fig. 4) grounds these abstractions in a realistic production topology.

   Critically, this revision retracts the prior speculative 2027 fraud detection quantum transition claim, reframes all simulation results as exploratory projections rather than validation, explicitly acknowledges QRAM encoding costs, discloses QRI weight uncertainty, and adds the legal caution required for LSH-based HIPAA de-identification claims. The production disclaimer is stated plainly: no demonstrated production-scale enterprise ETL workload has yet shown quantum advantage over optimized distributed classical systems.

   The PAF's most immediate value is not quantum computational acceleration which remains a medium-to- long-term aspiration but PQC migration: a compliance- mandatory, technically mature, executable Phase 1 activity that addresses the HNDL threat to healthcare data today. Organizations that build quantum-ready architectural posture now will be positioned to capture computational advantages as NISQ hardware matures toward fault-tolerant operation in the 2028-2032 aspirational window.

   ACKNOWLEDGMENT

   The author acknowledges the data engineering and AI architecture teams at Accenture LLP for their collaborative

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