Automated quantum error mitigation based on probabilistic error reduction

Current quantum computers suffer from a level of noise that prohibits extracting useful results directly from longer computations. The figure of merit in many near-term quantum algorithms is an expectation value measured at the end of the computation, which experiences a bias in the presence of hardware noise. A systematic way to remove such bias is probabilistic error cancellation (PEC). PEC requires a full characterization of the noise and introduces a sampling overhead that increases exponentially with circuit depth, prohibiting high-depth circuits at realistic noise levels. Probabilistic error reduction (PER) is a related quantum error mitigation method that systematically reduces the sampling overhead at the cost of reintroducing bias. In combination with zero-noise extrapolation, PER can yield expectation values with an accuracy comparable to PEC.Noise reduction through PER is broadly applicable to near-term algorithms, and the automated implementation of PER is thus desirable for facilitating its widespread use. To this end, we present an automated quantum error mitigation software framework that includes noise tomography and application of PER to user-specified circuits. We provide a multi-platform Python package that implements a recently developed Pauli noise tomography (PNT) technique for learning a sparse Pauli noise model and exploits a Pauli noise scaling method to carry out PER.We also provide software tools that leverage a previously developed toolchain, employing PyGSTi for gate set tomography and providing a functionality to use the software Mitiq for PER and zero-noise extrapolation to obtain error-mitigated expectation values on a user-defined circuit.Comment: 11 pages, 9 figure

Conservation Status of the Plains Spotted Skunk, Spilogale putorius interrupta, in Texas, with an Assessment of Genetic Variability in the Species

Robert C. Dowler, Department of Biology at Angelo State University is the corresponding author, robert dot dowler at angelo dot eduIn this report, we present results of research on the conservation status of the plains spotted skunk (Spilogale putorius interrupta) in Texas and an assessment of the genetic variability in populations throughout the range of the species. The conservation status portion of the study included (1) mapping the species’ potential habitat in Texas using maximum entropy modeling (Maxent) with historic museum specimen records, (2) field-based surveying of locations in 10 counties to determine occurrence of the plains spotted skunk, (3) seeking additional occurrence records in Texas through crowd sourcing and citizen scientist approaches (4) using all current (2001 – 2017) occurrences to produce a model of probable geographic distribution in Texas and (5) assessing anthropogenic changes in land use, which may threaten the species’ habitats, by mapping current and forecasted oil and gas development and urbanization within the species’ modeled range. The species distribution model, combined with the land-change assessment, was used to select sites in 10 representative counties for field-based surveys in the hopes of revealing patterns of current distribution. Field surveys were carried out using live traps, enclosed track plates, and camera traps. These methods documented detections of plains spotted skunks (n = 12) in 4 of the 10 sites sampled. All methods of detection were successful, but cameras and live traps out-performed track plates. Crowd-sourced approaches and citizen scientist camera trapping revealed an additional 82 occurrences in the state, 79 of which were since 2009. These recent records were used to produce a species distribution model that provides relative probability of occurrence for the plains spotted skunk in the state. Our land-change mapping revealed potential anthropogenic threats to habitats at 2 of the sites (Katy Prairie and Fort Hood), which also had robust populations of plains spotted skunks based on 25 and 51detections, respectively). For our genetic assessment, samples of tissue from three sources (i.e., field surveys, state agencies throughout the distribution of the eastern spotted skunk, and museum tissue collections) allowed a detailed assessment of the genetic variability in the species (S. putorius) using both microsatellite markers and cytochrome b gene sequence. Our analysis of 119 specimens was able to establish that genetic patterns were consistent with currently accepted taxonomy of the 3 recognized subspecies of S. putorius (S. p. putorius, S. p. ambarvalis, and S. p. interrupta). We also determined that there was no evidence for hybridization with the congener, S. gracilis (western spotted skunk), a species co-occurring with the eastern spotted skunk in parts of Texas. The differentiation between S. p. putorius and S. p. ambarvalis was less pronounced (FST = 0.178; cytochrome b sequence divergence = 1.2%) than between these subspecies and the plains spotted skunk (average FST = 0.278; cytochrome b sequence divergence = 2.9%). Overall, genetic variability (observed heterozygosity = 0.474) in the plains spotted skunk was lower than that seen in common carnivores (striped skunks, raccoons), but slightly higher than some endangered carnivores (black-footed ferret). The heterozygosity levels more closely resemble the levels found within the island spotted skunk (S. gracilis amphiala) from the Channel Islands of California and other vertebrates that have a “threatened” conservation status. Key findings of the study include: 1) the current geographic distribution of the plains spotted skunk in Texas is reduced relative to historic records; 2) the species distribution model based on recorded occurrences since 2001 suggests areas of the state that are in need of further survey efforts; 3) genetic variability of plains spotted skunks is lower than more common carnivores, but higher than some recognized endangered species; 4) the subspecies, S. p. interrupta is a distinct genetic subunit of the eastern spotted skunk; and 5) continued energy development and especially future urbanization in some parts of Texas may affect populations of the plains spotted skunk.Texas Comptroller of Public AccountsBureau of Economic Geolog

