The quantum regression theorem (QRT) is the most widely used tool for calculating multitime correlation functions for the assessment of quantum emitters. It is an approximate method based on a Markov assumption for environmental coupling. In this Letter we quantify properties of photons emitted from a single quantum dot coupled to phonons. For the single-photon purity and the indistinguishability, we compare numerically exact path-integral results with those obtained from the QRT. It is demonstrated that the QRT systematically overestimates the influence of the environment for typical quantum dots used in quantum information technology.

Download full-text PDF

Source
http://dx.doi.org/10.1103/PhysRevLett.127.100402DOI Listing

Publication Analysis

Top Keywords

quantum regression
8
regression theorem
8
quantum dot
8
quantum
6
accuracy quantum
4
theorem photon
4
photon emission
4
emission quantum
4
dot quantum
4
theorem qrt
4

Similar Publications

Background: The full pentavalent (DPT-HepB-Hib) vaccination is the main strategy to prevent five communicable diseases in early childhood, especially in countries with huge communicable disease burdens like Ethiopia. Exploring spatial distributions and determinants of full pentavalent vaccination status in minor ecological areas in Ethiopia is crucial for creating targeted immunization campaigns and monitoring the advancement of accomplishing sustainable development goals. This study aimed to investigate the spatial disparities and determinants of full pentavalent vaccination among 12-23-month-old children in Ethiopia.

View Article and Find Full Text PDF

A Quantitative Chemometric Study of Pharmaceutical Tablet Formulations Using Multi-Spectroscopic Fibre Optic Probes.

Pharmaceuticals (Basel)

December 2024

College of Science and Engineering, Flinders University, Bedford Park, South Australia 5042, Australia.

Two fibre optic probes were custom designed to perform Raman and near-infrared spectroscopic measurements. Our long-term objective is to develop a non-destructive tool able to collect data in hard-to-access locations for real-time analysis or diagnostic purposes. This study evaluated the quantitative performances of Probe A and Probe B using model pharmaceutical tablets.

View Article and Find Full Text PDF

An Automated Workflow to Discover the Structure-Stability Relations for Radiation Hard Molecular Semiconductors.

J Am Chem Soc

January 2025

Institute of Materials for Electronics and Energy Technology (i-MEET), Department of Materials Science and Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Martensstraße 7, 91058 Erlangen, Germany.

Article Synopsis
  • Emerging photovoltaics require radiation-hard materials for use in outer space, but predicting their resilience to high-energy radiation is currently a challenge.
  • The research combines lab automation and machine learning to rapidly identify and test over 130 organic hole transport materials, assessing their stability under UVC light exposure.
  • Findings reveal that materials with fused aromatic rings are more stable, while certain chemical groups negatively impact stability, providing valuable insights for future molecular design in creating durable semiconductors.
View Article and Find Full Text PDF

Regressions on quantum neural networks at maximal expressivity.

Sci Rep

December 2024

Departamento de Física, Universidad Carlos III de Madrid, Avda. de la Universidad 30, 28911, Leganés, Spain.

Considering a universal deep neural network organized as a series of nested qubit rotations, accomplished by adjustable data re-uploads we analyze its expressivity. This ability to approximate continuous functions in regression tasks is quantified making use of a partial Fourier decomposition of the generated output and systematically benchmarked with the aid of a teacher-student scheme. While the maximal expressive power increases with the depth of the network and the number of qubits, it is fundamentally bounded by the data encoding mechanism.

View Article and Find Full Text PDF

Chemometric techniques for the prediction of milk composition from MIR spectral data: A review.

Food Chem

December 2024

School of Chemical Sciences, The University of Auckland, 23 Symonds St., Auckland 1142, New Zealand; Te Pūnaha Matatini, Auckland, 1142, New Zealand. Electronic address:

Chemometrics; use of statistical models to characterise and understand complex chemical systems/samples, is an advancing field. In the dairy industry, the accurate prediction of milk composition involves combining mid-infrared spectroscopy with chemometric techniques for the evaluation of major constituents of milk. The increased interest in determination of detailed composition of dairy products, alongside emerging and more-widespread use of chemometric methodologies, have generated continuous improvement in predictive models for this application.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!