Quantum annealing (QA) is a quantum algorithm used to solve optimization problems, gaining attention for its potential in combinatorial optimization. It operates on the adiabatic quantum computing (AQC) framework, introduced in 2000. A recent study suggests that the Quantum Approximate Optimization Algorithm (QAOA), inspired by QA, may offer a quantum advantage over classical algorithms. The study also introduces a parametrized version of QA, enabling a precise 1-local analysis of the algorithm. The researchers suggest that schedule optimization could further improve QA's performance, highligh ..read more

Quantum annealing (QA) is a computational method that uses quantum mechanics to solve complex optimization problems. However, a challenge in QA is verifying the adiabaticity of a system. Researchers from Japan's National Institute of Advanced Industrial Science and Technology have proposed a method to evaluate the adiabatic condition during QA without the need to diagonalize the Hamiltonian. The method involves using an oscillating field during QA to measure the transition matrix element and the energy gap. This could improve the performance of QA and contribute significantly to the field of q ..read more

A study by researchers from the Chinese Academy of Sciences has explored the impact of growth temperature on InGaN/GaN quantum well (QW) layers, which are used in LEDs and other optoelectronic devices. The team found that within a certain range, growth temperature can control the efficiency of indium incorporation into QWs and strain energy in the QW structure, affecting luminescence efficiency. However, too high an annealing temperature can increase indium migration, reducing luminescence intensity. The findings highlight the importance of thermal management in optimizing the performance of I ..read more

Perovskite quantum dot (PQD) solar cells, specifically CsPbI3PQD, have shown significant potential for use in optoelectronic devices due to their extraordinary photo-electrical properties. The power conversion efficiency (PCE) of these solar cells can exceed 16% through optimization strategies. A key challenge in fabricating high-performance flexible CsPbI3PQD solar cells is the preparation of an electron transport layer (ETL) capable of forming high-quality thin films at low temperatures. A recent study designed a customized ETL using a UV sintering method, achieving a PCE of 12.70%, the high ..read more

Quantum annealing (QA), a computational paradigm used to solve combinatorial optimization problems, faces challenges such as vanishing gaps during the anneal. To address this, researchers have proposed using Bayesian optimization (BO) to design high-quality QA schedules. BO is a global optimization strategy that is cost-effective, making it suitable for optimizing quantum annealing schedules. The research shows that BO can design QA schedules that result in significantly better fidelities than standard protocols. It also demonstrates that BO can improve the design of hybrid quantum algorithms ..read more

A research team from Zentrum für Optische Quantentechnologien Universität Hamburg, Department of Physics and Research Center OPTIMAS RheinlandPfälzische Technische Universität KaiserslauternLandau, and The Hamburg Centre for Ultrafast Imaging Universität Hamburg has developed a nonunitdisk framework to solve combinatorial optimization problems on a Rydberg quantum annealer. The study, titled "Solving optimization problems with local lightshift encoding on Rydberg quantum annealers," uses a setup of a many-body interacting Rydberg system where locally controllable light shifts are applied to in ..read more

Quantum annealing is a quantum algorithm used to solve complex optimization problems. It involves transitioning a system from an initial Hamiltonian to a problem Hamiltonian, with the qubit states read out as classical bits at the end of the anneal. Minor embedding is a crucial part of this process, allowing logical problems to be programmed onto the hardware. A new method, the 4Clique network minor embedding, has been developed to optimize problems onto sparse quantum annealing hardware graphs. This method, researched by Elijah Pelofske from the Los Alamos National Laboratory, increases chain ..read more

Researchers have proposed a local application of quantum-trained Support Vector Machines (SVMs) to overcome the training set size limits of current quantum annealers. The team, including researchers from the University of Trento, Italy, the Jülich Supercomputing Centre in Germany, and the University of Iceland, interfaced the Fast Local Kernel Support Vector Machine (FaLKSVM) method with two quantum-trained SVM models. The empirical evaluation, conducted using D-Wave's quantum annealers and real-world datasets from the remote sensing domain, demonstrated the effectiveness, scalability, and pra ..read more

Researchers from various German universities have developed a Quantum Field Theory (QFT) for multipolar composite bosons with mass defect and relativistic corrections. The study by Tobias Asano, Enno Giese, and Fabio Di Pumpo presents a subspace effective field theory for interacting spin-carrying and possibly charged ensembles of atoms that form composite bosons. The theory includes relativistic corrections to the energy of a single composite boson, light-matter interaction, and the scattering potential between composite bosons. The findings could have significant implications for atomic high ..read more

Researchers from the University of Southern California and Washington University have developed a superconducting circuit device that can actively dissipate energy on demand. The device can reset a superconducting qubit's readout cavity after a measurement, removing photons at a high rate, and can also dampen and cool the cavity, eliminating thermal photon fluctuations. This could significantly improve the performance of superconducting qubits, a promising quantum computing technology, and aid in the implementation of fault-tolerant error correction schemes. The device represents a major advan ..read more