Quantum Computing: 17th June 2025

Senior Thesis Spotlight: A ‘high-risk, but well-defined’ idea to advance quantum computing (princeton.edu, 2025-06-12). Thomas Verrill's senior thesis at Princeton focuses on designing and fabricating superconducting quantum computing chips, utilizing specialized materials to enhance qubit performance through innovative techniques in a clean room environment

From Abstract Puzzles to Practical Innovations: The Infinite Resistor Grid's Hidden Impact on Engineering and Quantum Technology (eliza-ng.me, 2025-06-15). Explores the infinite resistor grid's relevance to electrical engineering and quantum technology, emphasizing its implications for noise management in integrated circuits and the essential bridge between theoretical concepts and practical applications

Qubits Can Create Space, Physicists Show (backreaction.blogspot.com, 2025-06-12). Physicists demonstrate that qubits, essential for quantum computing, can play a role in the creation of space, suggesting groundbreaking implications for our understanding of quantum mechanics and spacetime

A quantum random access memory based on transmon-controlled phonon routers (phys.org, 2025-06-12). Researchers developed a quantum random access memory architecture using transmon-controlled phonon routers, enhancing data retrieval through coherent superposition, with advantages in compact design, rapid routing, and effective error detection using hybrid dual-rail encoding

Quantum Gravity Can't Violate CPT (dispatchesfromturtleisland.blogspot.com, 2025-06-16). Quantum gravity must preserve Charge-Parity-Time (CPT) symmetry across all energy scales, as shown through Renormalization Group flow calculations, ruling out models that allow CPT violation

Bonus info for Reversible Computing and Megastructures (4gravitons.com, 2025-06-13). Explores reversible computing, highlighting Vaire Computing’s resonator technology, charge recovery advancements, and the historical background of reversible programming languages like Janus, with mentions of DNA computing and its implications for future technologies

Discover Three Quantum Technologies Inspiring Projects Funded by NGI Sargasso (ngisargasso.eu, 2025-06-10). Three NGI Sargasso-funded projects explore advanced quantum technologies, including post-quantum cryptography, quantum algorithms for resource management, and quantum generative adversarial networks for anomaly detection in network traffic

Three steps to post-quantum cryptography (thoughtworks.com, 2025-06-12). Organizations must respond to the quantum threat by migrating to post-quantum cryptography (PQC) using NIST-approved schemes like CRYSTALS-Kyber and CRYSTALS-Dilithium to secure sensitive data and enhance resilience

IBM’s New Quantum Roadmap Brings the Bitcoin Threat Closer (cosmicmeta.io, 2025-06-15). IBM’s quantum roadmap targets fault-tolerant, utility-scale machines by 2029, accelerating concerns over Bitcoin security and highlighting the need for post-quantum cryptography to counter potential threats from quantum algorithms like Shor’s

Dynamic Hypergraph Partitioning of Quantum Circuits with Hybrid Execution (arxiv:cs, 2025-06-11). Dynamic partitioning and hybrid execution enhance quantum circuit performance on NISQ devices, achieving a 42.30% noise reduction and 40% fewer qubits, addressing limitations in size and noise of quantum computations

Quantum Adiabatic Generation of Human-Like Passwords (arxiv:cs, 2025-06-10). Quantum adiabatic computing utilizes QUBO and UD-MIS approaches to generate human-like passwords, yielding realistic samples like 'Tunas200992' and 'teedem28iglove' using the QuEra Aquila 256-qubit quantum computer

Adaptive Job Scheduling in Quantum Clouds Using Reinforcement Learning (arxiv:cs, 2025-06-12). A simulation-based tool for distributed quantum job scheduling uses reinforcement learning to optimize task execution on networked QPUs, addressing challenges like coherence, error susceptibility, and circuit decomposition for improved computational throughput

Low-Level and NUMA-Aware Optimization for High-Performance Quantum Simulation (arxiv:cs, 2025-06-10). An open-source QuEST simulator extension features NUMA-aware memory allocation, thread pinning, AVX-512 vectorization, and aggressive loop unrolling, achieving 5.5-6.5x speedup in single-qubit operations, enhancing classical quantum simulation capacity

Extremal Magic States from Symmetric Lattices (arxiv:math, 2025-06-13). This study connects high-dimensional symmetric lattices like $E_8$ and $E_6$ to quantum magic states, providing closed-form expressions for maximal magic states in three-qubit and one-qutrit systems and examining their entanglement structure

Bug Classification in Quantum Software: A Rule-Based Framework and Its Evaluation (arxiv:cs, 2025-06-12). A rule-based framework classifies quantum software bugs by type, category, and severity, achieving 85.21% accuracy. Analysis shows classical bugs dominate at 67.2%, with frequent compatibility and functional defects

Controlling quantum chaos via Parrondo strategies on NISQ hardware (arxiv:cs, 2025-06-12). Controlling quantum chaos in NISQ systems through discrete-time quantum walks on cyclic graphs, employing quantum Fourier transform and Parrondo's paradox strategy. Enhancements observed with dynamical decoupling pulses in 3- and 4-cycle graphs

Genetic Transformer-Assisted Quantum Neural Networks for Optimal Circuit Design (arxiv:cs, 2025-06-10). Genetic Transformer Assisted Quantum Neural Networks (GTQNNs) leverage a transformer encoder and NSGA-II genetic algorithm to optimize quantum circuit design, achieving high classification accuracy with fewer gates on benchmarks like Iris and MNIST

Superposed Parameterised Quantum Circuits (arxiv:cs, 2025-06-10). Superposed parameterised quantum circuits leverage flip-flop quantum random-access memory and polynomial activation functions, achieving significant error reduction and increased accuracy in complex regression and classification tasks compared to classical baselines

Guided Graph Compression for Quantum Graph Neural Networks (arxiv:cs, 2025-06-11). Guided Graph Compression (GGC) utilizes a graph autoencoder for effective node and feature reduction, enhancing classifier performance in high energy physics tasks, notably Jet Tagging, outperforming standalone methods and classical GNN classifiers