For quantum voice security, we offer an integrated functional system that (i) encapsulates threat models for voice biometrics within the quantum world, (ii) integrates PQC (Post-Quantum Cryptographic) foundations with quantum resilient detection, and (iii) provides prescriptive choices for parameters upon deployment with experimentally confirmed metrics. We model attacks as resource bounded CPTP (Completely Positive Trace Preserving) maps and Lindbladian flows, while we model voice recordings as hybrid quantum-classical objects within composite CV (Continuous Variable) and qubit Hilbert spaces. These three high-level categories of quantum threats are framed by such formalism: quantum generative spoofing (measured by TD (Trace Distance) of RQGAN (Relativistic Quantum Generative Adversarial Network)/QGAN (Quantum GAN) synthesis), quantum amplified adversarial perturbations (e.g., Groover accelerated search and entangled operator perturbations within Fock space), and retrospective cryptanalytic compromises (e.g., Shor enabled store now, decrypt later). To defend against such threats, we use a variety of lattice templates supported protection (RLWE (Ring Learning With Errors)/MLWE (Module LWE) with min-entropy and TTC (Time-To-Compromise) constraints met explicitly within choice of parameters), CV-QKD (CV–Quantum Key Distribution) if possible, randomized keyed encodings with gauge equivariant quantum QEC (Error Correcting Codes), and quantum resilient detection approaches (Wigner negativity witnesses, QCNN (Quantum Convolutional Neural Network) classifiers, and QFI (Quantum Fisher Information) detectability tests). Operational measures relating privacy leakage as well as detection sensitivity are the PDR (Purity Displacement Ratio), QSDI (Quantum Signal-to-Distortion Index), and QBFI (Quantum Biometric Fidelity Index). We also obtain security parameter and sample complexity bounds that translate immediately to code distance, encoder, and key size decisions. RLWE template secrecy with decryption failure probabilities experimental results on LibriSpeech, ASVspoof, and synth data confirm \(<10^{-6}\) under modeled attacks by noise as well as strong detection of quantum synthesised spoofs (deepfake detection \(>96\%\), spoof suppression from 0.89 to 0.12 success) are achievable. Our technique provides insightful, empirical guideline direction toward voice authentication system development that is secure against existing as well as potential quantum threats (Note: abbreviations used over the course of study are tabulated in Appendix I).
