Quantum Integrated Sensing and Computation with Indefinite Causal Order

TL;DR

This paper explores the use of indefinite causal order in quantum information processing to integrate sensing and computation tasks within a single quantum framework, demonstrating potential advantages in magnetic navigation tasks.

Contribution

It introduces a novel scheme for combined quantum sensing and computation utilizing ICO, enabling superpositions of task orders and improving task performance.

Findings

Achieves small training and testing losses in magnetic navigation

Demonstrates feasibility of integrated sensing and computation with ICO

Shows potential advantages over traditional causal order paradigms

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.