Reversible Lifetime Semantics for Quantum Programs

• URL: http://arxiv.org/abs/2603.14538v1
• Date: Sun, 15 Mar 2026 18:25:59 GMT
• Title: Reversible Lifetime Semantics for Quantum Programs
• Abstract summary: We present a formal model of emphScope-Bounded Liveness-Guided Uncomputation, where lexical scope bounds variable lifetime and static liveness and entanglement analysis determine the earliest safe reclamation point.<n>We prove compositional correctness under nested scopes and show that early reclamation can reduce circuit depth by avoiding critical-path overhead.<n>We characterize the constraints (irreversibility, persistent entanglement, and aliasing) under which automatic uncomputation must be restricted.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reversible computation requires that intermediate data be explicitly undone rather than discarded. In quantum programming, this principle appears as uncomputation, usually treated as a technical cleanup mechanism. We instead present uncomputation as a semantic foundation. In the Qutes language, we introduce a formal model of \emph{Scope-Bounded Liveness-Guided Uncomputation}, where lexical scope bounds variable lifetime and static liveness and entanglement analysis determine the earliest safe reclamation point. We define semantic lifetime and a Restoration Invariant ensuring that temporary quantum information disappears once it becomes semantically irrelevant. We prove compositional correctness under nested scopes and show that early reclamation can reduce circuit depth by avoiding critical-path overhead and can bound peak live qubits through disciplined ancilla reuse. Finally, we show that parameter passing semantics emerges from the same lifetime discipline, with pass-by-value and pass-by-reference corresponding to different lifetime boundaries, and we characterize the constraints (irreversibility, persistent entanglement, and aliasing) under which automatic uncomputation must be restricted.

