Distinguishing quantum processes with bounded coherent memory

• URL: http://arxiv.org/abs/2606.19511v1
• Date: Wed, 17 Jun 2026 18:52:19 GMT
• Title: Distinguishing quantum processes with bounded coherent memory
• Abstract summary: Distinguishing multi-time quantum processes is a fundamental task.<n>Standard benchmark for distinguishing two processes is the strategy-norm distance.<n>We introduce machines for autonomous distinction.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Distinguishing multi-time quantum processes is a fundamental task underlying the diagnosis, benchmarking, and learning of temporally correlated quantum dynamics. The standard benchmark for distinguishing two processes is the strategy-norm distance, which optimizes over arbitrary adaptive probing strategies but can require large coherent memory and time-dependent control. We introduce machines for autonomous distinction~($\mathsf{MAD}$s): probing strategies that apply the same quantum instrument at each time step, retain the full classical outcome record, and carry a coherent memory of dimension $d_A$. Optimizing over these strategies defines a memory-parametrized distinguishability measure, $d^{(N)}_{\mathsf{MAD}}(\mathbf{P}^N,\mathbf{Q}^N;d_A)$. We show that the resulting hierarchy is monotone in coherent memory and complete at finite times. Specifically, any admissible $N$-step probing strategy can be compiled into a single $\mathsf{MAD}$ with an internal counter and sufficiently large coherent memory, so the hierarchy saturates the strategy-norm benchmark. For recurrent processes generated by repeated system--environment interactions, we derive a single-step description that separates the generation of new distinguishing information from the propagation and decay of information generated at earlier times. Numerical results in a repeated-interaction model show that increasing coherent memory systematically improves the $\mathsf{MAD}$ success probability and closes the gap to the strategy-norm distance while remaining substantially more tractable to evaluate. $\mathsf{MAD}$ distinguishability therefore provides an operational and scalable framework for quantifying what can be learned about genuinely multi-time quantum processes with bounded coherent memory.

