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"Nielsen, Erik"
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Measuring the capabilities of quantum computers
Quantum computers can now run interesting programs, but each processor’s capability—the set of programs that it can run successfully—is limited by hardware errors. These errors can be complicated, making it difficult to accurately predict a processor’s capability. Benchmarks can be used to measure capability directly, but current benchmarks have limited flexibility and scale poorly to many-qubit processors. We show how to construct scalable, efficiently verifiable benchmarks based on any program by using a technique that we call circuit mirroring. With it, we construct two flexible, scalable volumetric benchmarks based on randomized and periodically ordered programs. We use these benchmarks to map out the capabilities of twelve publicly available processors, and to measure the impact of program structure on each one. We find that standard error metrics are poor predictors of whether a program will run successfully on today’s hardware, and that current processors vary widely in their sensitivity to program structure.
Evaluations of quantum computers across architectures need reliable benchmarks. A class of benchmarks that can directly reflect the structure of any algorithm shows that different quantum computers have considerable variations in performance.
Journal Article
Fungal Enzymes Involved in Plastics Biodegradation
Plastic pollution is a growing environmental problem, in part due to the extremely stable and durable nature of this polymer. As recycling does not provide a complete solution, research has been focusing on alternative ways of degrading plastic. Fungi provide a wide array of enzymes specialized in the degradation of recalcitrant substances and are very promising candidates in the field of plastic degradation. This review examines the present literature for different fungal enzymes involved in plastic degradation, describing their characteristics, efficacy and biotechnological applications. Fungal laccases and peroxidases, generally used by fungi to degrade lignin, show good results in degrading polyethylene (PE) and polyvinyl chloride (PVC), while esterases such as cutinases and lipases were successfully used to degrade polyethylene terephthalate (PET) and polyurethane (PUR). Good results were also obtained on PUR by fungal proteases and ureases. All these enzymes were isolated from many different fungi, from both Basidiomycetes and Ascomycetes, and have shown remarkable efficiency in plastic biodegradation under laboratory conditions. Therefore, future research should focus on the interactions between the genes, proteins, metabolites and environmental conditions involved in the processes. Further steps such as the improvement in catalytic efficiency and genetic engineering could lead these enzymes to become biotechnological applications in the field of plastic degradation.
Journal Article
Using Convection-Allowing Ensembles to Understand the Predictability of an Extreme Rainfall Event
This research uses convection-allowing ensemble forecasts to address aspects of the predictability of an extreme rainfall event that occurred in south-central Texas on 25 May 2013, which was poorly predicted by operational and experimental numerical models and caused a flash flood in San Antonio that resulted in three fatalities. Most members of the ensemble had large errors in the location and magnitude of the heavy rainfall, but one member approximately reproduced the observed rainfall distribution. On a regional scale a flow-dependent diurnal cycle in ensemble spread growth is observed with large growth associated with afternoon convection, but the growth rate then reduced after convection dissipates the next morning rather than continuing to grow. Experiments that vary the magnitude of the perturbations to the initial and lateral boundary conditions reveal flow dependencies on the scales responsible for the ensemble growth and the degree to which practical (i.e., deficiencies in observing systems and numerical models) and intrinsic predictability limits (i.e., moist convective dynamic error growth) affect a particular convective event. Specifically, it was found that large-scale atmospheric forcing tends to dominate the ensemble spread evolution, but small-scale error growth can be of near-equal importance in certain convective scenarios where interaction across scales is prevalent and essential to the local precipitation processes. In a similar manner, aspects of the “upscale error growth” and “downscale error cascade” conceptual models are seen in the experiments, but neither completely explains the spread characteristics seen in the simulations.
Journal Article
Demonstration of qubit operations below a rigorous fault tolerance threshold with gate set tomography
Quantum information processors promise fast algorithms for problems inaccessible to classical computers. But since qubits are noisy and error-prone, they will depend on fault-tolerant quantum error correction (FTQEC) to compute reliably. Quantum error correction can protect against general noise if—and only if—the error in each physical qubit operation is smaller than a certain threshold. The threshold for general errors is quantified by their diamond norm. Until now, qubits have been assessed primarily by randomized benchmarking, which reports a different error rate that is not sensitive to all errors, and cannot be compared directly to diamond norm thresholds. Here we use gate set tomography to completely characterize operations on a trapped-Yb
+
-ion qubit and demonstrate with greater than 95% confidence that they satisfy a rigorous threshold for FTQEC (diamond norm ≤6.7 × 10
−4
).
Quantum computation will depend on fault-tolerant error correction, which requires the chance for errors to occur to be below a certain threshold. Here the authors use gate set tomography as a means to rigorously characterize error rates of single-qubit operations of a qubit encoded in a trapped ion.
Journal Article
Detecting and tracking drift in quantum information processors
If quantum information processors are to fulfill their potential, the diverse errors that affect them must be understood and suppressed. But errors typically fluctuate over time, and the most widely used tools for characterizing them assume static error modes and rates. This mismatch can cause unheralded failures, misidentified error modes, and wasted experimental effort. Here, we demonstrate a spectral analysis technique for resolving time dependence in quantum processors. Our method is fast, simple, and statistically sound. It can be applied to time-series data from any quantum processor experiment. We use data from simulations and trapped-ion qubit experiments to show how our method can resolve time dependence when applied to popular characterization protocols, including randomized benchmarking, gate set tomography, and Ramsey spectroscopy. In the experiments, we detect instability and localize its source, implement drift control techniques to compensate for this instability, and then demonstrate that the instability has been suppressed.
