In today's rapidly evolving technological landscape, organizations face the challenge of integrating external insights into their decision-making processes to stay competitive. To address this issue, this study proposes a method that combines topic modeling, expert knowledge inputs, and reinforcement learning (RL) to enhance the detection of technological changes. The method has four main steps: (1) Build a relevant topic model, starting with textual data like documents and reports to find key themes. (2) Create aspect-based topic models. Experts use curated keywords to build models that showcase key domain-specific aspects. (3) Iterative analysis and RL driven refinement: We examine metrics such as topic magnitude, similarity, entropy shifts, and how models change over time. We optimize topic selection with RL. Our reward function balances the diversity and similarity of the topics. (4) Synthesis and operational integration: Each iteration provides insights. In the final phase, the experts check these insights and reach new conclusions. These conclusions are designed for use in the firm's operational processes. The application is tested by forecasting trends in quantum communication. Results demonstrate the method's effectiveness in identifying, ranking, and tracking trends that align with expert input, providing a robust tool for exploring evolving technological landscapes. This research offers a scalable and adaptive solution for organizations to make informed strategic decisions in dynamic environments.

Topic modeling often requires examining topics from multiple perspectives to uncover hidden patterns, especially in less explored areas. This paper presents an approach to address this need, utilizing weighted keywords from various aspects derived from a domain knowledge. The research method starts with standard topic modeling. Then, it adds a process consisting of four key steps. First, it defines keywords for each aspect. Second, it gives weights to these keywords based on their relevance. Third, it calculates relevance scores for aspect-weighted keywords and topic keywords to create aspect-topic models. Fourth, it uses these scores to tune relevant new documents. Finally, the generated topic models are interpreted and validated. The findings show that top-scoring documents are more likely to be about the same aspect of a topic. This highlights the model's effectiveness in finding the related documents to the aspects.

Federated learning enables collaborative model training across multiple clients without sharing raw data, thereby enhancing privacy. However, the exchange of model updates can still expose sensitive information. Quantum teleportation, a process that transfers quantum states between distant locations without physical transmission of the particles themselves, has recently been implemented in real-world networks. This position paper explores the potential of integrating quantum teleportation into federated learning frameworks to bolster privacy. By leveraging quantum entanglement and the no-cloning theorem, quantum teleportation ensures that data remains secure during transmission, as any eavesdropping attempt would be detectable. We propose a novel architecture where quantum teleportation facilitates the secure exchange of model parameters and gradients among clients and servers. This integration aims to mitigate risks associated with data leakage and adversarial attacks inherent in classical federated learning setups. We also discuss the practical challenges of implementing such a system, including the current limitations of quantum network infrastructure and the need for hybrid quantum-classical protocols. Our analysis suggests that, despite these challenges, the convergence of quantum communication technologies and federated learning presents a promising avenue for achieving unprecedented levels of privacy in distributed machine learning.

We study the sample complexity of the prototypical tasks quantum purity estimation and quantum inner product estimation. In purity estimation, we are to estimate $tr(\rho^2)$ of an unknown quantum state $\rho$ to additive error $\epsilon$. Meanwhile, for quantum inner product estimation, Alice and Bob are to estimate $tr(\rho\sigma)$ to additive error $\epsilon$ given copies of unknown quantum state $\rho$ and $\sigma$ using classical communication and restricted quantum communication. In this paper, we show a strong connection between the sample complexity of purity estimation with bounded quantum memory and inner product estimation with bounded quantum communication and unentangled measurements. We propose a protocol that solves quantum inner product estimation with $k$-qubit one-way quantum communication and unentangled local measurements using $O(median\{1/\epsilon^2,2^{n/2}/\epsilon,2^{n-k}/\epsilon^2\})$ copies of $\rho$ and $\sigma$. Our protocol can be modified to estimate the purity of an unknown quantum state $\rho$ using $k$-qubit quantum memory with the same complexity. We prove that arbitrary protocols with $k$-qubit quantum memory that estimate purity to error $\epsilon$ require $\Omega(median\{1/\epsilon^2,2^{n/2}/\sqrt{\epsilon},2^{n-k}/\epsilon^2\})$ copies of $\rho$. This indicates the same lower bound for quantum inner product estimation with one-way $k$-qubit quantum communication and classical communication, and unentangled local measurements. For purity estimation, we further improve the lower bound to $\Omega(\max\{1/\epsilon^2,2^{n/2}/\epsilon\})$ for any protocols using an identical single-copy projection-valued measurement. Additionally, we investigate a decisional variant of quantum distributed inner product estimation without quantum communication for mixed state and provide a lower bound on the sample complexity.

