An exact effective two-qubit gate in a chain of three spins

We show that an effective two-qubit gate can be obtained from the free evolution of three spins in a chain with nearest neighbor XY coupling, without local manipulations. This gate acts on the two remote spins and leaves the mediating spin unchanged. It can be used to perfectly transfer an arbitrary quantum state from the first spin to the last spin or to simultaneously communicate one classical bit in each direction. One ebit can be generated in half of the time for state transfer. For longer spin chains, we present methods to create or transfer entanglement between the two end spins in half of the time required for quantum state transfer, given tunable coupling strength and local magnetic field. We also examine imperfect state transfer through a homogeneous XY chain.Comment: RevTeX4, 7 pages, 4 figue

Hybrid Subconvexity Bound for L(12,Sym2f⊗ρ)L\left(\frac{1}{2},\mathrm{Sym}^2 f\otimes\rho\right) via the Delta Method

Let $P$ be a prime and $k$ be an even integer. Let $f$ be a full level holomorphic cusp form of weight $k$ and $\rho$ be a primitive level $P$ holomorphic cusp form with arbitrary nebentypus and fixed weight $\kappa$. We prove a hybrid subconvexity bound for $L\left(\frac{1}{2},\mathrm{Sym}^2 f\otimes \rho\right)$ when $P^{\frac{1}{4}+\eta}<k<P^{\frac{21}{17}-\eta}$ for any $0<\eta<\frac{67}{136}$. This extends the range of $P$ and $k$ achieved by Holowinsky, Munshi and Qi. The result is established using a new variant of the delta method

High-order dynamic Bayesian network learning with hidden common causes for causal gene regulatory network

Background: Inferring gene regulatory network (GRN) has been an important topic in Bioinformatics. Many computational methods infer the GRN from high-throughput expression data. Due to the presence of time delays in the regulatory relationships, High-Order Dynamic Bayesian Network (HO-DBN) is a good model of GRN. However, previous GRN inference methods assume causal sufficiency, i.e. no unobserved common cause. This assumption is convenient but unrealistic, because it is possible that relevant factors have not even been conceived of and therefore un-measured. Therefore an inference method that also handles hidden common cause(s) is highly desirable. Also, previous methods for discovering hidden common causes either do not handle multi-step time delays or restrict that the parents of hidden common causes are not observed genes. Results: We have developed a discrete HO-DBN learning algorithm that can infer also hidden common cause(s) from discrete time series expression data, with some assumptions on the conditional distribution, but is less restrictive than previous methods. We assume that each hidden variable has only observed variables as children and parents, with at least two children and possibly no parents. We also make the simplifying assumption that children of hidden variable(s) are not linked to each other. Moreover, our proposed algorithm can also utilize multiple short time series (not necessarily of the same length), as long time series are difficult to obtain. Conclusions: We have performed extensive experiments using synthetic data on GRNs of size up to 100, with up to 10 hidden nodes. Experiment results show that our proposed algorithm can recover the causal GRNs adequately given the incomplete data. Using the limited real expression data and small subnetworks of the YEASTRACT network, we have also demonstrated the potential of our algorithm on real data, though more time series expression data is needed

Morbidity after surgical management of cervical cancer in low and middle income countries: A systematic review and meta-analysis

Objective: To investigate morbidity for patients after the primary surgical management of cervical cancer in low and middle-income countries (LMIC). Methods: The Pubmed, Cochrane, the Cochrane Central Register of Controlled Trials, Embase, LILACS and CINAHL were searched for published studies from 1st Jan 2000 to 30th June 2017 reporting outcomes of surgical management of cervical cancer in LMIC. Randomeffects meta-analytical models were used to calculate pooled estimates of surgical complications including blood transfusions, ureteric, bladder, bowel, vascular and nerve injury, fistulae and thromboembolic events. Secondary outcomes included five-year progression free (PFS) and overall survival (OS). Findings: Data were available for 46 studies, including 10,847 patients from 11 middle income countries. Pooled estimates were: blood transfusion 29% (95%CI 0.19–0.41, P = 0.00, I 2 = 97.81), nerve injury 1% (95%CI 0.00–0.03, I 2 77.80, P = 0.00), bowel injury, 0.5% (95%CI 0.01–0.01, I 2 = 0.00, P = 0.77), bladder injury 1% (95%CI 0.01–0.02, P = 0.10, I 2 = 32.2), ureteric injury 1% (95%CI 0.01–0.01, I 2 0.00, P = 0.64), vascular injury 2% (95% CI 0.01– 0.03, I 2 60.22, P = 0.00), fistula 2% (95%CI 0.01–0.03, I 2 = 77.32, P = 0.00,), pulmonary embolism 0.4% (95%CI 0.00–0.01, I 2 26.69, P = 0.25), and infection 8% (95%CI 0.04–0.12, 2 95.72, P = 0.00). 5-year PFS was 83% for laparotomy, 84% for laparoscopy and OS was 85% for laparotomy cases and 80% for laparoscopy. Conclusion: This is the first systematic review and meta-analysis of surgical morbidity in cervical cancer in LMIC, which highlights the limitations of the current data and provides a benchmark for future health services research and policy implementation