The field of quantum computing and optimization is rapidly advancing, with a focus on developing innovative methods to improve the reliability and efficiency of quantum computing systems. Researchers are exploring new approaches to reduce the impact of noise and minimize the number of two-qubit gates, which is crucial for the development of large-scale quantum computers. Additionally, there is a growing interest in using physics-inspired dynamical systems to solve computationally challenging combinatorial optimization problems. Noteworthy papers include: Optimizing Quantum Circuits via ZX Diagrams using Reinforcement Learning and Graph Neural Networks, which introduced a novel framework for quantum circuit optimization, and Context Switching for Secure Multi-programming of Near-Term Quantum Computers, which proposed a context-switching technique to defend against Zero Knowledge Tampering Attacks.
Quantum Computing and Optimization Advances
Sources
Optimizing Quantum Circuits via ZX Diagrams using Reinforcement Learning and Graph Neural Networks
Multi-Phase Coupled CMOS Ring Oscillator based Potts Machine
Comparative Analysis of Classical and Quantum-Inspired Solvers: A Preliminary Study on the Weighted Max-Cut Problem
Different Paths, Same Destination: Designing New Physics-Inspired Dynamical Systems with Engineered Stability to Minimize the Ising Hamiltonian
Quantum Combine and Conquer and Its Applications to Sublinear Quantum Convex Hull and Maxima Set Construction
Context Switching for Secure Multi-programming of Near-Term Quantum Computers