Optimization of a solid-state electron spin qubit using gate set tomography

State of the art qubit systems are reaching the gate fidelities required for scalable quantum computation architectures. Further improvements in the fidelity of quantum gates demands characterization and benchmarking protocols that are efficient, reliable and extremely accurate. Ideally, a benchmarking protocol should also provide information on how to rectify residual errors. Gate set tomography (GST) is one such protocol designed to give detailed characterization of as-built qubits. We implemented GST on a high-fidelity electron-spin qubit confined by a single 31P atom in 28Si. The results reveal systematic errors that a randomized benchmarking analysis could measure but not identify, whereas GST indicated the need for improved calibration of the length of the control pulses. After introducing this modification, we measured a new benchmark average gate fidelity of , an improvement on the previous value of . Furthermore, GST revealed high levels of non-Markovian noise in the system, which will need to be understood and addressed when the qubit is used within a fault-tolerant quantum computation scheme

Large tunable valley splitting in edge-free graphene quantum dots on boron nitride

Coherent manipulation of binary degrees of freedom is at the heart of modern quantum technologies. Graphene offers two binary degrees: the electron spin and the valley. Efficient spin control has been demonstrated in many solid state systems, while exploitation of the valley has only recently been started, yet without control on the single electron level. Here, we show that van-der Waals stacking of graphene onto hexagonal boron nitride offers a natural platform for valley control. We use a graphene quantum dot induced by the tip of a scanning tunneling microscope and demonstrate valley splitting that is tunable from -5 to +10 meV (including valley inversion) by sub-10-nm displacements of the quantum dot position. This boosts the range of controlled valley splitting by about one order of magnitude. The tunable inversion of spin and valley states should enable coherent superposition of these degrees of freedom as a first step towards graphene-based qubits

Ultrafast heating as a sufficient stimulus for magnetization reversal in a ferrimagnet.

The question of how, and how fast, magnetization can be reversed is a topic of great practical interest for the manipulation and storage of magnetic information. It is generally accepted that magnetization reversal should be driven by a stimulus represented by time-non-invariant vectors such as a magnetic field, spin-polarized electric current, or cross-product of two oscillating electric fields. However, until now it has been generally assumed that heating alone, not represented as a vector at all, cannot result in a deterministic reversal of magnetization, although it may assist this process. Here we show numerically and demonstrate experimentally a novel mechanism of deterministic magnetization reversal in a ferrimagnet driven by an ultrafast heating of the medium resulting from the absorption of a sub-picosecond laser pulse without the presence of a magnetic field

Valley splitting of single-electron Si MOS quantum dots

Silicon-based metal-oxide-semiconductor quantum dots are prominent candidates for high-fidelity, manufacturable qubits. Due to silicon's band structure, additional low-energy states persist in these devices, presenting both challenges and opportunities. Although the physics governing these valley states has been the subject of intense study, quantitative agreement between experiment and theory remains elusive. Here, we present data from an experiment probing the valley states of quantum dot devices and develop a theory that is in quantitative agreement with both this and a recently reported experiment. Through sampling millions of realistic cases of interface roughness, our method provides evidence that the valley physics between the two samples is essentially the same.J.K.G. gratefully acknowledges support from the Sandia National Laboratories Truman Fellowship Program, which is funded by the Laboratory Directed Research and Development (LDRD) program. This work was performed, in part, at the Center for Integrated Nanotechnologies, an Office of Science User Facility operated for the U.S. Department of Energy (DOE) Office of Science. C.H.Y. and A.S.D. acknowledge support from the Australian Research Council (CE11E0001017), the U.S. Army Research Office (W911NF-13-1-0024) and the NSW Node of the Australian National Fabrication Facility. A.R. acknowledges support from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 654712 (SINHOPSI)

Obesity and renal cell cancer – a quantitative review

Obesity has been associated with an increased risk of renal cell cancer among women, while the evidence for men is considered weaker. We conducted a quantitative summary analysis to evaluate the existing evidence that obesity increases the risk of renal cell cancer both among men and women. We identified all studies examining body weight in relation to kidney cancer, available in MEDLINE from 1966 to 1998. The quantitative summary analysis was limited to studies assessing obesity as body mass index (BMI, kg m−2), or equivalent. The risk estimates and the confidence intervals were extracted from the individual studies, and a mixed effect weighted regression model was used. We identified 22 unique studies on each sex, and the quantitative analysis included 14 studies on men and women, respectively. The summary relative risk estimate was 1.07 (95% CI 1.05–1.09) per unit of increase in BMI (corresponding to 3 kg body weight increase for a subject of average height). We found no evidence of effect modification by sex. Our quantitative summary shows that increased BMI is equally strongly associated with an increased risk of renal cell cancer among men and women. © 2001 Cancer Research Campaignhttp://www.bjcancer.co

