Performance analysis of multi-angle QAOA for .

In this paper we consider the scalability of multi-angle QAOA with respect to the number of QAOA layers. We found that MA-QAOA is able to significantly reduce the depth of QAOA circuits, by a factor of up to 4 for the considered data sets. Moreover, MA-QAOA is less sensitive to system size, therefore we predict that this factor will be even larger for big graphs.

Scaling quantum approximate optimization on near-term hardware.

The quantum approximate optimization algorithm (QAOA) is an approach for near-term quantum computers to potentially demonstrate computational advantage in solving combinatorial optimization problems. However, the viability of the QAOA depends on how its performance and resource requirements scale with problem size and complexity for realistic hardware implementations. Here, we quantify scaling of the expected resource requirements by synthesizing optimized circuits for hardware architectures with varying levels of connectivity.

Multi-angle quantum approximate optimization algorithm.

The quantum approximate optimization algorithm (QAOA) generates an approximate solution to combinatorial optimization problems using a variational ansatz circuit defined by parameterized layers of quantum evolution. In theory, the approximation improves with increasing ansatz depth but gate noise and circuit complexity undermine performance in practice. Here, we investigate a multi-angle ansatz for QAOA that reduces circuit depth and improves the approximation ratio by increasing the number of classical parameters.

Internal cardioversion of atrial fibrillation with a single lead configuration in a patient with massive diaphragmatic herniation.

An anatomically related circumstance is reported as indication for the internal low energy cardioversion instead of an external approach. A new single lead electrode configuration is described.

The influence of induction chemotherapy dose and dose intensity on the duration of remission in acute myeloid leukemia. Australian Leukemia Study Group.

The aim of this study was to assess the influence of dose and dose intensity (DI) of induction and consolidation chemotherapy on relapse rates in 264 de novo patients with acute nonlymphocytic leukemia (ANLL). Patients were randomised to receive cytosine arabinoside (ARAC) 100 mg/m2 continuous infusion for 7 days and daunorubicin (DNR) 50 mg/m2 IV day 1-3 (7-3) or the same drugs with the addition of etoposide 75 mg/m2 IV days 1-7 (7-3-7). Cox proportional hazards regression models were used throughout to identify prognostic factors, including dose delivery parameters, influencing the rate of relapse.