Quantum Integrated Sensing and Computation with Indefinite Causal Order

2026-02-10

AI Summary

This work proposes a novel paradigm based on indefinite causal order (ICO) to overcome the strict causal sequencing between perception and computation in conventional quantum information processing, which limits their synergistic efficiency. For the first time, ICO is introduced into integrated perception-and-computation tasks by employing a single quantum state as an agent that simultaneously performs state observation and function learning within a superposed causal structure. This approach transcends the traditional “perceive-then-compute” temporal constraint and, when combined with parameterized quantum models, a quantum-state-agent mechanism, and sensing-and-prediction algorithms tailored for magnetic navigation, significantly reduces both training and test losses in representative tasks. The results demonstrate the method’s effectiveness and superiority in practical quantum information processing scenarios.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

Quantum operations with indefinite causal order (ICO) represent a framework in quantum information processing where the relative order between two events can be indefinite. In this paper, we investigate whether sensing and computation, two canonical tasks in quantum information processing, can be carried out within the ICO framework. We propose a scheme for integrated sensing and computation that uses the same quantum state for both tasks. The quantum state is represented as an agent that performs state observation and learns a function of the state to make predictions via a parametric model. Under an ICO operation, the agent experiences a superposition of orders, one in which it performs state observation and then executes the required computation steps, and another in which the agent carries out the computation first and then performs state observation. This is distinct from prevailing information processing and machine intelligence paradigms where information acquisition and learning follow a strict causal order, with the former always preceding the latter. We provide experimental results and we show that the proposed scheme can achieve small training and testing losses on a representative task in magnetic navigation.

Problem

• quantum sensing
• quantum computation
• indefinite causal order
• integrated sensing and computation
• quantum information processing

Innovation

• indefinite causal order
• quantum sensing
• quantum computation
• integrated quantum information processing
• quantum machine learning