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  "id": "oai:papers.qnfo.org:coherence-assisted-error-correction-for-deep-learning-a-reconciled-feasibility-a",
  "slug": "coherence-assisted-error-correction-for-deep-learning-a-reconciled-feasibility-a",
  "title": "Coherence-Assisted Error Correction for Deep Learning: A Reconciled Feasibility Analysis of a Hybrid Quantum-Classical Architecture",
  "authors": [
    "Rowan Brad Quni-Gudzinas"
  ],
  "abstract": "Deep learning inference and training are increasingly limited not by raw throughput but by the energy cost of protecting computation against bit-level faults arising from aggressive low-voltage operation, near-threshold computing, or radiation-prone deployment environments. This paper reconciles three independent design studies into a single analytical assessment of a hybrid quantum-classical architecture in which a quantum co-processor supplies coherence-based primitives—syndrome extraction, amplitude-based confidence estimation, and quantum-enhanced sampling—to assist the error-correction layer of a classical deep-learning accelerator, rather than to replace it. We ground the design in the vertical/horizontal taxonomy of hybrid quantum-classical computing and in dataflow frameworks for orchestrating heterogeneous workloads. Our contribution is analytical, with all arithmetic shown: we derive the energy budget of classical error-correction codes (Hamming SEC-DED and BCH(t=2)) at realistic bit-error rates, the coherence-time and shot-count constraints a quantum co-processor must satisfy, and the break-even conditions under which quantum assistance reduces per-inference correction energy. With superconducting qubits at 100 µs coherence and 10 µs round-trip latency, quantum-assisted correction breaks even only at bit-error rates below approximately 8.2 × 10⁻¹² per bit (4.1 × 10⁻¹⁰ under the most favorable sunk-cost accounting)—far below any regime where correction is needed. Under a conservative fault model (per-MAC bit-error rate 10⁻⁵, margin-sensitivity κ = 0.02), the maximum defensible accuracy recovery is 0.2 percentage points, not 20%. We identify the failure modes that would falsify the approach, the parameter regimes that could revive it, and the experiments required to test it. The central finding is negative and robust: coherence-assisted error correction for deep learning cannot currently be justified on energy grounds, though bounded accuracy-recovery and statistical-verification framings remain open.",
  "abstract_markdown": "Deep learning inference and training are increasingly limited not by raw throughput but by the energy cost of protecting computation against bit-level faults arising from aggressive low-voltage operation, near-threshold computing, or radiation-prone deployment environments. This paper reconciles three independent design studies into a single analytical assessment of a hybrid quantum-classical architecture in which a quantum co-processor supplies coherence-based primitives—syndrome extraction, amplitude-based confidence estimation, and quantum-enhanced sampling—to assist the error-correction layer of a classical deep-learning accelerator, rather than to replace it. We ground the design in the vertical/horizontal taxonomy of hybrid quantum-classical computing and in dataflow frameworks for orchestrating heterogeneous workloads. Our contribution is analytical, with all arithmetic shown: we derive the energy budget of classical error-correction codes (Hamming SEC-DED and BCH(t=2)) at realistic bit-error rates, the coherence-time and shot-count constraints a quantum co-processor must satisfy, and the break-even conditions under which quantum assistance reduces per-inference correction energy. With superconducting qubits at 100 µs coherence and 10 µs round-trip latency, quantum-assisted correction breaks even only at bit-error rates below approximately 8.2 × 10⁻¹² per bit (4.1 × 10⁻¹⁰ under the most favorable sunk-cost accounting)—far below any regime where correction is needed. Under a conservative fault model (per-MAC bit-error rate 10⁻⁵, margin-sensitivity κ = 0.02), the maximum defensible accuracy recovery is 0.2 percentage points, not 20%. We identify the failure modes that would falsify the approach, the parameter regimes that could revive it, and the experiments required to test it. The central finding is negative and robust: coherence-assisted error correction for deep learning cannot currently be justified on energy grounds, though bounded accuracy-recovery and statistical-verification framings remain open.",
  "date": "2026-10-03",
  "first_published": "2026-10-03",
  "last_revised": "2026-10-03T00:00:00Z",
  "modified": "2026-10-03T00:00:00Z",
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  "url": "https://papers.qnfo.org/papers/coherence-assisted-error-correction-for-deep-learning-a-reconciled-feasibility-a",
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        "name": "Rowan Brad Quni-Gudzinas",
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        "title": "Coherence-Assisted Error Correction for Deep Learning: A Reconciled Feasibility Analysis of a Hybrid Quantum-Classical Architecture"
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        "description": "Deep learning inference and training are increasingly limited not by raw throughput but by the energy cost of protecting computation against bit-level faults arising from aggressive low-voltage operation, near-threshold computing, or radiation-prone deployment environments. This paper reconciles three independent design studies into a single analytical assessment of a hybrid quantum-classical architecture in which a quantum co-processor supplies coherence-based primitives—syndrome extraction, amplitude-based confidence estimation, and quantum-enhanced sampling—to assist the error-correction layer of a classical deep-learning accelerator, rather than to replace it. We ground the design in the vertical/horizontal taxonomy of hybrid quantum-classical computing and in dataflow frameworks for orchestrating heterogeneous workloads. Our contribution is analytical, with all arithmetic shown: we derive the energy budget of classical error-correction codes (Hamming SEC-DED and BCH(t=2)) at realistic bit-error rates, the coherence-time and shot-count constraints a quantum co-processor must satisfy, and the break-even conditions under which quantum assistance reduces per-inference correction energy. With superconducting qubits at 100 µs coherence and 10 µs round-trip latency, quantum-assisted correction breaks even only at bit-error rates below approximately 8.2 × 10⁻¹² per bit (4.1 × 10⁻¹⁰ under the most favorable sunk-cost accounting)—far below any regime where correction is needed. Under a conservative fault model (per-MAC bit-error rate 10⁻⁵, margin-sensitivity κ = 0.02), the maximum defensible accuracy recovery is 0.2 percentage points, not 20%. We identify the failure modes that would falsify the approach, the parameter regimes that could revive it, and the experiments required to test it. The central finding is negative and robust: coherence-assisted error correction for deep learning cannot currently be justified on energy grounds, though bounded accuracy-recovery and statistical-verification framings remain open.",
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