Quantum
The Classical Boundary of Quantum Computation: History-Resolved Causal Stochastic Machines and the Cost of Classicalization
Publication: quant-ph updates on arXiv.org
Author: Kyoungho Cho, Junghee Ryu, Minsu Kim, Jeongho Bang
Source published: 19/10/2026, 04:00:00 UTC
Briefing created: 19/10/2026, 04:21:03 UTC
Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 19/10/2026, 04:13:24 UTC.
Briefings
This article proposes a precise classical boundary for quantum computation using history-resolved causal stochastic machines (HCSMs). It establishes that HCSM history laws coincide exactly with the fixed-basis, prefix-preserving diagonal sector of ideal quantum processes, denoted as PTM_proc. This boundary is identified as the limit where reversible quantum processes can be simulated stochastically, while coherent quantum processes extend strictly beyond it. The paper argues that universal quantum continuations require retaining predictive information absent from visible probabilities, leading to quantum-state dimension lower bounds under full-state encoding assumptions.
Key points
- HCSM history laws coincide exactly with the fixed-basis, prefix-preserving diagonal sector of ideal quantum processes, denoted as PTM_proc.
- The boundary between quantum and classical computation is defined by the requirement to retain predictive information absent from visible probabilities in stochastic simulations.
Context
The study addresses the fundamental question of what separates quantum computation from classical computation when a classical model may retain every branch history and arbitrary causal memory. This research provides a precise classical boundary and a no-free-classicalization principle, refining the familiar slogan PTM subset QTM into a specific structural constraint on quantum processes.
Limitations
The evidence is limited to a single preprint (arXiv:2610.22750v1) from October 2026, which may not reflect final peer-reviewed conclusions. The study relies on theoretical proofs and abstract definitions rather than experimental data or empirical validation. Key parameters like specific dimension lower bounds are derived under continuous full-state encoding assumptions without detailed numerical constraints or known exceptions.
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