To this end, quantum technologies could distributed quantum computing including quantum provide a natural embedding of privacy. To counteract machine learning (QML) [1-9], has garnered considerable the gradient inversion attack, one recent proposal [9] replaced attention due to its remarkable capability to the classical neural network in the FL model with harness the collective power of distributed quantum resources, variational quantum circuits built using expressive quantum surpassing the limitations of individual quantum feature maps such that the problem of a successful nodes. Distributed quantum computation usually involves attack is reduced to solving high-degree multivariate generating and transmitting quantum states across Chebyshev equations. Other quantum-based proposals multiple nodes leveraging the advancements in quantum include adding a certain level of noise to the gradient communication technologies [10]. Remarkably, distributed values to reduce the probability of a successful gradient quantum computing protocols offer a ray of hope inversion attack [24], leveraging blind quantum computing in addressing privacy concerns in the presence of adversaries [25], and others [26-29]. An alternative to the [10-14], while traditional classical methods have aforementioned methods is to encode the client's classical struggled to ensure the confidentiality of sensitive information gradient values into quantum states and leverage during distributed processes. These adversaries quantum communication between the clients and server not only involve third-party attacks that can be tackled to transmit the states. This provides opportunities to with well-celebrated quantum communication technologies hide the gradient values of individual clients from the such as quantum key distribution [10, 11], but also server while allowing the server to perform the model aggregation include privacy concerns with untrusted computing nodes using appropriate quantum operations on their [12, 13].

As the scale of the datasets and parameterized models used to perform computation over data continues to grow [43, 53], distributing workloads across multiple devices becomes essential for enabling progress. The choice of architecture for large-scale training and inference must not only make the best use of computational and memory resources, but also contend with the fact that communication may become a bottleneck [85]. When using modern optical interconnects, classical computers exchange bits represented by light. This however does not fully utilize the potential of the physical substrate; given suitable computational capabilities and algorithms, the quantum nature of light can be harnessed as a powerful communication resource. Here we show that for a broad class of parameterized models, if quantum bits (qubits) are communicated instead of classical bits, an exponential reduction in the communication required to perform inference and gradientbased training can be achieved. This protocol additionally guarantees improved privacy of both the user data and model parameters through natural features of quantum mechanics, without the need for additional cryptographic or privacy protocols. To our knowledge, this is the first example of generic, exponential quantum advantage on problems that occur naturally in the training and deployment of large machine learning models. These types of communication advantages help scope the future roles and interplay between quantum and classical communication for distributed machine learning.

A quantum version of data centers might be significant in the quantum era. In this paper, we introduce Quantum Data Center (QDC), a quantum version of existing classical data centers, with a specific emphasis on combining Quantum Random Access Memory (QRAM) and quantum networks. We argue that QDC will provide significant benefits to customers in terms of efficiency, security, and precision, and will be helpful for quantum computing, communication, and sensing. We investigate potential scientific and business opportunities along this novel research direction through hardware realization and possible specific applications. We show the possible impacts of QDCs in business and science, especially the machine learning and big data industries.

This work investigates the application of quantum machine learning techniques for classical and quantum communication across different qubit channel models. By employing parameterized quantum circuits and a flexible channel noise model, we develop a machine learning framework to generate quantum channel codes and evaluate their effectiveness. We explore classical, entanglement-assisted, and quantum communication scenarios within our framework. Applying it to various quantum channel models as proof of concept, we demonstrate strong performance in each case. Our results highlight the potential of quantum machine learning in advancing research on quantum communication systems, enabling a better understanding of capacity bounds under modulation constraints, various communication settings, and diverse channel models.