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Polypoidal choroidal vasculopathy (PCV) is increasingly recognized as an important cause of exudative maculopathy in Asians as against Wet age-related macular degeneration in Caucasians. A panel of retinal experts methodically evaluated pertinent updated literature on PCV with thorough PubMed/MEDLINE search. Based on this, the panel agreed upon and proposed the current consensus recommendations in the diagnosis (clinical and imaging), management and follow-up schedule of PCV. Diagnosis of PCV should be based on the gold standard indocyanine green angiography which demonstrates early nodular hyperfluorescence signifying the polyp with additional features such as abnormal vascular network (AVN). Optical coherence tomography is an excellent adjuvant for diagnosing PCV, monitoring disease activity, and decision-making regarding the treatment. Current treatment modalities for PCV include photodynamic therapy, anti-vascular endothelial growth factor agents, and thermal laser. Choice of specific treatment modality and prognosis depends on multiple factors such as the location and size of PCV lesion, presence or absence of polyp with residual AVN, amount of submacular hemorrhage, presence or absence of leakage on fundus fluorescein angiography, visual acuity, and so on. Current recommendations would be invaluable for the treating physician in diagnosing PCV and in formulating the best possible individualized treatment strategy for optimal outcomes in PCV management.

The next few years will be exciting as prototype universal quantum processors emerge, enabling the implementation of a wider variety of algorithms. Of particular interest are quantum heuristics, which require experimentation on quantum hardware for their evaluation and which have the potential to significantly expand the breadth of applications for which quantum computers have an established advantage. A leading candidate is Farhi et al.’s quantum approximate optimization algorithm, which alternates between applying a cost function based Hamiltonian and a mixing Hamiltonian. Here, we extend this framework to allow alternation between more general families of operators. The essence of this extension, the quantum alternating operator ansatz, is the consideration of general parameterized families of unitaries rather than only those corresponding to the time evolution under a fixed local Hamiltonian for a time specified by the parameter. This ansatz supports the representation of a larger, and potentially more useful, set of states than the original formulation, with potential long-term impact on a broad array of application areas. For cases that call for mixing only within a desired subspace, refocusing on unitaries rather than Hamiltonians enables more efficiently implementable mixers than was possible in the original framework. Such mixers are particularly useful for optimization problems with hard constraints that must always be satisfied, defining a feasible subspace, and soft constraints whose violation we wish to minimize. More efficient implementation enables earlier experimental exploration of an alternating operator approach, in the spirit of the quantum approximate optimization algorithm, to a wide variety of approximate optimization, exact optimization, and sampling problems. In addition to introducing the quantum alternating operator ansatz, we lay out design criteria for mixing operators, detail mappings for eight problems, and provide a compendium with brief descriptions of mappings for a diverse array of problems.

This study was done to compare the results of posterior continuous curvilinear capsulorhexis created using forceps with those created using vitrector in eyes suffering from congenital cataract. Vitrectorhexis term was first used by Wilson et al in 1999. [1] Fifty eyes with congenital and developmental cataract were included in this study. The posterior capsulorhexis was created using utrata forceps in 17 eyes or through a vitrector in 33 eyes. Forceps capsulorhexis was performed before IOL implantation, while vitrectorhexis was performed after IOL implantation in the bag. The results of both the surgery were compared using the following criteria: incidence of extension of rhexis, ability to achieve posterior rhexis of appropriate size, ability to implant the IOL in the bag, the surgical time, and learning curve. Vitrectorhexis after IOL implantation was an easy to learn alternative to manual posterior continuous curvilinear capsulorhexis in pediatric cataract surgery. It was more predictable and reproducible, with a short learning curve and lesser surgical time.

Mainstream machine-learning techniques such as deep learning and probabilistic programming rely heavily on sampling from generally intractable probability distributions. There is increasing interest in the potential advantages of using quantum computing technologies as sampling engines to speed up these tasks or to make them more effective. However, some pressing challenges in state-of-the-art quantum annealers have to be overcome before we can assess their actual performance. The sparse connectivity, resulting from the local interaction between quantum bits in physical hardware implementations, is considered the most severe limitation to the quality of constructing powerful generative unsupervised machine-learning models. Here, we use embedding techniques to add redundancy to data sets, allowing us to increase the modeling capacity of quantum annealers. We illustrate our findings by training hardware-embedded graphical models on a binarized data set of handwritten digits and two synthetic data sets in experiments with up to 940 quantum bits. Our model can be trained in quantum hardware without full knowledge of the effective parameters specifying the corresponding quantum Gibbs-like distribution; therefore, this approach avoids the need to infer the effective temperature at each iteration, speeding up learning; it also mitigates the effect of noise in the control parameters, making it robust to deviations from the reference Gibbs distribution. Our approach demonstrates the feasibility of using quantum annealers for implementing generative models, and it provides a suitable framework for benchmarking these quantum technologies on machine-learning-related tasks.

Many NP-hard problems can be seen as the task of finding a ground state of a disordered highly connected Ising spin glass. If solutions are sought by means of quantum annealing, it is often necessary to represent those graphs in the annealer’s hardware by means of the graph-minor embedding technique, generating a final Hamiltonian consisting of coupled chains of ferromagnetically bound spins, whose binding energy is a free parameter. In order to investigate the effect of embedding on problems of interest, the fully connected Sherrington-Kirkpatrick model with random ±1 couplings is programmed on the D-Wave Two^{TM} annealer using up to 270 qubits interacting on a Chimera-type graph. We present the best embedding prescriptions for encoding the Sherrington-Kirkpatrick problem in the Chimera graph. The results indicate that the optimal choice of embedding parameters could be associated with the emergence of the spin-glass phase of the embedded problem, whose presence was previously uncertain. This optimal parameter setting allows the performance of the quantum annealer to compete with (and potentially outperform, in the absence of analog control errors) optimized simulated annealing algorithms.