Time-dependent errors are one of the main obstacles to fully-fledged quantum information processing. Here, the authors develop a general methodology to monitor time-dependent errors, which could be used to make other characterisation protocols time-resolved, and demonstrate it on a trapped-ion qubit.
Journal Article
Dynamical Insights into Extreme Short-Term Precipitation Associated with Supercells and Mesovortices
In some prominent extreme precipitation and flash flood events, radar and rain gauge observations have suggested that the heaviest short-term rainfall accumulations (up to 177 mm h−1) were associated with supercells or mesovortices embedded within larger convective systems. In this research, we aim to identify the influence that rotation has on the storm-scale processes associated with heavy precipitation. Numerical model simulations conducted herein were inspired by a rainfall event that occurred in central Texas in October 2015 where the most extreme rainfall accumulations were collocated with meso-β-scale vortices. Five total simulations were performed to test the sensitivity of precipitation processes to rotation. A control simulation, based on a wind profile from the aforementioned event, was compared with two experiments with successively weaker low-level shear. With greater environmental low-level shear, more precipitation fell, in both a point-maximum and an area-averaged sense. Intense, rotationally induced low-level vertical accelerations associated with the dynamic nonlinear perturbation vertical pressure gradient force were found to enhance the low- to midlevel updraft strength and total vertical mass flux and allowed access to otherwise inhibited sources of moisture and CAPE in the higher-shear simulations. The dynamical accelerations, which increased with the intensity of the low-level shear, dominated over buoyant accelerations in the low levels and were responsible for inducing more intense low-level updrafts that were sustained despite a stable boundary layer.
Journal Article
Observations of Extreme Short-Term Precipitation Associated with Supercells and Mesovortices
Extreme hourly rainfall accumulations (e.g., exceeding 75 mm h−1) in several noteworthy flash flood events have suggested that the most intense accumulations were attendant with discrete mesoscale rotation or rotation embedded within larger organized systems. This research aims to explore how often extreme short-term rain rates in the United States are associated with storm-scale or mesoscale vortices. Five years of METAR observations and three years of Stage-IV analyses were obtained and filtered for hourly accumulations over 75 and 100 mm, respectively, clustered into events, and subjectively identified for rotation. The distribution of the short-term, locally extreme events shows the majority of the events were located along the Atlantic and Gulf of Mexico coastlines with additional events occurring in the central plains and into the Midwest. Nearly 50% of the cases were associated with low-level rotation in high-precipitation supercells or mesoscale vortices embedded in organized storm modes. Rotation events occurred more clearly in the warm sector, while nonrotation events tended to occur along a surface boundary. The rotation events tended to produce higher hourly accumulations over a larger region, but were associated with somewhat stronger synoptic-to-mesoscale forcing for ascent and more total column moisture. These results support recent modeling results suggesting that rotationally induced dynamic vertical pressure perturbations should not be ignored when it comes to extreme precipitation and can potentially enhance the short-term rain rates.
Journal Article
Dynamical Mechanisms Supporting Extreme Rainfall Accumulations in the Houston “Tax Day” 2016 Flood
This research examines the environmental and storm-scale characteristics of the extreme rainfall and flooding in the Houston, Texas, area on 18 April 2016, known as the “Tax Day” flood. Radar and local mesonet rain gauge observations were used to identify the locations and structures of extreme rain-rate-producing cells, with special attention given to rotating updrafts. To supplement this observation-based analysis, a WRF-ARW simulation of the Tax Day storm in 2016 was examined for the influence of any attendant rotation on both the dynamics and microphysics of the cells producing the most intense short-term (i.e., subhourly to hourly) rainfall accumulations. Results show that the most intense rainfall accumulations in the model analysis, as in the observational analysis, are associated with rotating convective elements. A lowering of the updraft base, enhancement of the low-level vertical velocities, and increased low-level rainwater production is seen in rotating updrafts, compared to those without rotation. These differences are also maintained despite increased hydrometeor loading. The results agree with the findings of previous idealized model simulations that show dynamical accelerations associated with meso-γ-scale rotation can enhance convective rainfall rates.
Journal Article
Modeling cocaine traffickers and counterdrug interdiction forces as a complex adaptive system
Counterdrug interdiction efforts designed to seize or disrupt cocaine shipments between South American source zones and US markets remain a core US “supply side” drug policy and national security strategy. However, despite a long history of US-led interdiction efforts in the Western Hemisphere, cocaine movements to the United States through Central America, or “narco-trafficking,” continue to rise. Here, we developed a spatially explicit agent-based model (ABM), called “NarcoLogic,” of narco-trafficker operational decision making in response to interdiction forces to investigate the root causes of interdiction ineffectiveness across space and time. The central premise tested was that spatial proliferation and resiliency of narco-trafficking are not a consequence of ineffective interdiction, but rather part and natural consequence of interdiction itself. Model development relied on multiple theoretical perspectives, empirical studies, media reports, and the authors’ own years of field research in the region. Parameterization and validation used the best available, authoritative data source for illicit cocaine flows. Despite inherently biased, unreliable, and/or incomplete data of a clandestine phenomenon, the model compellingly reproduced the “cat-and-mouse” dynamic between narco-traffickers and interdiction forces others have qualitatively described. The model produced qualitatively accurate and quantitatively realistic spatial and temporal patterns of cocaine trafficking in response to interdiction events. The NarcoLogic model offers a much-needed, evidence-based tool for the robust assessment of different drug policy scenarios, and their likely impact on trafficker behavior and the many collateral damages associated with the militarized war on drugs.
Journal Article