Essential and recurrent roles for hairpin RNAs in silencing \u3ci\u3ede novo sex\u3c/i\u3e chromosome conflict in \u3ci\u3eDrosophila simulans\u3c/i\u3e

Meiotic drive loci distort the normally equal segregation of alleles, which benefits their own transmission even in the face of severe fitness costs to their host organism. However, relatively little is known about the molecular identity of meiotic drivers, their strategies of action, and mechanisms that can suppress their activity. Here, we present data from the fruitfly Drosophila simulans that address these questions. We show that a family of de novo, protamine- derived X-linked selfish genes (the Dox gene family) is silenced by a pair of newly emerged hairpin RNA (hpRNA) small interfering RNA (siRNA)-class loci, Nmy and Tmy. In the w[XD1] genetic background, knockout of nmy derepresses Dox and MDox in testes and depletes male progeny, whereas knockout of tmy causes misexpression of PDox genes and renders males sterile. Importantly, genetic interactions between nmy and tmy mutant alleles reveal that Tmy also specifically maintains male progeny for normal sex ratio. We show the Dox loci are functionally polymorphic within D. simulans, such that both nmy-associated sex ratio bias and tmy-associated sterility can be rescued by wild-type X chromosomes bearing natural deletions in different Dox family genes. Finally, using tagged transgenes of Dox and PDox2, we provide the first experimental evidence Dox family genes encode proteins that are strongly derepressed in cognate hpRNA mutants. Altogether, these studies support a model in which protamine-derived drivers and hpRNA suppressors drive repeated cycles of sex chromosome conflict and resolution that shape genome evolution and the genetic control of male gametogenesis

Automated quantum error mitigation based on probabilistic error reduction

Current quantum computers suffer from a level of noise that prohibits extracting useful results directly from longer computations. The figure of merit in many near-term quantum algorithms is an expectation value measured at the end of the computation, which experiences a bias in the presence of hardware noise. A systematic way to remove such bias is probabilistic error cancellation (PEC). PEC requires a full characterization of the noise and introduces a sampling overhead that increases exponentially with circuit depth, prohibiting high-depth circuits at realistic noise levels. Probabilistic error reduction (PER) is a related quantum error mitigation method that systematically reduces the sampling overhead at the cost of reintroducing bias. In combination with zero-noise extrapolation, PER can yield expectation values with an accuracy comparable to PEC.Noise reduction through PER is broadly applicable to near-term algorithms, and the automated implementation of PER is thus desirable for facilitating its widespread use. To this end, we present an automated quantum error mitigation software framework that includes noise tomography and application of PER to user-specified circuits. We provide a multi-platform Python package that implements a recently developed Pauli noise tomography (PNT) technique for learning a sparse Pauli noise model and exploits a Pauli noise scaling method to carry out PER.We also provide software tools that leverage a previously developed toolchain, employing PyGSTi for gate set tomography and providing a functionality to use the software Mitiq for PER and zero-noise extrapolation to obtain error-mitigated expectation values on a user-defined circuit.This is a pre-print of the article McDonough, Benjamin, Andrea Mari, Nathan Shammah, Nathaniel T. Stemen, Misty Wahl, William J. Zeng, and Peter P. Orth. "Automated quantum error mitigation based on probabilistic error reduction." arXiv preprint arXiv:2210.08611 (2022). DOI: 10.48550/arXiv.2210.08611. Copyright 2022 The Authors. Posted with permission

