A linear response framework for quantum simulation of bosonic and fermionic correlation functions.

Response functions are a fundamental aspect of physics; they represent the link between experimental observations and the underlying quantum many-body state. However, this link is often under-appreciated, as the Lehmann formalism for obtaining response functions in linear response has no direct link to experiment. Within the context of quantum computing, and via a linear response framework, we restore this link by making the experiment an inextricable part of the quantum simulation. This method can be frequency- and momentum-selective, avoids limitations on operators that can be directly measured, and can be more efficient than competing methods. As prototypical examples of response functions, we demonstrate that both bosonic and fermionic Green's functions can be obtained, and apply these ideas to the study of a charge-density-wave material on the ibm_auckland superconducting quantum computer. The linear response method provides a robust framework for using quantum computers to study systems in physics and chemistry.

Intensive care unit readmission in adult Egyptian patients undergoing living donor liver transplant: A single-centre retrospective cohort study.

BACKGROUND: Patients who undergo living donor liver transplantation (LDLT) may suffer complications that require intensive care unit (ICU) readmission. AIM: To identify the incidence, causes, and outcomes of ICU readmission after LDLT. METHODS: A retrospective cohort study was conducted on patients who underwent LDLT. The collected data included patient demographics, preoperative characteristics, intraoperative details; postoperative stay, complications, causes of ICU readmission, and outcomes. Patients were divided into two groups according to ICU readmission after hospital discharge. Risk factors for ICU readmission were identified in univariate and multivariate analyses. RESULTS: The present study included 299 patients. Thirty-one (10.4%) patients were readmitted to the ICU after discharge. Patients who were readmitted to the ICU were older in age (53.0 ± 5.1 vs 49.4 ± 8.8, P = 0.001) and had a significantly higher percentage of women (29% vs 13.4%, P = 0.032), diabetics (41.9% vs 24.6%, P = 0.039), hypertensives (22.6% vs 6.3%, P = 0.006), and renal (6.5% vs 0%, P = 0.010) patients as well as a significantly longer initial ICU stay (6 vs 4 d, respectively, P < 0.001). Logistic regression analysis revealed that significant independent risk factors for ICU readmission included recipient age (OR = 1.048, 95%CI = 1.005-1.094, P = 0.030) and length of initial hospital stay (OR = 0.836, 95%CI = 0.789-0.885, P < 0.001). CONCLUSION: The identification of high-risk patients (older age and shorter initial hospital stay) before ICU discharge may help provide optimal care and tailor follow-up to reduce the rate of ICU readmission.