Original title: Behavioral Memory under Symmetry in One-Way Quantum Automata
Zeyu Chen · September 2026
Quantum computers store information differently from ordinary computers, especially when they follow physical rules called symmetries. Scientists wanted to know how much memory a simple, one-way quantum machine actually uses to produce its behavior. You cannot just count all its mathematical pieces, because some stay frozen, some cannot be detected, and some act like normal classical memory. The researchers solved this by building a three-step mathematical filter. They matched reachable states with future measurements to track real behavior. They found that a system's symmetry strictly controls memory. Normal, commutative pieces demand a number of classical states equal to their exact size. However, quantum pieces that do not commute add a fee of exactly one extra state. Finally, choosing different symmetries on the same space can swing memory needs from small polynomial sizes to huge exponential ones, even following a specific Catalan counting rule at half filling.
Imagine sorting laundry where some clothes can only be washed in cold water, some are invisible until dry, and others can be mixed freely. The challenge is to use the fewest baskets while accounting for these rules. In this paper, the quantum system's memory is like the laundry baskets, with symmetry imposing frozen, hidden, or movable categories. The theory determines how many baskets are needed based on the type of rules.
For quantum computing, memory efficiency is critical. This work shows how symmetry types set hard limits on memory, guiding the design of quantum automata.
The abstract does not mention experimental validation or applicability beyond one-way quantum automata with compact symmetry. It focuses on theoretical derivations, so real-world implementation details are not covered.
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Behavioral Memory under Symmetry in One-Way Quantum Automata — Zeyu Chen
Abstract licence: CC0
Explanation generated by Synthesis from the abstract — it is not the original paper. This is a preprint, so it hasn't necessarily been peer reviewed.