Testing sequential quantum measurements: how can maximal knowledge be extracted?
Ontology highlight
ABSTRACT:
SUBMITTER: Nagali E
PROVIDER: S-EPMC3379168 | biostudies-other | 2012
REPOSITORIES: biostudies-other
Similar Datasets
Project description:Self-testing represents the strongest form of certification of a quantum system. Here, we theoretically and experimentally investigate self-testing of nonprojective quantum measurements. That is, how can one certify, from observed data only, that an uncharacterized measurement device implements a desired nonprojective positive-operator valued measure (POVM). We consider a prepare-and-measure scenario with a bound on the Hilbert space dimension and develop methods for (i) robustly self-testing extremal qubit POVMs and (ii) certifying that an uncharacterized qubit measurement is nonprojective. Our methods are robust to noise and thus applicable in practice, as we demonstrate in a photonic experiment. Specifically, we show that our experimental data imply that the implemented measurements are very close to certain ideal three- and four-outcome qubit POVMs and hence non-projective. In the latter case, the data certify a genuine four-outcome qubit POVM. Our results open interesting perspective for semi-device-independent certification of quantum devices.
| S-EPMC7164945 | biostudies-literature
Project description:Nonoverlapping sequential pattern mining, as a kind of repetitive sequential pattern mining with gap constraints, can find more valuable patterns. Traditional algorithms focused on finding all frequent patterns and found lots of redundant short patterns. However, it not only reduces the mining efficiency, but also increases the difficulty in obtaining the demand information. To reduce the frequent patterns and retain its expression ability, this paper focuses on the Nonoverlapping Maximal Sequential Pattern (NMSP) mining which refers to finding frequent patterns whose super-patterns are infrequent. In this paper, we propose an effective mining algorithm, Nettree for NMSP mining (NetNMSP), which has three key steps: calculating the support, generating the candidate patterns, and determining NMSPs. To efficiently calculate the support, NetNMSP employs the backtracking strategy to obtain a nonoverlapping occurrence from the leftmost leaf to its root with the leftmost parent node method in a Nettree. To reduce the candidate patterns, NetNMSP generates candidate patterns by the pattern join strategy. Furthermore, to determine NMSPs, NetNMSP adopts the screening method. Experiments on biological sequence datasets verify that not only does NetNMSP outperform the state-of-the-arts algorithms, but also NMSP mining has better compression performance than closed pattern mining. On sales datasets, we validate that our algorithm guarantees the best scalability on large scale datasets. Moreover, we mine NMSPs and frequent patterns in SARS-CoV-1, SARS-CoV-2 and MERS-CoV. The results show that the three viruses are similar in the short patterns but different in the long patterns. More importantly, NMSP mining is easier to find the differences between the virus sequences.
| S-EPMC8743106 | biostudies-literature
Project description:Epitaxial InAs quantum dots grown on GaAs substrate are being used in several applications ranging from quantum communications to solar cells. The growth mechanism of these dots also helps us to explore fundamental aspects of self-organized processes. Here we show that composition and strain profile of the quantum dots can be tuned by controlling in-plane density of the dots over the substrate with the help of substrate-temperature profile. The compositional profile extracted from grazing incidence x-ray measurements show substantial amount of inter-diffusion of Ga and In within the QD as a function of height in the low-density region giving rise to higher variation of lattice parameters. The QDs grown with high in-plane density show much less spread in lattice parameter giving almost flat density of In over the entire height of an average QD and much narrower photoluminescence (PL) line. The results have been verified with three different amounts of In deposition giving systematic variation of the In composition as a function of average quantum dot height and average energy of PL emission.
| S-EPMC4623704 | biostudies-other
Project description:The Holevo bound is a keystone in many applications of quantum information theory. We propose " maximal Holevo quantity for weak measurements" as the generalization of the maximal Holevo quantity which is defined by the optimal projective measurements. The scenarios that weak measurements is necessary are that only the weak measurements can be performed because for example the system is macroscopic or that one intentionally tries to do so such that the disturbance on the measured system can be controlled for example in quantum key distribution protocols. We evaluate systematically the maximal Holevo quantity for weak measurements for Bell-diagonal states and find a series of results. Furthermore, we find that weak measurements can be realized by noise and project measurements.
| S-EPMC4473702 | biostudies-other
Project description:The impact of measurement imperfections on quantum metrology protocols has not been approached in a systematic manner so far. In this work, we tackle this issue by generalising firstly the notion of quantum Fisher information to account for noisy detection, and propose tractable methods allowing for its approximate evaluation. We then show that in canonical scenarios involving N probes with local measurements undergoing readout noise, the optimal sensitivity depends crucially on the control operations allowed to counterbalance the measurement imperfections-with global control operations, the ideal sensitivity (e.g., the Heisenberg scaling) can always be recovered in the asymptotic N limit, while with local control operations the quantum-enhancement of sensitivity is constrained to a constant factor. We illustrate our findings with an example of NV-centre magnetometry, as well as schemes involving spin-1/2 probes with bit-flip errors affecting their two-outcome measurements, for which we find the input states and control unitary operations sufficient to attain the ultimate asymptotic precision.