Late consequences of early selection: when memory monitoring backfires

At retrieval, people can adopt a retrieval orientation by which they recreate the mental operations used at encoding. Monitoring by retrieval orientation leads to assessing all test items for qualities related to the encoding task, which enriches foils with some of the qualities already possessed by targets. We investigated the consequences of adopting a retrieval orientation under conditions of repeated monitoring of the same foils. Participants first processed foils in the context of one of two tests encouraging different retrieval orientations. The foils were then re-used on a subsequent test in which retrieval orientation either matched or mismatched that adopted on the first test. In the aggregate data, false alarms for repeated foils were higher when there was a match between the retrieval orientations on both tests. This demonstrates that when retrieval orientation enriches foils with target-like characteristics, it can backfire when repeated monitoring of the same foils is required

Homeostatic levels of SRC-2 and SRC-3 promote early human adipogenesis

Individual or joint knockdown of the transcriptional coactivators SRC-2 and SRC-3 inhibits lipid accumulation in adipocytes by decreasing PPARγ activity

Cluster K Mycobacteriophages: Insights into the Evolutionary Origins of Mycobacteriophage TM4

Five newly isolated mycobacteriophages –Angelica, CrimD, Adephagia, Anaya, and Pixie – have similar genomic architectures to mycobacteriophage TM4, a previously characterized phage that is widely used in mycobacterial genetics. The nucleotide sequence similarities warrant grouping these into Cluster K, with subdivision into three subclusters: K1, K2, and K3. Although the overall genome architectures of these phages are similar, TM4 appears to have lost at least two segments of its genome, a central region containing the integration apparatus, and a segment at the right end. This suggests that TM4 is a recent derivative of a temperate parent, resolving a long-standing conundrum about its biology, in that it was reportedly recovered from a lysogenic strain of Mycobacterium avium, but it is not capable of forming lysogens in any mycobacterial host. Like TM4, all of the Cluster K phages infect both fast- and slow-growing mycobacteria, and all of them – with the exception of TM4 – form stable lysogens in both Mycobacterium smegmatis and Mycobacterium tuberculosis; immunity assays show that all five of these phages share the same immune specificity. TM4 infects these lysogens suggesting that it was either derived from a heteroimmune temperate parent or that it has acquired a virulent phenotype. We have also characterized a widely-used conditionally replicating derivative of TM4 and identified mutations conferring the temperature-sensitive phenotype. All of the Cluster K phages contain a series of well conserved 13 bp repeats associated with the translation initiation sites of a subset of the genes; approximately one half of these contain an additional sequence feature composed of imperfectly conserved 17 bp inverted repeats separated by a variable spacer. The K1 phages integrate into the host tmRNA and the Cluster K phages represent potential new tools for the genetics of M. tuberculosis and related species

Advancing Research on Racial–Ethnic Health Disparities: Improving Measurement Equivalence in Studies with Diverse Samples

To conduct meaningful, epidemiologic research on racial–ethnic health disparities, racial–ethnic samples must be rendered equivalent on other social status and contextual variables via statistical controls of those extraneous factors. The racial–ethnic groups must also be equally familiar with and have similar responses to the methods and measures used to collect health data, must have equal opportunity to participate in the research, and must be equally representative of their respective populations. In the absence of such measurement equivalence, studies of racial–ethnic health disparities are confounded by a plethora of unmeasured, uncontrolled correlates of race–ethnicity. Those correlates render the samples, methods, and measures incomparable across racial–ethnic groups, and diminish the ability to attribute health differences discovered to race–ethnicity vs. to its correlates. This paper reviews the non-equivalent yet normative samples, methodologies and measures used in epidemiologic studies of racial–ethnic health disparities, and provides concrete suggestions for improving sample, method, and scalar measurement equivalence