Two-electron spin correlations in precision placed donors in silicon

Substitutional donor atoms in silicon are promising qubits for quantum computation with extremely long relaxation and dephasing times demonstrated. One of the critical challenges of scaling these systems is determining inter-donor distances to achieve controllable wavefunction overlap while at the same time performing high fidelity spin readout on each qubit. Here we achieve such a device by means of scanning tunnelling microscopy lithography. We measure anti-correlated spin states between two donor-based spin qubits in silicon separated by 16 ± 1 nm. By utilising an asymmetric system with two phosphorus donors at one qubit site and one on the other (2P−1P), we demonstrate that the exchange interaction can be turned on and off via electrical control of two in-plane phosphorus doped detuning gates. We determine the tunnel coupling between the 2P−1P system to be 200 MHz and provide a roadmap for the observation of two-electron coherent exchange oscillations

Intermittent Screening and Treatment versus Intermittent Preventive Treatment of Malaria in Pregnancy: A Randomised Controlled Non-Inferiority Trial

BACKGROUND: The effectiveness of intermittent preventive treatment of malaria in pregnancy (IPTp) may be compromised by the spread of resistance to sulphadoxine/pyrimethamine (SP) across Africa. But little information exists on alternative drugs for IPTp or alternative strategies for the prevention of malaria in pregnancy. Therefore, we have investigated whether screening with a rapid diagnostic test and treatment of those who are positive (IST) at routine antenatal clinic attendances is as effective and as safe as SP-IPTp in pregnant women. METHODS AND FINDINGS: During antenatal clinic sessions in six health facilities in Ghana held between March 2007 and September 2007, 3333 pregnant women who satisfied inclusion criteria were randomised into three intervention arms (1) standard SP-IPTp, (2) IST and treatment with SP or (3) IST and treatment with amodiaquine+artesunate (AQ+AS). All women received a long-lasting insecticide treated net. Study women had a maximum of three scheduled follow-up visits following enrollment. Haemoglobin concentration and peripheral parasitaemia were assessed between 36 and 40 weeks of gestation. Birth weight was measured at delivery or within 72 hours for babies delivered at home. Parasite prevalence at enrollment in primigravidae and in multigravidae was 29.6% and 10.2% respectively. At 36-40 weeks of gestation the prevalence of asymptomatic parasitaemia was 12.1% in study women overall and was very similar in all treatment groups. The risk of third trimester severe anaemia or low birth weight did not differ significantly between the three treatment groups regardless of gravidity. IST with AQ+AS or SP was not inferior to SP-IPTp in reducing the risk of low birth weight (RD  =  -1.17[95%CI; -4.39-1.02] for IST-SP vs. SP-IPTp and RD = 0.78[95%CI; -2.11-3.68] for IST-AQAS vs. SP-IPTp); third trimester severe anaemia (RD = 0.29[95%CI; -0.69-1.30] for IST-SP vs. SP-IPTp and RD  =  -0.36[95%CI;-1.12-0.44] for IST-AQAS vs. SP-IPTp). CONCLUSION: The results of this study suggest that in an area of moderately high malaria transmission, IST with SP or AS+AQ may be a safe and effective strategy for the control of malaria in pregnancy. However, it is important that these encouraging findings are confirmed in other geographical areas and that the impact of IST on placental malaria is investigated. TRIAL REGISTRATION: ClinicalTrials.gov NCT00432367 [NCT00432367]

Using healthcare systems data for outcomes in clinical trials: issues to consider at the design stage.

BACKGROUND: Healthcare system data (HSD) are increasingly used in clinical trials, augmenting or replacing traditional methods of collecting outcome data. This study, PRIMORANT, set out to identify, in the UK context, issues to be considered before the decision to use HSD for outcome data in a clinical trial is finalised, a methodological question prioritised by the clinical trials community. METHODS: The PRIMORANT study had three phases. First, an initial workshop was held to scope the issues faced by trialists when considering whether to use HSDs for trial outcomes. Second, a consultation exercise was undertaken with clinical trials unit (CTU) staff, trialists, methodologists, clinicians, funding panels and data providers. Third, a final discussion workshop was held, at which the results of the consultation were fed back, case studies presented, and issues considered in small breakout groups. RESULTS: Key topics included in the consultation process were the validity of outcome data, timeliness of data capture, internal pilots, data-sharing, practical issues, and decision-making. A majority of consultation respondents (n = 78, 95%) considered the development of guidance for trialists to be feasible. Guidance was developed following the discussion workshop, for the five broad areas of terminology, feasibility, internal pilots, onward data sharing, and data archiving. CONCLUSIONS: We provide guidance to inform decisions about whether or not to use HSDs for outcomes, and if so, to assist trialists in working with registries and other HSD providers to improve the design and delivery of trials