| S-EPMC9666656 | biostudies-literature
Project description:Environment-induced decoherence has long been recognised as being of crucial importance in the study of chaos in quantum systems. In particular, the exact form and strength of the system-environment interaction play a major role in the quantum-to-classical transition of chaotic systems. In this work we focus on the effect of varying monitoring strategies, i.e. for a given decoherence model and a fixed environmental coupling, there is still freedom on how to monitor a quantum system. We show here that there is a region between the deep quantum regime and the classical limit where the choice of the monitoring parameter allows one to control the complex behaviour of the system, leading to either the emergence or suppression of chaos. Our work shows that this is a result from the interplay between quantum interference effects induced by the nonlinear dynamics and the effectiveness of the decoherence for different measurement schemes.
| S-EPMC5357801 | biostudies-literature
Project description:MotivationIn molecular biology, as in many other scientific fields, the scale of analyses is ever increasing. Often, complex Monte Carlo simulation is required, sometimes within a large-scale multiple testing setting. The resulting computational costs may be prohibitively high.ResultsWe here present MCFDR, a simple, novel algorithm for false discovery rate (FDR) modulated sequential Monte Carlo (MC) multiple hypothesis testing. The algorithm iterates between adding MC samples across tests and calculating intermediate FDR values for the collection of tests. MC sampling is stopped either by sequential MC or based on a threshold on FDR. An essential property of the algorithm is that it limits the total number of MC samples whatever the number of true null hypotheses. We show on both real and simulated data that the proposed algorithm provides large gains in computational efficiency.AvailabilityMCFDR is implemented in the Genomic HyperBrowser (http://hyperbrowser.uio.no/mcfdr), a web-based system for genome analysis. All input data and results are available and can be reproduced through a Galaxy Pages document at: http://hyperbrowser.uio.no/mcfdr/u/sandve/p/mcfdr.Contactgeirksa@ifi.uio.no.
| S-EPMC3223366 | biostudies-literature
Project description:Causal quantum theory assumes that measurements or collapses are well-defined physical processes, localized in space-time, and never give perfectly reliable outcomes and that the outcome of one measurement only influences the outcomes of others within its future light cone. Although the theory has unusual properties, it is not immediately evident that it is inconsistent with experiment to date. I discuss its implications and experimental tests.
| S-EPMC6304019 | biostudies-literature
Project description:Functional assessments of cardiovascular fitness (CVF) are needed to establish animal models of dysfunction, test the effects of novel therapeutics, and establish the cardio-metabolic phenotype of mice. In humans, the graded maximal exercise test (GXT) is a standardized diagnostic for assessing CVF and mortality risk. These tests, which consist of concurrent staged increases in running speed and inclination, provide diagnostic cardio-metabolic parameters, such as, VO2max, anaerobic threshold, and metabolic crossover. Unlike the human-GXT, published mouse treadmill tests have set, not staged, increases in inclination as speed progress until exhaustion (PXT). Additionally, they often lack multiple cardio-metabolic parameters. Here, we developed a mouse-GXT with the intent of improving mouse-exercise testing sensitivity and developing translatable parameters to assess CVF in healthy and dysfunctional mice. The mouse-GXT, like the human-GXT, incorporated staged increases in inclination, speed, and intensity; and, was designed by considering imitations of the PXT and differences between human and mouse physiology. The mouse-GXT and PXTs were both tested in healthy mice (C57BL/6J, FVBN/J) to determine their ability to identify cardio-metabolic parameters (anaerobic threshold, VO2max, metabolic crossover) observed in human-GXTs. Next, theses assays were tested on established diet-induced (obese-C57BL/6J) and genetic (cardiac isoform Casq2-/-) models of cardiovascular dysfunction. Results showed that both tests reported VO2max and provided reproducible data about performance. Only the mouse-GXT reproducibly identified anaerobic threshold, metabolic crossover, and detected impaired CVF in dysfunctional models. Our findings demonstrated that the mouse-GXT is a sensitive, non-invasive, and cost-effective method for assessing CVF in mice. This new test can be used as a functional assessment to determine the cardio-metabolic phenotype of various animal models or the effects of novel therapeutics.
| S-EPMC4747552 | biostudies-literature
Project description:Pharmacovigilance involves continually monitoring drug safety after drugs are put to market. To aid this process; algorithms for the identification of strongly correlated drug/adverse drug reaction (ADR) pairs from data sources such as adverse event reporting systems or Electronic Health Records have been developed. These methods are generally statistical in nature, and do not draw upon the large volumes of knowledge embedded in the biomedical literature. In this paper, we investigate the ability of scalable Literature Based Discovery (LBD) methods to identify side effects of pharmaceutical agents. The advantage of LBD methods is that they can provide evidence from the literature to support the plausibility of a drug/ADR association, thereby assisting human review to validate the signal, which is an essential component of pharmacovigilance. To do so, we draw upon vast repositories of knowledge that has been extracted from the biomedical literature by two Natural Language Processing tools, MetaMap and SemRep. We evaluate two LBD methods that scale comfortably to the volume of knowledge available in these repositories. Specifically, we evaluate Reflective Random Indexing (RRI), a model based on concept-level co-occurrence, and Predication-based Semantic Indexing (PSI), a model that encodes the nature of the relationship between concepts to support reasoning analogically about drug-effect relationships. An evaluation set was constructed from the Side Effect Resource 2 (SIDER2), which contains known drug/ADR relations, and models were evaluated for their ability to "rediscover" these relations. In this paper, we demonstrate that both RRI and PSI can recover known drug-adverse event associations. However, PSI performed better overall, and has the additional advantage of being able to recover the literature underlying the reasoning pathways it used to make its predictions.
| S-EPMC4261011 | biostudies-literature