Speakers

Abstract: Spectral Graph Compression For Practical Quantum Deployment In Financial Recommender Systems: The Final Step Toward A Tier-1 Implementation

This paper presents the third and final study in a research series, initially published in MDPI Computers, that directly addresses the path from theoretical quantum algorithm analysis to practical deployment at a Tier-1 financial institution. While previous papers established the quan- tum readiness of recommender algorithms and introduced initial compression techniques, this final study solves the critical deployment bottleneck: creating a compression pipeline that can recover the original high-dimensional customer data while providing recommendations consistent with customer experience. The fundamental complexity stems from financial data dimensionality—where customer vectors typically contain 50+ fields (versus 3 to 5 in e-commerce)—creating an exponentially larger solution space that makes the clustering problem substantially more challenging than conventional recommendation tasks. Building upon the foundational work in [1, 2], we introduce an enhanced spectral graph compression method that strategically expands the problem to 1024 nodes before com-

pressing to hardware-feasible dimensions. Crucially, our pipeline addresses the sampling bottleneck identified in earlier work, achieving 98% in-sample and ∼95% out-of-sample accuracy via leave-one- out cross-validation, while meeting the bank’s requirement for data recovery and recommendation consistency. This work completes the trilogy of studies needed to translate quantum advantage into operational financial systems.

Keywords: quantum computing, QAOA, financial recommender systems, spectral graph com- pression, high-dimensional clustering, quantum finance deployment

Abstract: Spontaneously formed organic-inorganic hybrid superlattices with perfect crystallinity and 2-D like electronic and optical properties

II–VI based organic–inorganic hybrid semiconductors have attracted significant attention due to their unique structural and electronic properties. Among them, superlattice-like materials with the general formula MQ(L)₀.₅ consist of ultra-thin II–VI semiconductor sheets separated by single-molecule organic spacers, forming a two-dimensional (2D)-like semiconductor within a three-dimensional (3D) framework. A notable example, β-ZnTe(en)₀.₅, features alternating two-monolayer ZnTe sheets and ethylenediamine spacers connected through strong covalent-like bonding. These materials exhibit exceptional crystallinity and remarkable long-term stability under ambient conditions for over 15 years. They also demonstrate fascinating physical properties, including strong quantum confinement leading to significant bandgap blueshift, intense excitonic absorption, large exciton binding energy, and highly anisotropic thermal and mechanical responses. With a wide excitonic bandgap and excellent p-type conductivity potential, these hybrid structures show promise for advanced applications such as transparent electronics, deep-UV sensing and lighting, room-temperature exciton-polariton devices, and memory technologies.

Abstract: No Code Quantum Computing using Qniverse

Quantum computing is emerging as a transformative technology with the potential to solve complex problems beyond the capabilities of classical systems. However, the steep learning curve associated with quantum programming, mathematical formalism, and specialized tools often limits its accessibility. This guest lecture introduces Qniverse, a no-code platform designed to simplify quantum circuit design, simulation, and experimentation without requiring extensive programming knowledge. The session will present the fundamentals of quantum computing, followed by a conceptual demonstration of how Qniverse enables users to build quantum circuits, visualize quantum states, perform simulations, and explore real-world applications through an intuitive graphical interface. Use cases in education, research, and early-stage quantum experimentation will be discussed to illustrate how no-code environments can accelerate learning and innovation. The lecture aims to democratize access to quantum computing by empowering students, researchers, and professionals to prototype and understand quantum concepts quickly. The session will also highlight best practices, current limitations, and the future potential of no-code platforms in supporting the growing quantum ecosystem.

Abstract: Finite Pulse Ratchet Effect Of Cold Atoms

Directed transport in systems driven out of equilibrium—commonly known as the ratchet effect—has attracted sustained interest due to its relevance in biological motors, nanoscale devices, and quantum transport phenomena. In ratchet systems, net particle transport can arise despite the absence of any net external bias, provided that appropriate spatiotemporal symmetries are broken. While extensive theoretical and experimental work has explored ratchet dynamics in both classical and quantum regimes, most quantum ratchet models rely on idealized delta-kicked (infinitely short) driving potentials. In contrast, experimentally generated optical lattices used to manipulate cold atoms inherently produce finite-width temporal pulses, motivating the need for more realistic theoretical treatments. In this work, we investigate the finite pulse ratchet effect in cold atom systems subjected to a spatially asymmetric optical potential and a periodic rectangular pulse train. The asymmetry of the potential is introduced through the superposition of sinusoidal spatial harmonics, which breaks inversion symmetry and enables directed transport. The temporal driving is characterized by a duty cycle (τ), defined as the ratio of pulse width to pulse period, allowing systematic exploration of finite pulse effects. The quantum nature of the system is governed by an effective Planck constant (𝓀), while the driving strength is quantified by an effective kick strength (Γ). These parameters jointly control the emergence and magnitude of ratchet currents. The system dynamics are modeled using the time-dependent Schrödinger equation, which is solved numerically via the split-operator method. This approach efficiently captures the wave-packet evolution of the cold atoms under time-dependent driving. From the evolving wave function, key observables such as the ratchet current and energy growth are computed over multiple kicks. The finite pulse train is constructed using a Fourier representation of a rectangular pulse, ensuring accurate temporal modulation of the optical potential. Our results first demonstrate that in the limit of small duty cycle (τ = 0.01), the finite-pulse model closely reproduces previously established results for delta-kicked ratchet systems. This confirms the validity of the developed numerical framework. As the duty cycle is increased, however, finite pulse effects lead to substantial modifications of the transport dynamics. In particular, the amplitude of resonance-induced ratchet currents decreases with increasing 𝓀 for larger τ, while enhanced transport is observed at lower values of 𝓀. At fixed quantum resonance points, the ratchet current increases rapidly with both the duty cycle and the effective kick strength. The system also exhibits modified resonance structures and current reversals, highlighting the sensitivity of quantum transport to finite pulse width. These findings show that finite pulse width is not merely a small correction to idealized delta-kicked models but constitutes an important control parameter in quantum ratchet dynamics. The results provide a more experimentally relevant description of cold atom ratchets and offer new avenues for controlling and optimizing directed transport in driven quantum systems.