Related papers

• Least Variable Quantum Counting Processes [0.27185251060695437]
  We find a finite-memory quantum advantage in temporal precision and connect discrete-time counting processes with continuous-time quantum timekeeping.<n>Our results establish a finite-memory quantum advantage in temporal precision and connect discrete-time counting processes with continuous-time quantum timekeeping.
  arXiv  Detail & Related papers  (2026-08-26T18:00:00Z)
• UniMem: Complementary Episodic-to-Parametric Memory for Boundary-Agnostic Task Streams [77.52140119830241]
  We propose UniMem, a self-routing framework for autonomous memory management.<n>UniMem uses learnable routing tokens as memory controllers, enabling adaptive coordination between complementary memory pathways.<n>Experiments on long-horizon streaming task sequences show that UniMem consistently outperforms baselines while maintaining execution fidelity.
  arXiv  Detail & Related papers  (2026-07-28T17:28:21Z)
• Automatic quantum function parallelization and memory management in Qrisp [0.0]
  We introduce a novel data-structure for representing quantum programs called permeability DAG.<n> operating on this representation facilitates a variety of powerful transformations.<n>Memory management and parallelization can be made sensitive to execution speed details of each particular quantum gate.
  arXiv  Detail & Related papers  (2026-06-30T15:40:51Z)
• LongSpike: Fractional Order Spiking State Space Models for Efficient Long Sequence Learning [53.855185131868446]
  Spiking Neural Networks (SNNs) are well-regarded for their biological plausibility and energy efficiency in processing sequential data.<n>In this work, we propose LongSpike, a novel SNN framework that integrates fractional-order State-Space Modeling.<n>By extending traditional integer-order SSMs to the fractional-calculus regime, LongSpike enables the hierarchical integration of neuronal dynamics with long-memory kernels.
  arXiv  Detail & Related papers  (2026-06-11T04:54:07Z)
• DMF: A Deterministic Memory Framework for Conversational AI Agents [0.0]
  We present the Deterministic Memory Framework (DMF), a CPU-first approach that replaces generative memory compression with a fully deterministic pipeline.<n>DMF assigns each conversational interaction a Survival Score $$ computed from deterministic content signals, conversational cues, and structured provenance, combined through a logistic projection.<n>Experiments are conducted on a purpose-built benchmark using the LoCoMo and LongMemEval datasets.
  arXiv  Detail & Related papers  (2026-06-02T10:41:28Z)
• Efficient construction of time-invariant process tensors for simulating high-dimensional non-Markovian open quantum systems [0.0]
  We propose a new algorithm for computing process tensors--matrix product operator (MPO) representations.<n>MPO representations capture the environment influence--which achieves greatly enhanced computational scalings with system size.<n>The algorithm we introduce not only allows for capturing non-Markovian dynamics in large open quantum systems, but also further extends all the existing capabilities of process tensors.
  arXiv  Detail & Related papers  (2026-03-06T20:02:43Z)
• From Verbatim to Gist: Distilling Pyramidal Multimodal Memory via Semantic Information Bottleneck for Long-Horizon Video Agents [78.30630000529133]
  We propose MM-Mem, a pyramidal multimodal memory architecture grounded in Fuzzy-Trace Theory.<n> MM-Mem memory structures hierarchically into a Sensory Buffer, Episodic Stream, and Symbolic.<n>Experiments confirm the effectiveness of MM-Mem on both offline and streaming tasks.
  arXiv  Detail & Related papers  (2026-03-02T05:12:45Z)
• Agentic Learner with Grow-and-Refine Multimodal Semantic Memory [50.81667005063605]
  ViLoMem is a dual-stream memory framework that constructs compact, schema-based memory.<n>It encodes visual distraction patterns and logical reasoning errors, enabling MLLMs to learn from their successful and failed experiences.
  arXiv  Detail & Related papers  (2025-11-26T18:55:08Z)
• Heterogenous Memory Augmented Neural Networks [84.29338268789684]
  We introduce a novel heterogeneous memory augmentation approach for neural networks. By introducing learnable memory tokens with attention mechanism, we can effectively boost performance without huge computational overhead. We show our approach on various image and graph-based tasks under both in-distribution (ID) and out-of-distribution (OOD) conditions.
  arXiv  Detail & Related papers  (2023-10-17T01:05:28Z)
• Sublinear scaling in non-Markovian open quantum systems simulations [0.0]
  We introduce a numerically exact algorithm to calculate process tensors. Our approach requires only $mathcalO(nlog n)$ singular value decompositions for environments with infinite memory.
  arXiv  Detail & Related papers  (2023-04-11T15:40:33Z)
• Cumulative Memory Lower Bounds for Randomized and Quantum Computation [1.52292571922932]
  Cumulative memory is a measure of time-space complexity. We prove the first lower bounds on cumulative memory complexity for both sequential classical computation and quantum circuits.
  arXiv  Detail & Related papers  (2023-01-13T17:57:02Z)
• Quantum Goemans-Williamson Algorithm with the Hadamard Test and Approximate Amplitude Constraints [62.72309460291971]
  We introduce a variational quantum algorithm for Goemans-Williamson algorithm that uses only $n+1$ qubits. Efficient optimization is achieved by encoding the objective matrix as a properly parameterized unitary conditioned on an auxilary qubit. We demonstrate the effectiveness of our protocol by devising an efficient quantum implementation of the Goemans-Williamson algorithm for various NP-hard problems.
  arXiv  Detail & Related papers  (2022-06-30T03:15:23Z)
• Theoretical Convergence of Multi-Step Model-Agnostic Meta-Learning [63.64636047748605]
  We develop a new theoretical framework to provide convergence guarantee for the general multi-step MAML algorithm. In particular, our results suggest that an inner-stage step needs to be chosen inversely proportional to $N$ of inner-stage steps in order for $N$ MAML to have guaranteed convergence.
  arXiv  Detail & Related papers  (2020-02-18T19:17:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.