arXiv subjects

Michal Koren

Publications and source records attributed to Michal Koren.

5 recordsLinked to original sources

arXiv2025

Quantum-Amplified M/G/1/K Simulation: A Comparator-Controlled Framework for Arbitrary Service Distributions

Finite-capacity single-server queues with general service-time distributions form the backbone of numerous real-world systems, yet classical simulation of performance metrics such as blocking probabilities and delay becomes computationally prohibitive as service variability or required precision increases. This work presents the first coherent quantum circuit for simulating an M/G/1/K queue under arbitrary service-time laws. The circuit encodes the service distribution through a logarithmic-depth ladder of $R_y$ rotations and enforces buffer constraints via a comparator-controlled phase gate, while preserving the quadratic speed-up of amplitude amplification. Grover iterations center on estimating the expected number of customers in the system, yielding provable $O(\sqrt{N})$ variance reduction and closed-form confidence bounds, where $N$ denotes the number of shots. Empirical evaluations on IBM quantum simulators across four service distributions and three traffic intensities demonstrate fidelity above 0.99 with four qubits and above 0.76 with ten qubits, with Jensen-Shannon divergence below 0.11. Waiting-time estimation errors decrease by an order of magnitude as system load approaches capacity and remain within 3% in high-traffic regimes using registers of up to 63 qubits. These results establish the first end-to-end quantum simulation framework for finite-buffer, non-Markovian queueing systems and provide a concrete foundation for quantum-accelerated performance analysis in service-oriented architectures.

quant-ph↗

arXiv2025

Quantum Markov Chains: Hub-Pruned Estimation for Fashion Recommenders

We investigate whether shallow quantum circuits can accurately reproduce the short-horizon dynamics of discrete-time Markov chains derived from fashion electronic-commerce recommendation links. Transition operators are compiled into block-encoded circuits and iterated using fixed-point oblivious amplitude amplification, and amplitude-encoded marginals are used to estimate the classical push-forward. Empirically, colour categories such as black and, to a lesser extent, white function as high-degree hubs that dominate probability flow. Consequently, we assess three chain variants: the full network including all colours, a network without black, and a network without black and white, to quantify the effect of hub pruning under realistic circuit depths and measurement budgets. Across networks aggregated from multiple retailers, hub pruning consistently improves quantum and classical agreement at shallow depth; total-variation distance and Kullback-Leibler divergence typically decrease by approximately a factor of two relative to the full network, while state fidelities remain close to unity. A bias-and-contraction analysis explains these gains through reduced cross-terms and an effectively widened spectral gap. The results identify hub-pruned block-encodings as a practical heuristic for near-term experiments on recommendation dynamics using small quantum registers, and they provide a reproducible benchmarking protocol that reports total-variation distance, Kullback-Leibler divergence, and state fidelity as functions of circuit depth and prediction horizon.

quant-ph↗

arXiv2025

UAMDP: Uncertainty-Aware Markov Decision Process for Risk-Constrained Reinforcement Learning from Probabilistic Forecasts

Sequential decisions in volatile, high-stakes settings require more than maximizing expected return; they require principled uncertainty management. This paper presents the Uncertainty-Aware Markov Decision Process (UAMDP), a unified framework that couples Bayesian forecasting, posterior-sampling reinforcement learning, and planning under a conditional value-at-risk (CVaR) constraint. In a closed loop, the agent updates its beliefs over latent dynamics, samples plausible futures via Thompson sampling, and optimizes policies subject to preset risk tolerances. We establish regret bounds that converge to the Bayes-optimal benchmark under standard regularity conditions. We evaluate UAMDP in two domains including high-frequency equity trading and retail inventory control, both marked by structural uncertainty and economic volatility. Relative to strong deep learning baselines, UAMDP improves long-horizon forecasting accuracy (RMSE decreases by up to 25% and sMAPE by 32%), and these gains translate into economic performance: the trading Sharpe ratio rises from 1.54 to 1.74 while maximum drawdown is roughly halved. These results show that integrating calibrated probabilistic modeling, exploration aligned with posterior uncertainty, and risk-aware control yields a robust, generalizable approach to safer and more profitable sequential decision-making.

cs.LG↗

arXiv2024

Data clustering: a fundamental method in data science and management

This paper explores the critical role of data clustering in data science, emphasizing its methodologies, tools, and diverse applications. Traditional techniques, such as partitional and hierarchical clustering, are analyzed alongside advanced approaches such as data stream, density-based, graph-based, and model-based clustering for handling complex structured datasets. The paper highlights key principles underpinning clustering, outlines widely used tools and frameworks, introduces the workflow of clustering in data science, discusses challenges in practical implementation, and examines various applications of clustering. By focusing on these foundations and applications, the discussion underscores clustering's transformative potential. The paper concludes with insights into future research directions, emphasizing clustering's role in driving innovation and enabling data-driven decision-making.

cs.AI↗

arXiv2024

Quantum simulation of single-server Markovian queues: A dynamic amplification approach

Quantum computing is revolutionizing various fields, including operations research and queueing theory. This study presents a quantum method for simulating single-server Markovian (M/M/1) queues, making quantum computing more accessible to researchers in operations research. We introduce a dynamic amplification approach that adapts to queue traffic, potentially improving simulation efficiency, and design custom-parameterized quantum gates for arrival and service processes. This flexible framework enables modeling of various queueing scenarios while bridging quantum computing and classical queueing theory. Notably, our quantum method shows potential advantages over classical simulations, particularly in high-traffic scenarios. This quantum simulation approach opens new possibilities for analyzing complex queueing systems, potentially outperforming classical methods in challenging scenarios and paving the way for quantum-enhanced operations research. The method was implemented and tested across low-, moderate-, and high-traffic scenarios, comparing quantum simulations with both theoretical formulas and classical simulations. Results demonstrate high agreement between quantum computations and theoretical predictions, with relative errors below 0.002 for effective arrival rates in high-traffic scenarios. As the number of qubits increases, we observe rapid convergence to theoretical values, with relative errors decreasing by up to two orders of magnitude in some cases. Sensitivity analysis reveals optimal parameter regions yielding errors lower than 0.001.

quant-ph↗