Lineshape Optimization in Inhomogeneous $\Lambda$-type Quantum Memory

TL;DR

This paper investigates how to optimize the spectral lineshape in inhomogeneously broadened $ ext{Lambda}$-type quantum memories to enhance efficiency, comparing EIT and AFC protocols, and employing numerical methods for optimal lineshape design.

Contribution

It introduces a systematic approach to optimize inhomogeneous lineshapes for quantum memory efficiency, including protocol-agnostic numerical optimization techniques.

Findings

Optimized lineshapes improve EIT memory efficiency.

Comparison shows optimized lineshapes outperform standard AFC.

Numerical optimization yields near-maximum memory performance.

Abstract

Photonic quantum memory is a crucial elementary operation in photonic quantum information processing. While many physically distinct memory protocols and hardware implementations have been applied to this task, the development of a quantum memory performant in all relevant metrics simultaneously (e.g., efficiency, bandwidth, lifetime, etc.) is still an open challenge. In this work, we focus on inhomogeneously broadened ensembles of $\Lambda$-type quantum emitters, which have long coherence lifetimes and broad bandwidth compatibility, but tend to exhibit low efficiency, in part due to technical constraints on medium growth and preparation, and in part due to inefficient use of a key resource in these systems: the inhomogeneously broadened excited state lineshape. We investigate the properties of electromagnetically induced transparency (EIT) for a survey of inhomogeneous lineshapes that are straightforward to realize experimentally, and optimize the memory efficiency for each lineshape over a large range of experimental parameters. We compare the optimal EIT efficiency to the well-known atomic frequency comb (AFC) protocol, which also relies on spectral shaping of the inhomogeneous broadening, and observe that with sufficient control field power the optimized lineshapes allow more efficient storage. Finally, we optimize over the inhomogeneous lineshape in a protocol agnostic fashion by numerically constructing the linear integral kernel describing the memory interaction and using a singular value decomposition and interpolation procedure to ensure optimality of the resulting lineshape.