Prospective validation of the 4C prognostic models for adults hospitalised with COVID-19 using the ISARIC WHO Clinical Characterisation Protocol

Purpose: To prospectively validate two risk scores to predict mortality (4C Mortality) and in-hospital deterioration (4C Deterioration) among adults hospitalised with COVID-19. // Methods: Prospective observational cohort study of adults (age ≥18 years) with confirmed or highly suspected COVID-19 recruited into the International Severe Acute Respiratory and emerging Infections Consortium (ISARIC) WHO Clinical Characterisation Protocol UK (CCP-UK) study in 306 hospitals across England, Scotland and Wales. Patients were recruited between 27 August 2020 and 17 February 2021, with at least 4 weeks follow-up before final data extraction. The main outcome measures were discrimination and calibration of models for in-hospital deterioration (defined as any requirement of ventilatory support or critical care, or death) and mortality, incorporating predefined subgroups. // Results: 76 588 participants were included, of whom 27 352 (37.4%) deteriorated and 12 581 (17.4%) died. Both the 4C Mortality (0.78 (0.77 to 0.78)) and 4C Deterioration scores (pooled C-statistic 0.76 (95% CI 0.75 to 0.77)) demonstrated consistent discrimination across all nine National Health Service regions, with similar performance metrics to the original validation cohorts. Calibration remained stable (4C Mortality: pooled slope 1.09, pooled calibration-in-the-large 0.12; 4C Deterioration: 1.00, –0.04), with no need for temporal recalibration during the second UK pandemic wave of hospital admissions. // Conclusion: Both 4C risk stratification models demonstrate consistent performance to predict clinical deterioration and mortality in a large prospective second wave validation cohort of UK patients. Despite recent advances in the treatment and management of adults hospitalised with COVID-19, both scores can continue to inform clinical decision making

Development and validation of the ISARIC 4C Deterioration model for adults hospitalised with COVID-19: a prospective cohort study

BACKGROUND: Prognostic models to predict the risk of clinical deterioration in acute COVID-19 cases are urgently required to inform clinical management decisions. METHODS: We developed and validated a multivariable logistic regression model for in-hospital clinical deterioration (defined as any requirement of ventilatory support or critical care, or death) among consecutively hospitalised adults with highly suspected or confirmed COVID-19 who were prospectively recruited to the International Severe Acute Respiratory and Emerging Infections Consortium Coronavirus Clinical Characterisation Consortium (ISARIC4C) study across 260 hospitals in England, Scotland, and Wales. Candidate predictors that were specified a priori were considered for inclusion in the model on the basis of previous prognostic scores and emerging literature describing routinely measured biomarkers associated with COVID-19 prognosis. We used internal-external cross-validation to evaluate discrimination, calibration, and clinical utility across eight National Health Service (NHS) regions in the development cohort. We further validated the final model in held-out data from an additional NHS region (London). FINDINGS: 74 944 participants (recruited between Feb 6 and Aug 26, 2020) were included, of whom 31 924 (43·2%) of 73 948 with available outcomes met the composite clinical deterioration outcome. In internal-external cross-validation in the development cohort of 66 705 participants, the selected model (comprising 11 predictors routinely measured at the point of hospital admission) showed consistent discrimination, calibration, and clinical utility across all eight NHS regions. In held-out data from London (n=8239), the model showed a similarly consistent performance (C-statistic 0·77 [95% CI 0·76 to 0·78]; calibration-in-the-large 0·00 [-0·05 to 0·05]); calibration slope 0·96 [0·91 to 1·01]), and greater net benefit than any other reproducible prognostic model. INTERPRETATION: The 4C Deterioration model has strong potential for clinical utility and generalisability to predict clinical deterioration and inform decision making among adults hospitalised with COVID-19. FUNDING: National Institute for Health Research (NIHR), UK Medical Research Council, Wellcome Trust, Department for International Development, Bill & Melinda Gates Foundation, EU Platform for European Preparedness Against (Re-)emerging Epidemics, NIHR Health Protection Research Unit (HPRU) in Emerging and Zoonotic Infections at University of Liverpool, NIHR HPRU in Respiratory Infections at Imperial College London