Abstract: Receiver-Centric Threat Modeling of Adversarial Noise in Continuous-Variable Quantum Communication

Abstract: The Economic System as a Scale-Free Network Governed by the Principles of Quantum Physics

It is a well-established empirical fact that the distribution of wealth and income in every society studied, presently or in the past, follows a similar pattern in that a small share of the people control a large share of wealth and income. This observation was enunciated more than 100 years ago and is known as the Pareto law. Economists have debated the causes of this inequality at length over the years. The standard analytical model of economic theory, known as the Arrow-Debreu model, provides a mathematical framework defining the conditions for a competitive general market equilibrium where supply equals demand across all markets, and where the decisions of the members of the economy lead to an efficient, consistent, and stable market equilibrium, assuming perfect competition and rational behavior among its members, but is silent as far as wealth and income inequality are concerned, thereby necessitating the development of an alternative model. Such an alternative model of the economy has been developed based on the science of scale-free networks that are implicitly governed by the principles of quantum physics and specifically statistical thermodynamics. The economy is modeled as a scale free network comprised of nodes representing actors/people that are linked by multiple units of wealth or income. Scale-free networks, which appear in a lot of systems such as, for example, the wide-web, display a quite distinct behavior from that of random or Gaussian networks that have been employed traditionally to describe various aspects of the economy and more specifically finance. Scale-free network are characterized by power laws and display a one-to-one correspondence with Bose-Einstein statistics of integral spin particles, and more specifically with photons (zero mass particles) described by Planck's black body radiation law. Thus, in the model of the economy as a scale-free network, the nodes or actors represent the energy levels allowed to be occupied given a total available energy, which corresponds to the total amount of wealth/income available, while the number of links represent the wealth/income per actor. The economic system reaches a state of equilibrium when its entropy is maximized and a consistent with the empirical Pareto law distribution of wealth/income is obtained. A fundamental tenet of the economy as a scale-free network consistent with its physical description as a system of occupied energy states (nodes or actors) with varying energy levels (number of links of wealth/income per actor) is that all actors have equal access to the system. Moreover, there is a finite, i.e., non-zero, amount of wealth or income (link size) that is designated as the quantum of wealth or income. Under these conditions the resulting distribution of wealth and of income is optimal in the sense of the most efficient (maximum entropy) distribution of a given amount of wealth/income among the actors comprising the economy and corresponds to a Pareto index is two. However, optimal does not mean equal and a certain degree of inequality is unavoidable and necessary to ensure that the economic system displays a hierarchical structure and stability. The "invisible hand" of Adam Smith that is also invoked in the Arrow-Debreu model is none other than the manifestation of the state of maximum entropy in the scale-free network model of the economy. An analytical model of the Pareto index is derived based on the quantum of wealth/ income along with the introduction of the "temperature" of the economy, which is a concept related to the average number of links per node in the system of the economy, and the equipartition of wealth/income in direct analogy to that of energy in statistical thermodynamics. The introduced parameters of the quantum of wealth/income and temperature are easily understood indicators of the state of the economy compared to the rather esoteric Pareto index and Gini coefficient indicators. A comparison of the actual wealth and income distributions for the four largest economies in the world shows that in most instances, particularly regarding wealth, significant deviations from the optimal values, i.e., excessive inequality, exist. This suggests that equal opportunity or access is lacking, specifically regarding education, health, housing, institutions, and regulations, and is a manifestation of the "human hand" at work, whether well-intentioned but misguided or else deliberate. Thus, societies, rather than trying to fix the symptoms of excessive inequality by forcing inefficiently the allocation of scarce resources, should strive instead to ensure equal access and opportunity of all its members to the economy.