Related papers

• Position: Quantum Program Generation Must Prioritize Validity Over Probabilistic Scaling [7.165225514185139]
  Unlike natural languages, quantum circuits require strict adherence to mathematical constraints that manifest a significant syntax-semantics gap.<n>Training on unverified quantum programs means that models learn syntax but fail to capture the physical semantics of the Hilbert space.<n>We propose a pivot from human-centric copilots to verifier-centric agents.
  arXiv  Detail & Related papers  (2026-07-15T21:28:59Z)
• DeSeG: Decoupling Semantic Intent and Geometric Constraints for Physically Plausible Human-Scene Interaction [50.06148080975928]
  We propose DeSeG, a hierarchical framework that explicitly decouples semantic intent from geometric constraints.<n>DeSeG achieves state-of-the-art performance, reducing mean scene penetration by 47% and improving semantic alignment by 29% over the SOTA baselines.
  arXiv  Detail & Related papers  (2026-07-07T03:21:08Z)
• Active Timepoint Selection for Learning Measure-Valued Trajectories [55.49809248697036]
  Inferring continuous probability paths from snapshots is a fundamental challenge in domains like single-cell biology.<n>We introduce a framework which extends active experimentation to the space of sparse measures.<n>This yields an acquisition policy that iteratively selects measurement times to minimize uncertainty.
  arXiv  Detail & Related papers  (2026-05-28T22:22:35Z)
• MIITA: Memory-Induced Inference-Time Adaptation for Continual Learning with Small Language Models [64.2820121997882]
  MIITA is a Memory-Induced Inference-Time Adaptation framework for supervised CL under constrained storage.<n>It stores supervised experiences as compact correction-direction prototypes with semantic anchors, and retrieves them at inference time using semantic and uncertainty-based cues.<n>Experiments show that MIITA consistently improves final performance and mitigates forgetting under fixed memory budgets.
  arXiv  Detail & Related papers  (2026-05-20T03:03:04Z)
• The Physical and Contextual Limits of Quantum Speedup [0.0]
  Quantum computation is frequently mischaracterized as the simultaneous execution of exponentially many classical computations.<n>This article offers a conceptual clarification, demonstrating that the components of a quantum superposition cannot be treated as independently readable classical branches.
  arXiv  Detail & Related papers  (2026-05-12T19:25:47Z)
• Word-Anchored Temporal Forgery Localization [6.691985085293349]
  We propose word-anchored temporal forgery localization (WAFL) to derive candidate forgery proposals.<n>We first analyze the essence of temporal forgeries and identify the minimum meaningful forgery units, word tokens, and then align data preprocessing with the natural linguistic boundaries of speech.<n>To overcome the extreme class imbalance inherent to forgery detection, we design the artifact-centric asymmetric (ACA) loss.
  arXiv  Detail & Related papers  (2026-03-06T12:33:28Z)
• Partial Feedback Online Learning [88.27143767009376]
  We study a new learning protocol, termed partial-feedback online learning.<n>Each instance admits a set of acceptable labels, but the learner observes only one acceptable label per round.
  arXiv  Detail & Related papers  (2026-01-29T09:39:11Z)
• Denotational semantics for stabiliser quantum programs [0.0]
  We develop a sound, universal and complete denotational semantics for stabiliser operations.<n>We demonstrate the power of the resulting semantics by describing a small, proof-of-concept assembly language for stabiliser programs.
  arXiv  Detail & Related papers  (2025-11-27T19:51:43Z)
• TaCo: Capturing Spatio-Temporal Semantic Consistency in Remote Sensing Change Detection [54.22717266034045]
  Ta-Co is a consistent semantic network for temporal semantic transitions.<n>We show that Ta-Co consistently achieves SOTA performance on remote sensing detection tasks.<n>This design can yield substantial gains without any additional computational overhead during inference.
  arXiv  Detail & Related papers  (2025-11-25T13:44:29Z)
• A unified relativistic path integral origin for noise-activated collapse and decoherence [6.864380740265179]
  We construct a relativistic path integral that recovers the Dirac, Klein-Gordon, and Schr"odinger equations.<n>This term dormant in differentiable potentials but is activated by non-differentiable noise, driving outcome probabilities through bounded-martingale process.<n>Because the trigger is the noise spectrum, our work shows that engineering colored'' noise can expedite or steer collapse.
  arXiv  Detail & Related papers  (2025-09-20T01:58:50Z)
• Qurts: Automatic Quantum Uncomputation by Affine Types with Lifetime [0.0]
  Uncomputation is a feature in quantum programming that allows the programmer to discard a value without losing quantum information. We extend the Rust type system to give a uniform framework for automatic uncomputation called Qurts.
  arXiv  Detail & Related papers  (2024-11-16T16:34:08Z)
• (Quantum) Indifferentiability and Pre-Computation [50.06591179629447]
  Indifferentiability is a cryptographic paradigm for analyzing the security of ideal objects. Despite its strength, indifferentiability is not known to offer security against pre-processing attacks. We propose a strengthening of indifferentiability which is not only composable but also takes arbitrary pre-computation into account.
  arXiv  Detail & Related papers  (2024-10-22T00:41:47Z)
• ECLIPSE: Efficient Continual Learning in Panoptic Segmentation with Visual Prompt Tuning [54.68180752416519]
  Panoptic segmentation is a cutting-edge computer vision task. We introduce a novel and efficient method for continual panoptic segmentation based on Visual Prompt Tuning, dubbed ECLIPSE. Our approach involves freezing the base model parameters and fine-tuning only a small set of prompt embeddings, addressing both catastrophic forgetting and plasticity.
  arXiv  Detail & Related papers  (2024-03-29T11:31:12Z)
• Uncovering the Missing Pattern: Unified Framework Towards Trajectory Imputation and Prediction [60.60223171143206]
  Trajectory prediction is a crucial undertaking in understanding entity movement or human behavior from observed sequences. Current methods often assume that the observed sequences are complete while ignoring the potential for missing values. This paper presents a unified framework, the Graph-based Conditional Variational Recurrent Neural Network (GC-VRNN), which can perform trajectory imputation and prediction simultaneously.
  arXiv  Detail & Related papers  (2023-03-28T14:27:27Z)
• Recovery With Incomplete Knowledge: Fundamental Bounds on Real-Time Quantum Memories [0.0]
  I consider spectator-based (incomplete knowledge) recovery protocols as a real-time parameter estimation problem. I present information-theoretic and metrological bounds on the performance of this protocol.
  arXiv  Detail & Related papers  (2022-08-08T21:26:07Z)
• Quantum Geometric Confinement and Dynamical Transmission in Grushin Cylinder [68.8204255655161]
  We classify the self-adjoint realisations of the Laplace-Beltrami operator minimally defined on an infinite cylinder. We retrieve those distinguished extensions previously identified in the recent literature, namely the most confining and the most transmitting.
  arXiv  Detail & Related papers  (2020-03-16T11:37:23Z)

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.