Error Exponents of Probabilistic Quantum Resource Distillation

• URL: http://arxiv.org/abs/2609.06688v1
• Date: Sun, 06 Sep 2026 15:56:59 GMT
• Title: Error Exponents of Probabilistic Quantum Resource Distillation
• Abstract summary: We analyze the conditional error exponents of probabilistic resource distillation under approximately resource-nongenerating instruments.<n>For generic quantum resource theories satisfying suitable structural conditions, we derive general one-shot bounds on the conditional distillation error exponents.<n>We obtain analytical characterizations of the conditional error exponents for entanglement, coherence, and magic distillation at finite blocklength, and derive their expressions in the zero-rate regime.
• Score: 4.704514200771222
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: In this manuscript, we establish a unified framework for analyzing the conditional error exponents of probabilistic resource distillation under approximately resource-nongenerating instruments. For generic quantum resource theories satisfying suitable structural conditions, we derive general one-shot bounds on the conditional distillation error exponents. By relating probabilistic distillation to postselected composite quantum hypothesis testing, we further obtain analytical characterizations of the conditional error exponents for entanglement, coherence, and magic distillation at finite blocklength, and derive their asymptotic expressions in the zero-rate regime. For several representative families of states in entanglement and magic resource theories, these characterizations reduce to explicit closed-form formulas. Comparing them with the corresponding deterministic distillation exponents, we identify regimes in which postselection yields a strict improvement in the exponential decay rate of the conditional error. Our results reveal an operational advantage of postselection in quantum resource distillation and establish postselected composite hypothesis testing as a general tool for characterizing probabilistic resource-processing tasks.

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