Abstract: Federated Learning for Secure and Efficient Network Slicing in 6G Edge-Cloud Continuum

Privacy risks and scalability limitations in centralized AI training pose significant challenges for optimizing next-generation 6G networks. To address these issues, this paper proposes a federated learning–based framework for secure and intelligent network slicing across the edge–cloud continuum, integrated with Software-Defined Networking (SDN) and Network Function Virtualization (NFV) for dynamic orchestration. The proposed approach enables distributed model training on slice-specific data without sharing raw information, supporting heterogeneous 6G service requirements such as ultra-reliable low-latency communications (URLLC) and massive machine-type communications (mMTC). Key challenges, including model aggregation under heterogeneous network conditions and robustness against poisoning attacks, are systematically addressed. Simulation results demonstrate improved slicing efficiency, reduced end-to-end latency, and enhanced privacy preservation, highlighting the suitability of the proposed framework for AI-native 6G deployments with massive device connectivity.

Abstract: Tars Cloud: An Agentic Framework for Automated Orchestration of Heterogeneous Quantum-Classical Workloads

It is a cloud-native, agent-driven orchestration framework designed to automate the execution and management of heterogeneous quantum–classical workloads in the Noisy Intermediate-Scale Quantum (NISQ) era. The framework addresses key limitations of existing quantum cloud platforms, including manual workload decomposition, static resource allocation, and provider-specific execution models. Tars Cloud integrates artificial intelligence–based agents to enable end-to-end automation across the quantum workload lifecycle, from code validation and resource estimation to backend selection, scheduling, execution monitoring, and result post-processing. The system incorporates automated static analysis to extract quantum kernels and workload characteristics from user-submitted programs, followed by bounded simulation to detect execution inconsistencies prior to deployment on real quantum hardware. Resource estimation and backend profiling are performed using multi-objective utility functions that account for qubit capacity, circuit depth, noise parameters, queue latency, execution cost, and fidelity constraints. An agent-based scheduling mechanism dynamically maps hybrid workloads to appropriate classical and quantum resources, while a plugin-based abstraction layer enables interoperability across gate- based and annealing paradigms. Execution monitoring agents support backend-aware error mitigation and structured result aggregation with full provenance metadata. Tars Cloud is designed with scalability, security, and usability as core principles, enabling provider-agnostic orchestration and reproducible execution workflows. The framework provides a practical engineering foundation for Quantum-as-a-Service platforms and demonstrates how agent- driven automation can lower the barrier to quantum experimentation while optimizing resource utilization in cloud-based quantum environments.

Abstract: Comparing the Pre-Early and Early Stages of Quantum Computing Adoption: A Qualitative Study Based on Expert Interviews

Quantum computing is an innovative technology with the potential to fundamentally extend existing computational paradigms by leveraging the principles of quantum mechanics. However, there is limited systematic understanding of how adoption logic evolves over time and across stages. In particular, few studies have closely examined the diffusion of emerging technologies such as quantum computing by focusing on the transition from the pre-early stage to the early stage of adoption. Accordingly, this study aims to distinguish between the pre-early and early stages of quantum computing adoption and to identify differences in the key factors and perceptual structures that influence firms’ adoption intentions at each stage. Building on prior research that examined determinants of firms’ intentions to adopt quantum computing, this study conducts a longitudinal investigation by carrying out follow-up interviews and surveys with the same participants two years after the initial study. Grounded in innovation diffusion theory and prior literature on emerging technology adoption, the study analyzes how firms’ technological perceptions, expectation levels, and assessments of technological maturity—as well as organizational and environmental factors—exhibit both change and persistence over time. In addition, by examining the gap between initial adoption intentions and actual levels of adoption readiness, this study seeks to elucidate the evolving decision-making mechanisms underlying the adoption of quantum computing.

Abstract: Building Quantum Computing Research Centers: A Strategic Framework for Universities

This presentation provides a comprehensive roadmap for establishing quantum computing research centers within university settings, emphasizing the strategic imperative for academic institutions to lead in the emerging quantum era. Authored by a leading expert in quantum cybersecurity and AI-driven solutions, the content outlines the foundational principles of quantum computing—including qubits, superposition, entanglement, and coherence—as the technological "engine" for future innovation. It details the critical infrastructure requirements for quantum centers, such as vibration isolation, cryogenic systems, and specialized hardware platforms like superconducting and trapped ion modalities. The presentation further highlights transformative applications across domains like computational biology, artificial intelligence, and cryptography, underscoring the potential for quantum technologies to solve previously intractable problems. A key focus is the role of universities in workforce development, interdisciplinary research, and strategic leadership, proposing structured academic programs, industry partnerships, and inclusive cultural initiatives. A use-case of the Center of Excellence in Quantum and Intelligent Computing (QIC-PSU) is presented as a model for implementation, outlining a phased strategic plan that integrates education, post-quantum security, and immersive learning tools. Ultimately, the presentation argues that proactive engagement in quantum computing is essential for universities to define the future of technology, drive economic competitiveness, and responsibly navigate the profound societal shifts brought about by the quantum revolution.