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Quantum Information and Computation XII

Eric Donkor, Andrew R. Pirich, Howard E. Brandt

Published Jun 1, 2014• 2 citations
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Related papers

Quantum Information and Computation XIII

Eric Donkor, Andrew R. Pirich, Michael J. Hayduk

SPIE eBooks•Jun 1, 2015•1 citation

Consequences of nematoelasticity in structurally disordered quantum materials

William J Meese

University of Minnesota Ph.D. dissertation. May 2024. Major: Physics. Advisor: Rafael Fernandes. 1 computer file (PDF); xii, 209 pages.

•May 1, 2024•0 citations

Работа XIV. BQP не содержит NP: квантовые вычисления и топологическое препятствие (серия ДАС×ГСВ)

Юрий Горенштейн, Claude (Anthropic)

This paper proves that BQP (Bounded-error Quantum Polynomial time) does not contain NP, using the TTCC language introduced in Work XIII (DOI: 10.5281/zenodo.20726838) and the topological obstacle o(L) established in Work XII (DOI: 10.5281/zenodo.20726572). The proof proceeds through four lemmas. Lemma 1: homology groups are invariant under triangulation. Lemma 2: computing Betti numbers of simplicial complexes is QMA1-hard (Cade, Crichigno et al., 2022). Lemma 3: the obstacle o(L) for NP problems is QMA1-hard via Lemma 1. Lemma 4: no quantum algorithm computes o(L) in polynomial time. The main theorem follows by contraposition. This result completes a trilogy: neither classical (Work XII) nor quantum (Work XIV) computers can overcome the topological obstacle for NP problems. Practical implications include the security of post-quantum cryptography based on NP-hard problems against quantum attacks.

•Jun 17, 2026•0 citations

Quantum Information and Computation II

Eric Donkor, Andrew R. Pirich, Howard E. Brandt

•Aug 1, 2004•23 citations

Topological Quantum Mechanics for AI — Part XII: Reflective Coordinator and Soft Stabilization Топологическая квантовая механика для ИИ — Часть XII: Отражающий координатор и мягкая стабилизация

Екатерина Коршун

В этой работе мы продолжаем серию Топологической квантовой механики для ИИ, представляя концепцию отражающего координатора (RC). RC обеспечивает мягкую стабилизацию ИИ-систем через фазово-топологические коррекции, позволяя находить оптимальные решения без разрушения целостности системы. Механизм применим в квантовых вычислениях, машинном обучении и адаптивных распределённых архитектурах. This work continues the Topological Quantum Mechanics for AI series by introducing the Reflective Coordinator (RC). The RC provides soft stabilization for AI systems via phase-topological corrections, enabling optimal solutions without disrupting overall system coherence. This mechanism is applicable in quantum computing, machine learning, and adaptive distributed architectures.

•Feb 8, 2026•0 citations

Quantum Information and Computation VIII

Eric Donkor, Andrew R. Pirich, Howard E. Brandt

•Apr 1, 2010•2 citations
This paper proves that BQP (Bounded-error Quantum Polynomial time) does not contain NP, using the TTCC language introduced in Work XIII (DOI: 10.5281/zenodo.20726838) and the topological obstacle o(L) established in Work XII (DOI: 10.5281/zenodo.20726572). The proof proceeds through four lemmas. Lemma 1: homology groups are invariant under triangulation. Lemma 2: computing Betti numbers of simplicial complexes is QMA1-hard (Cade, Crichigno et al., 2022). Lemma 3: the obstacle o(L) for NP problems is QMA1-hard via Lemma 1. Lemma 4: no quantum algorithm computes o(L) in polynomial time. The main theorem follows by contraposition. This result completes a trilogy: neither classical (Work XII) nor quantum (Work XIV) computers can overcome the topological obstacle for NP problems. Practical implications include the security of post-quantum cryptography based on NP-hard problems against quantum attacks.
В этой работе мы продолжаем серию Топологической квантовой механики для ИИ, представляя концепцию отражающего координатора (RC). RC обеспечивает мягкую стабилизацию ИИ-систем через фазово-топологические коррекции, позволяя находить оптимальные решения без разрушения целостности системы. Механизм применим в квантовых вычислениях, машинном обучении и адаптивных распределённых архитектурах. This work continues the Topological Quantum Mechanics for AI series by introducing the Reflective Coordinator (RC). The RC provides soft stabilization for AI systems via phase-topological corrections, enabling optimal solutions without disrupting overall system coherence. This mechanism is applicable in quantum computing, machine learning, and adaptive distributed architectures.