Collectively pumped superradiant laser with coherence scaling beyond the standard quantum limit
Ori Somech, Howard M. Wiseman
Abstract
For more than half a century, the standard quantum limit (SQL) was thought to limit the laser coherence C -- the number of photons emitted from the laser into the beam in one coherence time -- to a scaling Θ(μ2), where μ is the mean number of optical-frequency excitations stored inside the laser. However, recently it has been shown [Baker et al., Nat. Phys. 17, 179 (2021)] that the Heisenberg limit -- an achievable ultimate limit set by quantum mechanics for the task of producing a beam with the standard properties of a laser beam -- is C=Θ(μ4), a quadratic enhancement. So far, proposals to demonstrate beyond-SQL scaling of C have been limited to circuit QED, at microwave frequencies (i.e., a maser). Here, we introduce a class of generalized far-above-threshold (FAT) laser models, obtained by replacing the ordinary bosonic lowering operator a by a general lowering operator L in the superoperators appearing in the master equation that governs the FAT laser dynamics. We then show that an optical-frequency superradiant laser in the bad-cavity limit, with feedback-controlled collective pumping, realizes one such model, in which L is identified with the collective superradiant lowering operator J. This model can surpass the SQL scaling, with the coherence exhibiting a scaling as large as C= Θ(μ8/3) in the optimal parameter regime. Here, in the bad-cavity limit, μ≈ N/2, where N is the number of superradiant atoms.
Create a lesson
Related papers
Using topology to reveal all mutually unbiased bases in dimension six
Mateo Cárdenes Wuttig, Joseph Tindall
Arithmetic Depth, Minimal Gate Alphabets, and Exact Program-State Resources for Single-Qubit Dyadic Programs
Tony Newton, Dan-Adrian German
Probing gravity at megahertz frequencies with levitated rotors
Matteo Fadel, Andrea Vinante
Optimal Holding Times for Entanglement Swapping
Van Sy Mai, Richard J. La, Abdella Battou
Quantum Algorithms for Molecular Response Functions: Resolvent Kernels, Time-Domain Sampling, Query Lower Bounds with Matched Single-Point Calibers, and a Six-Route Comparison
Yunlong Liu, Yujin Tang, Miao Xu et al.
GFlowQEC: Generative Flow Networks that Learn to Sample Quantum LDPC Codes
Nuno Batista, Gabriel Falcao, Penousal Machado