A quantum algorithm for solving open system dynamics on quantum computers using noise

In this paper we present a quantum algorithm that uses noise as a resource. The goal of our quantum algorithm is the calculation of operator averages of an open quantum system evolving in time. Selected low-noise system qubits and noisy bath qubits represent the system and the bath of the open quantum system. All incoherent qubit noise can be mapped to bath spectral functions. The form of the spectral functions can be tuned digitally, allowing for the time evolution of a wide range of open-system models at finite temperature. We study the feasibility of this approach with a focus on the solution of the spin-boson model and assume intrinsic qubit noise that is dominated by damping and dephasing. We find that classes of open quantum systems exist where our algorithm performs very well, even with gate errors as high as 1%. In general the presented algorithm performs best if the system-bath interactions can be decomposed into native gates.Comment: 19 pages, 8 figures in total: 10 pages main text with 7 figure

Consensus guidelines for sarcopenia prevention, diagnosis and management in Australia and New Zealand

Background: Sarcopenia is an age-associated skeletal muscle condition characterized by low muscle mass, strength, and physical performance. There is no international consensus on a sarcopenia definition and no contemporaneous clinical and research guidelines specific to Australia and New Zealand. The Australian and New Zealand Society for Sarcopenia and Frailty Research (ANZSSFR) Sarcopenia Diagnosis and Management Task Force aimed to develop consensus guidelines for sarcopenia prevention, assessment, management and research, informed by evidence, consumer opinion, and expert consensus, for use by health professionals and researchers in Australia and New Zealand. Methods: A four-phase modified Delphi process involving topic experts and informed by consumers, was undertaken between July 2020 and August 2021. Phase 1 involved a structured meeting of 29 Task Force members and a systematic literature search from which the Phase 2 online survey was developed (Qualtrics). Topic experts responded to 18 statements, using 11-point Likert scales with agreement threshold set a priori at >80%, and five multiple-choice questions. Statements with moderate agreement (70%–80%) were revised and re-introduced in Phase 3, and statements with low agreement (80%) were confirmed by the Task Force in Phase 4. Conclusions: The ANZSSFR Task Force present 17 sarcopenia management and research recommendations for use by health professionals and researchers which includes the recommendation to adopt the EWGSOP2 sarcopenia definition in Australia and New Zealand. This rigorous Delphi process that combined evidence, consumer expert opinion and topic expert consensus can inform similar initiatives in countries/regions lacking consensus on sarcopenia

Consensus guidelines for sarcopenia prevention, diagnosis and management in Australia and New Zealand

Background: Sarcopenia is an age-associated skeletal muscle condition characterized by low muscle mass, strength, and physical performance. There is no international consensus on a sarcopenia definition and no contemporaneous clinical and research guidelines specific to Australia and New Zealand. The Australian and New Zealand Society for Sarcopenia and Frailty Research (ANZSSFR) Sarcopenia Diagnosis and Management Task Force aimed to develop consensus guidelines for sarcopenia prevention, assessment, management and research, informed by evidence, consumer opinion, and expert consensus, for use by health professionals and researchers in Australia and New Zealand. Methods: A four-phase modified Delphi process involving topic experts and informed by consumers, was undertaken between July 2020 and August 2021. Phase 1 involved a structured meeting of 29 Task Force members and a systematic literature search from which the Phase 2 online survey was developed (Qualtrics). Topic experts responded to 18 statements, using 11-point Likert scales with agreement threshold set a priori at \u3e 80 %, and five multiple-choice questions. Statements with moderate agreement (70 % – 80 %) were revised and re-introduced in Phase 3, and statements with low agreement ( \u3c 70 %) were rejected. In Phase 3, topic experts responded to six revised statements and three additional questions, incorporating results from a parallel Consumer Expert Delphi study. Phase 4 involved finalization of consensus statements. Results: Topic experts from Australia (n = 62, 92.5 %) and New Zealand (n = 5, 7.5 %) with a mean ± SD age of 45.7 ± 11.8 years participated in Phase 2; 38 (56.7 %) were women, 38 (56.7 %) were health professionals and 27 (40.3 % ) were researchers/academics. In Phase 2, 15 of 18 (83.3 %) statements on sarcopenia prevention, screening, assessment, management and future research were accepted with strong agreement. The strongest agreement related to encouraging a healthy lifestyle (100 %) and offering tailored resistance training to people with sarcopenia (92.5 %). Forty-seven experts participated in Phase 3; 5/6 (83.3 %) revised statements on prevention, assessment and management were accepted with strong agreement. A majority of experts (87.9 %) preferred the revised European Working Group for Sarcopenia in Older Persons (EWGSOP2) definition. Seventeen statements with strong agreement ( \u3e 80 %) were confirmed by the Task Force in Phase 4. Conclusions: The ANZSSFR Task Force present 17 sarcopenia management and research recommendations for use by health professionals and researchers which includes the recommendation to adopt the EWGSOP2 sarcopenia definition in Australia and New Zealand. This rigorous Delphi process that combined evidence, consumer expert opinion and topic expert consensus can inform similar initiatives in countries/regions lacking consensus on sarcopenia

