Eliminating Incoherent Noise: A Coherent Quantum Approach in Multi-Sensor Dark Matter Detection

TL;DR

This paper introduces a quantum coherence-based method for dark matter detection using multiple sensors, significantly improving sensitivity by eliminating incoherent noise and enhancing signal strength quadratically.

Contribution

It presents a novel quantum coherence technique across sensor networks that reduces background noise and boosts detection sensitivity in dark matter experiments.

Findings

Signal strength scales with the square of the number of sensors.

Noise increases linearly with the number of sensors due to operational infidelity.

Method enhances detection sensitivity compared to traditional approaches.

Abstract

We propose a novel dark matter detection scheme by leveraging quantum coherence across a network of multiple quantum sensors. This method effectively eliminates incoherent background noise, thereby significantly enhancing detection sensitivity. This is achieved by performing a series of basis transformation operations, allowing the coherent signal to be expressed as a combination of sensor population measurements without introducing background noise. We present a comprehensive analytical analysis and complement it with practical numerical simulations. These demonstrations reveal that signal strength is enhanced by the square of the number of sensors, while noise, primarily due to operational infidelity rather than background fluctuations, increases only linearly with the number of sensors. Our approach paves the way for next-generation dark matter searches that optimally utilize an advanced network of sensors and quantum technologies.