Results Summary, Recommendations and Future Work

Rajendran Saravanan, Aniruddha Chakraborty

Gregg S. Jaeger

The notion of the macroscopic in fundamental quantum theory is analyzed. After a brief summary of use of the term macroscopic, its use in quantum theory is compared with its previous use elsewhere. Next, the connections specifically to the Copenhagen interpretation of quantum mechanics and quantum measurement theory more generally, where this term first began to deviate from previous uses, are explained and exhibited through a number of examples. Then, recent attempts to define accurately and quantitatively the extent of being macroscopic, that is, macroscopicity are discussed and their implications considered. This is done most particularly in the realm of quantum optics, where it differs most from previous uses and has recently been of considerable interest. Finally, with the benefit of this analysis, recommendations are made regarding future use of the notion of the macroscopic in fundamental physics.

Haixing Miao

Thi Ha Kyaw

Honzák, Lukáš

This paper explores quantum machine learning approaches to address the cold-start problem in personalized recommendation systems. It evaluates the potential of quantum-enhanced models, including QSVM-based methods, to improve prediction accuracy in scenarios with sparse or limited user–item interactions. The study outlines experimental results, discusses key constraints of current quantum hardware, and highlights implications for future quantum recommendation engines.

Michael L. Wall