Variational Neural-Network Ansatz for Continuum Quantum Field Theory.

Physicists dating back to Feynman have lamented the difficulties of applying the variational principle to quantum field theories. In nonrelativistic quantum field theories, the challenge is to parametrize and optimize over the infinitely many n-particle wave functions comprising the state's Fock-space representation. Here we approach this problem by introducing neural-network quantum field states, a deep learning ansatz that enables application of the variational principle to nonrelativistic quantum field theories in the continuum. Our ansatz uses the Deep Sets neural network architecture to simultaneously parametrize all of the n-particle wave functions comprising a quantum field state. We employ our ansatz to approximate ground states of various field theories, including an inhomogeneous system and a system with long-range interactions, thus demonstrating a powerful new tool for probing quantum field theories.

Analytic Theory for the Dynamics of Wide Quantum Neural Networks.

Parametrized quantum circuits can be used as quantum neural networks and have the potential to outperform their classical counterparts when trained for addressing learning problems. To date, much of the results on their performance on practical problems are heuristic in nature. In particular, the convergence rate for the training of quantum neural networks is not fully understood. Here, we analyze the dynamics of gradient descent for the training error of a class of variational quantum machine learning models. We define wide quantum neural networks as parametrized quantum circuits in the limit of a large number of qubits and variational parameters. Then, we find a simple analytic formula that captures the average behavior of their loss function and discuss the consequences of our findings. For example, for random quantum circuits, we predict and characterize an exponential decay of the residual training error as a function of the parameters of the system. Finally, we validate our analytic results with numerical experiments.

Synergistic Effect of Gefitinib and Temozolomide on U87MG Glioblastoma Angiogenesis.

One of the most common and deadly brain tumors is Glioblastoma multiforme (GBM). Due to recent advances in angiogenesis and its related key factors, this process as a hallmark in glioblastoma has attracted more consideration from the research community. Temozolomide (TMZ) as the first-line treatment used to treat GBM but, resistance to TMZ limits its effectiveness and the need for better treatments is still felt. Therefore, we aimed to examine the Synergistic effects of Gefitinib (GFI) in combination with Temozolomide on VEGF and MMPs in glioma cell line (U87MG). Our results displayed that GFI could induce cytotoxic effects in U87MG with IC50 values of 11 µM. U87MG cells produced large amounts of VEGF without any stimuli, and the results showed that GFI in combination with TMZ caused a significant decrease in VEGF production in these cells. In this study, we demonstrated that after treating with TMZ and GFI, there was more decrease in the levels of MMP 2 and 9 secretions in cells than treatment with GFI and TMZ doses alone. This study indicates synergistic effects of GFI plus TMZ against glioma are mediated by the potentiated anti-angiogenesis. Therefore, it can be considered as a promising plan for future studies.

Preparation of A Magnetic Nanosensor Based on Cobalt Ferrite Nanoparticles for The Electrochemical Determination of Methyldopa in The Presence of Uric Acid.

AIM AND OBJECTIVE: Methyldopa is one of the medications that is used for the treatment of hypertension. Therefore, the determination of methyldopa in the presence of other biological components is essential. In this work, a promising electrochemical sensor based on CoFe2O4 magnetic nanoparticles modified glassy carbon electrode (CoFe2O4/GCE) was developed for electrochemical determination of methyldopa in the presence of uric acid. Cobalt ferrite nanoparticles were synthesized via chemical method. MATERIALS AND METHODS: Characterizing the CoFe2O4 was investigated by field emission scanning electron microscopy (FESEM), X-ray diffraction (XRD), energy dispersive X-ray spectroscopy (EDX), transmission electron microscope (TEM), and cyclic voltammetry techniques. RESULTS: Under the optimal experimental conditions, the current response of the electrochemical sensor obtained with differential pulse voltammetry was increased linearly in the concentration range from 1.45 to 15.1 µmol L-1 with the detection limit of 1.07 µmol L-1 for methyldopa. Also, by using the proposed method, methyldopa and uric acid could be analyzed in a mixture independently. The difference in peak potential for analytes is about 150 mV. CONCLUSION: The present sensor was successfully applied for the determination of methyldopa in the presence of uric acid in biological samples and the pharmaceutical samples with satisfactory results.

Toward Long-Range Entanglement between Electrically Driven Single-Molecule Magnets.

Over the past two decades, several molecules have been explored as possible building blocks of a quantum computer, a device that would provide exponential speedups for a number of problems, including the simulation of large, strongly correlated chemical systems. Achieving strong interactions and entanglement between molecular qubits remains an outstanding challenge. Here, we show that the TbPc2 single-molecule magnet has the potential to overcome this obstacle because of its sensitivity to electric fields stemming from the hyperfine Stark effect. We show how this feature can be leveraged to achieve long-range entanglement between pairs of molecules using a superconducting resonator as a mediator. Our results suggest that the molecule-resonator interaction is near the edge of the strong-coupling regime and could potentially pass into it given a more detailed, quantitative understanding of the TbPc2 molecule.