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Sequential Capacity of Quantum Processes with Finite Memory

arXiv 2610.02068 ↗First posted 1 Oct 2026quant-ph cs.LGcited by 0

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Abstract (arXiv)

How complex can the responses of a quantum device become as it runs longer with a fixed internal memory? We quantify this complexity through sequential response capacity: how many adaptive testing stages, each using a fresh run, can continue to separate possible processes by a prescribed gap in response probabilities. For fixed system and memory sizes, we establish a tight law relating this capacity to run length and probability resolution. At fixed resolution, the capacity grows on the order of Klog K, where K is the number of time steps in each run. Our construction attains this growth using time-dependent phase rotations on a single visible qubit with no additional internal memory; its tests give response probabilities exactly zero or one. Under the same tests, classical stochastic processes that measure in a fixed basis at every step have only linear capacity at fixed sizes and resolution. For phase sequences selected by a stored classical label, we then quantify how known independent Pauli noise changes this logarithmic enhancement. With ideal controls and weak residual phase noise after correction, we prove matching capacity bounds at a fixed small probability gap. These bounds identify the inverse residual phase-flip probability as the coherence timescale that limits the extra logarithmic growth.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 31 references that are in the map. First posted on arXiv 1 Oct 2026.

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References · 6

  1. 2018Online Learning of Quantum Statescited by 37
  2. 2022Online Self-Concordant and Relatively Smooth Minimization, With Applications to Online Portfolio Selection and Learning Quantum Statescited by 7
  3. 2024Online learning of a panoply of quantum objectscited by 5
  4. 2022Online Convex Optimization of Programmable Quantum Computers to Simulate Time-Varying Quantum Channelscited by 1
  5. 2025Online Learning of Pure States is as Hard as Mixed Statescited by 1
  6. 2025Noisy Quantum Learning Theorycited by 1

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