Reqomp: Space-constrained Uncomputation for Quantum Circuits

Quantum circuits must run on quantum computers with tight limits on qubit and gate counts. To generate circuits respecting both limits, a promising opportunity is exploiting $uncomputation$ to trade qubits for gates. We present Reqomp, a method to automatically synthesize correct and efficient uncomputation of ancillae while respecting hardware constraints. For a given circuit, Reqomp can offer a wide range of trade-offs between tightly constraining qubit count or gate count. Our evaluation demonstrates that Reqomp can significantly reduce the number of required ancilla qubits by up to 96%. On 80% of our benchmarks, the ancilla qubits required can be reduced by at least 25% while never incurring a gate count increase beyond 28%

Performance of non-invasive tests and histology for the prediction of clinical outcomes in patients with non-alcoholic fatty liver disease: an individual participant data meta-analysis

BackgroundHistologically assessed liver fibrosis stage has prognostic significance in patients with non-alcoholic fatty liver disease (NAFLD) and is accepted as a surrogate endpoint in clinical trials for non-cirrhotic NAFLD. Our aim was to compare the prognostic performance of non-invasive tests with liver histology in patients with NAFLD.MethodsThis was an individual participant data meta-analysis of the prognostic performance of histologically assessed fibrosis stage (F0–4), liver stiffness measured by vibration-controlled transient elastography (LSM-VCTE), fibrosis-4 index (FIB-4), and NAFLD fibrosis score (NFS) in patients with NAFLD. The literature was searched for a previously published systematic review on the diagnostic accuracy of imaging and simple non-invasive tests and updated to Jan 12, 2022 for this study. Studies were identified through PubMed/MEDLINE, EMBASE, and CENTRAL, and authors were contacted for individual participant data, including outcome data, with a minimum of 12 months of follow-up. The primary outcome was a composite endpoint of all-cause mortality, hepatocellular carcinoma, liver transplantation, or cirrhosis complications (ie, ascites, variceal bleeding, hepatic encephalopathy, or progression to a MELD score ≥15). We calculated aggregated survival curves for trichotomised groups and compared them using stratified log-rank tests (histology: F0–2 vs F3 vs F4; LSM: 2·67; NFS: 0·676), calculated areas under the time-dependent receiver operating characteristic curves (tAUC), and performed Cox proportional-hazards regression to adjust for confounding. This study was registered with PROSPERO, CRD42022312226.FindingsOf 65 eligible studies, we included data on 2518 patients with biopsy-proven NAFLD from 25 studies (1126 [44·7%] were female, median age was 54 years [IQR 44–63), and 1161 [46·1%] had type 2 diabetes). After a median follow-up of 57 months [IQR 33–91], the composite endpoint was observed in 145 (5·8%) patients. Stratified log-rank tests showed significant differences between the trichotomised patient groups (p<0·0001 for all comparisons). The tAUC at 5 years were 0·72 (95% CI 0·62–0·81) for histology, 0·76 (0·70–0·83) for LSM-VCTE, 0·74 (0·64–0·82) for FIB-4, and 0·70 (0·63–0·80) for NFS. All index tests were significant predictors of the primary outcome after adjustment for confounders in the Cox regression.InterpretationSimple non-invasive tests performed as well as histologically assessed fibrosis in predicting clinical outcomes in patients with NAFLD and could be considered as alternatives to liver biopsy in some cases

Abstraqt: Analysis of Quantum Circuits via Abstract Stabilizer Simulation

Stabilizer simulation can efficiently simulate an important class of quantum circuits consisting exclusively of Clifford gates. However, all existing extensions of this simulation to arbitrary quantum circuits including non-Clifford gates suffer from an exponential runtime. To address this challenge, we present a novel approach for efficient stabilizer simulation on arbitrary quantum circuits, at the cost of lost precision. Our key idea is to compress an exponential sum representation of the quantum state into a single abstract summand covering (at least) all occurring summands. This allows us to introduce an abstract stabilizer simulator that efficiently manipulates abstract summands by over−approximating the effect of circuit operations including Clifford gates, non-Clifford gates, and (internal) measurements. We implemented our abstract simulator in a tool called Abstraqt and experimentally demonstrate that Abstraqt can establish circuit properties intractable for existing techniques.ISSN:2521-327

Unqomp: Synthesizing uncomputation in Quantum circuits

A key challenge when writing quantum programs is the need for uncomputation: temporary values produced during the computation must be reset to zero before they can be safely discarded. Unfortunately, most existing quantum languages require tedious manual uncomputation, often leading to inefficient and error-prone programs. We present Unqomp, the first procedure to automatically synthesize uncomputation in a given quantum circuit. Unqomp can be readily integrated into popular quantum languages, allowing the programmer to allocate and use temporary values analogously to classical computation, knowing they will be uncomputed by Unqomp. Our evaluation shows that programs leveraging Unqomp are not only shorter (-19% on average), but also generate more efficient circuits (-71% gates and-19% qubits on average)