The Australian and New Zealand Society for Sarcopenia and Frailty Research (ANZSSFR) sarcopenia diagnosis and management task force: Findings from the consumer expert Delphi process

Objectives: To develop guidelines, informed by health-care consumer values and preferences, for sarcopenia prevention, assessment and management for use by clinicians and researchers in Australia and New Zealand. Methods: A three-phase Consumer Expert Delphi process was undertaken between July 2020 and August 2021. Consumer experts included adults with lived experience of sarcopenia or health-care utilisation. Phase 1 involved a structured meeting of the Australian and New Zealand Society for Sarcopenia and Frailty Research (ANZSSFR) Sarcopenia Diagnosis and Management Task Force and consumer representatives from which the Phase 2 survey was developed. In Phase 2, consumers from Australia and New Zealand were surveyed online with opinions sought on sarcopenia outcome priorities, consultation preferences and interventions. Findings were confirmed and disseminated in Phase 3. Descriptive statistical analyses were performed. Results: Twenty-four consumers (mean ± standard deviation age 67.5 ± 12.8 years, 18 women) participated in Phase 2. Ten (42%) identified as being interested in sarcopenia, 7 (29%) were health-care consumers and 6 (25%) self-reported having/believing they have sarcopenia. Consumers identified physical performance, living circumstances, morale, quality of life and social connectedness as the most important outcomes related to sarcopenia. Consumers either had no preference (46%) or preferred their doctor (40%) to diagnose sarcopenia and preferred to undergo assessments at least yearly (54%). For prevention and treatment, 46% of consumers preferred resistance exercise, 2–3 times per week (54%). Conclusions: Consumer preferences reported in this study can inform the implementation of sarcopenia guidelines into clinical practice at local, state and national levels across Australia and New Zealand

Current Cut Points of Three Falls Risk Assessment Tools Are Inferior to Calculated Cut Points in Geriatric Evaluation and Management Units

Background: Falls risk assessment tools are used in hospital inpatient settings to identify patients at increased risk of falls to guide and target interventions for fall prevention. In 2022, Western Health, Melbourne, Australia, introduced a new falls risk assessment tool, the Western Health St. Thomas’ Risk Assessment Tool (WH-STRATIFY), which adapted The Northern Hospital’s risk tool (TNH-STRATIFY) by adding non-English speaking background and falls-risk medication domains to reflect patient demographics. WH-STRATIFY replaced Peninsula Health Risk Screening Tool (PH-FRAT) previously in use at Western Health. This study compared the predictive accuracy of the three falls risk assessment tools in an older inpatient high-risk population. Aims: To determine the predictive accuracy of three falls risk assessment tools (PH-FRAT, TNH-STRATIFY, and WH-STRATIFY) on admission to Geriatric Evaluation Management (GEM) units (subacute inpatient wards where the most frail and older patients rehabilitate under a multi-disciplinary team). Method: A retrospective observational study was conducted on four GEM units. Data was collected on 54 consecutive patients who fell during admission and 62 randomly sampled patients who did not fall between December 2020 and June 2021. Participants were scored against three falls risk assessment tools. The event rate Youden (Youden IndexER) indices were calculated and compared using default and optimal cut points to determine which tool was most accurate for predicting falls. Results: Overall, all tools had low predictive accuracy for falls. Using default cut points to compare falls assessment tools, TNH-STRATIFY had the highest predictive accuracy (Youden IndexER = 0.20, 95% confidence interval CI = 0.07, 0.34). The PH-FRAT (Youden IndexER = 0.01 and 95% CI = −0.04, 0.05) and WH-STRATIFY (Youden IndexER = 0.00 and 95% CI = −0.04, 0.03) were statistically equivalent and not predictive of falls compared to TNH-STRATIFY. When calculated optimal cut points were applied, predictive accuracy improved for PH-FRAT (Cut point 17, Youden IndexER = 0.14 and 95% CI = 0.01, 0.29) and WH-STRATIFY (Cut point 7, Youden IndexER = 0.18 and 95% CI = 0.00, 0.35). Conclusions: TNH-STRATIFY had the highest predictive accuracy for falls. The predictive accuracy of WH-STRATIFY improved and was significant when the calculated optimal cut point was applied. The optimal cut points of falls risk assessment tools should be determined and validated in different clinical settings to optimise local predictive accuracy, enabling targeted fall risk mitigation strategies and resource allocation