Quantum Computing: A Bubble Ready to Burst or a Looming Breakthrough?

Stefano Patarnello

Philip Ball

As quantum computing makes its first forays from the lab to the real world, are the latest claims mere hype causing a bubble that will burst before the field finds its feet? Or are investors and researchers right to be enthusiastic about this burgeoning technological revolution? Philip Ball investigates the successes and pitfalls of commercializing quantum information technology.

Chirag Sharma, Kashvi Sood, Sandeep K. Sood

Quantum Machine Learning (QML) merges Quantum Computing (QC) and Artificial Intelligence (AI) to solve problems that are intractable by classical computers. At the same time, domains such as finance, healthcare, manufacturing, and transportation domains are transitioning into Internet of Things (IoT)-enabled cyber physical systems generating streams of heterogeneous data with high dimensionality and volume. This work presents a unified scientometric and qualitative review of QML across these sectors. A PRISMA-based screening is followed by a Keyword Co-occurrence Cluster Analysis (KCCA), a Keyword Burst Analysis (KBA), and a Citation Burst Analysis (CBA) using CiteSpace on a Scopus-derived corpus of literature. An evidence-based Technology Readiness Level (TRL) framework is introduced to assess the technology’s maturity. Our findings indicate that the volume of publications has increased after 2022, with healthcare dominating the volume and manufacturing the impact. Furthermore, a clear trend towards the development of hybrid quantum classical architectures is present across all domains. Security applications such as anomaly detection are the most developed QML entry point to IoT systems. All four domains are at a TRL of 3-4, indicating that QML research is still at the concept-validation stage. Challenges include hardware noise, barren plateaus, absence of standardized benchmarks, and encoding heterogeneous data from IoT devices for training QML applications.

Skip Sanzeri

Shengjun Xiong, Lisheng Zhou, Yuxin Xiong +1 more

This paper presents a low-computational algorithm for the check node processing of non-binary low-density parity-check (NB-LPDC) decoders, the base algorithm that we use is the extended min-sum (EMS) algorithm, which has been widely studied in the recent literature, and has been shown to approach the performance of the belief propagation algorithm, with limited complexity. This algorithm, named novel bubble check, can reduce greatly the number of bubbles to be compared, this significant computational reduction is achieved without any performance loss. This algorithm is particularly suitable for NB-LPDC decoders in super-high order fields, which have superior performance in the presence of burst errors.

Keith D. Willett