Modular Relaxed Dependencies in Weak Memory Concurrency

We present a denotational semantics for weak memory concurrency that avoids thin-air reads, provides data-race free programs with sequentially consistent semantics (DRF-SC), and supports a compositional refinement relation for validating optimisations. Our semantics identifies false program dependencies that might be removed by compiler optimisation, and leaves in place just the dependencies necessary to rule out thin-air reads. We show that our dependency calculation can be used to rule out thin-air reads in any axiomatic concurrency model, in particular C++. We present a tool that automatically evaluates litmus tests, show that we can augment C++ to fix the thin-air problem, and we prove that our augmentation is compatible with the previously used compilation mappings over key processor architectures. We argue that our dependency calculation offers a practical route to fixing the longstanding problem of thin-air reads in the C++ specification

Twenty actions for a “good Anthropocene”—perspectives from early-career conservation professionals

Humans are now recognized as the main drivers of environmental change, leaving the future of our planet dependent on human action or inaction. Although the outlook of our planet is often depicted in a “doom and gloom” manner due to recent troubling environmental trends, we suggest that a “good Anthropocene” (in which human quality of life may be maintained or improved without cost to the environment) is attainable if we engage in adaptive, multi-disciplinary actions capable of addressing the socio-ecological issues of today and tomorrow. Early-career conservation scientists and practitioners have an unmatched understanding of novel technologies and social connectivity and, as those left with the ever-growing responsibility to be the problem solvers of the attributed increasing environmental consequences of living in the Anthropocene, their perspectives on steps towards a good Anthropocene are valuable. Here we present a list of 20 actions derived by early-career conservation scientists and practitioners for conservationists to help achieve a good Anthropocene that utilize the social connectivity and technology of today. Central to these actions are the notions that multi-, inter-, and trans-disciplinary collaboratives that embrace diverse world views need to be integrated into decision-making processes; training and outreach platforms need to communicate both environmental challenges and solutions broadly; and conservation successes need to be acknowledged and disseminated in a forward-looking, adaptive capacity. Together the 20 actions identified here reinforce the underlying paradigm shift that must accompany living in the Anthropocene, given that biodiversity and healthy ecosystems are requisite for sustained human life. By sharing this list of actions, we look to promote positive socio-environmental changes towards the collective goal of achieving a good Anthropocene.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Twenty actions for a “good anthropocene”—perspectives from early-career conservation professionals

Humans are now recognized as the main drivers of environmental change, leaving the future of our planet dependent on human action or inaction. Although the outlook of our planet is often depicted in a “doom and gloom” manner due to recent troubling environmental trends, we suggest that a “good Anthropocene” (in which human quality of life may be maintained or improved without cost to the environment) is attainable if we engage in adaptive, multi-disciplinary actions capable of addressing the socio-ecological issues of today and tomorrow. Early-career conservation scientists and practitioners have an unmatched understanding of novel technologies and social connectivity and, as those left with the ever-growing responsibility to be the problem solvers of the attributed increasing environmental consequences of living in the Anthropocene, their perspectives on steps towards a good Anthropocene are valuable. Here we present a list of 20 actions derived by early-career conservation scientists and practitioners for conservationists to help achieve a good Anthropocene that utilize the social connectivity and technology of today. Central to these actions are the notions that multi-, inter-, and trans-disciplinary collaboratives that embrace diverse world views need to be integrated into decision-making processes; training and outreach platforms need to communicate both environmental challenges and solutions broadly; and conservation successes need to be acknowledged and disseminated in a forward-looking, adaptive capacity. Together the 20 actions identified here reinforce the underlying paradigm shift that must accompany living in the Anthropocene, given that biodiversity and healthy ecosystems are requisite for sustained human life. By sharing this list of actions, we look to promote positive socio-environmental changes towards the collective goal of achieving a good Anthropocene

Machine learning algorithm improves the detection of NASH (NAS-based) and at-risk NASH: A development and validation study

Background and aims: Detecting NASH remains challenging, while at-risk NASH (steatohepatitis and F≥ 2) tends to progress and is of interest for drug development and clinical application. We developed prediction models by supervised machine learning techniques, with clinical data and biomarkers to stage and grade patients with NAFLD. Approach and results: Learning data were collected in the Liver Investigation: Testing Marker Utility in Steatohepatitis metacohort (966 biopsy-proven NAFLD adults), staged and graded according to NASH CRN. Conditions of interest were the clinical trial definition of NASH (NAS ≥ 4;53%), at-risk NASH (NASH with F ≥ 2;35%), significant (F ≥ 2;47%), and advanced fibrosis (F ≥ 3;28%). Thirty-five predictors were included. Missing data were handled by multiple imputations. Data were randomly split into training/validation (75/25) sets. A gradient boosting machine was applied to develop 2 models for each condition: clinical versus extended (clinical and biomarkers). Two variants of the NASH and at-risk NASH models were constructed: direct and composite models.Clinical gradient boosting machine models for steatosis/inflammation/ballooning had AUCs of 0.94/0.79/0.72. There were no improvements when biomarkers were included. The direct NASH model produced AUCs (clinical/extended) of 0.61/0.65. The composite NASH model performed significantly better (0.71) for both variants. The composite at-risk NASH model had an AUC of 0.83 (clinical and extended), an improvement over the direct model. Significant fibrosis models had AUCs (clinical/extended) of 0.76/0.78. The extended advanced fibrosis model (0.86) performed significantly better than the clinical version (0.82). Conclusions: Detection of NASH and at-risk NASH can be improved by constructing independent machine learning models for each component, using only clinical predictors. Adding biomarkers only improved the accuracy of fibrosis