arXiv subjects

Lac Nguyen

Publications and source records attributed to Lac Nguyen.

4 recordsLinked to original sources

arXiv2025

Financial Fraud Detection with Entropy Computing

We introduce CVQBoost, a novel classification algorithm that leverages early hardware implementing Quantum Computing Inc's Entropy Quantum Computing (EQC) paradigm, Dirac-3 [Nguyen et. al. arXiv:2407.04512]. We apply CVQBoost to a fraud detection test case and benchmark its performance against XGBoost, a widely utilized ML method. Running on Dirac-3, CVQBoost demonstrates a significant runtime advantage over XGBoost, which we evaluate on high-performance hardware comprising up to 48 CPUs and four NVIDIA L4 GPUs using the RAPIDS AI framework. Our results show that CVQBoost maintains competitive accuracy (measured by AUC) while significantly reducing training time, particularly as dataset size and feature complexity increase. To assess scalability, we extend our study to large synthetic datasets ranging from 1M to 70M samples, demonstrating that CVQBoost on Dirac-3 is well-suited for large-scale classification tasks. These findings position CVQBoost as a promising alternative to gradient boosting methods, offering superior scalability and efficiency for high-dimensional ML applications such as fraud detection.

cs.LG↗

arXiv2024

Entropy Computing, A Paradigm for Optimization in Open Photonic Systems

Finding better solutions to combinatorial optimization problems could have a large positive impact on many real-world application areas, such as logistics. For this reason, significant efforts have been made to design novel optimisation paradigms. Here we show an early instance of such paradigm in an optical setting, the entropy computing paradigm. Specifically, we experimentally demonstrate the feasibility of entropy computing by building a hybrid photonic-electronic computer that uses optical measurement and feedback to solve non-convex optimization problems. The system functions by using temporal photonic modes to create qudits in order to encode probability amplitudes in the time-frequency degree of freedom of a photon. This scheme, when coupled with with electronic interconnects, allows us to encode an arbitrary Hamiltonian into the system and solve non-convex continuous variables and combinatorial optimization problems. We show that the proposed entropy computing paradigm can act as a scalable and versatile platform for tackling a large range of NP-hard optimization problems.

quant-ph↗

arXiv2021

A trustless decentralized protocol for distributed consensus of public quantum random numbers

Quantum random number (QRNG) beacons distinguish themselves from classical counterparts by providing intrinsic unpredictability originating from the fundamental laws of quantum mechanics. Most demonstrations have focused on certifiable randomness generators to guarantee the public that their genuineness is independent from imperfect implementations. These efforts however do not benefit applications where multiple distrusted users need a common set of random numbers, as they must rely on the honesty of beacon owners. In this paper, we formally introduce a design and proof-of-principle experiment of the first consensus protocol producing QRNs in a decentralized environment (dQRNG). Such protocol allows N number of participants contribute in the generation process and publicly verify numbers they collect. Security of the protocol is guaranteed given(N-1) dishonest participants. Our method is thus suited for distribute systems that requires a bias-resistant, highly secure, and public-verifiable random beacon.

quant-ph↗

arXiv2018

Quantum Systems for Monte Carlo Methods and Applications to Fractional Stochastic Processes

Random numbers are a fundamental and useful resource in science and engineering with important applications in simulation, machine learning and cyber-security. Quantum systems can produce true random numbers because of the inherent randomness at the core of quantum mechanics. As a consequence, quantum random number generators are an efficient method to generate random numbers on a large scale. We study in this paper the applications of a viable source of unbiased quantum random numbers (QRNs) whose statistical properties can be arbitrarily programmed without the need for any post-processing and that pass all standard randomness tests of the NIST and Dieharder test suites without any randomness extraction. Our method is based on measuring the arrival time of single photons in shaped temporal modes that are tailored with an electro-optical modulator. The advantages of our QRNs are shown via two applications: simulation of a fractional Brownian motion, which is a non-Markovian process, and option pricing under the fractional SABR model where the stochastic volatility process is assumed to be driven by a fractional Brownian motion. The results indicate that using the same number of random units, our QRNs achieve greater accuracy than those produced by standard pseudo-random number generators. Moreover, we demonstrate the advantages of our method via an increase in computational speed, efficiency, and convergence.

quant-ph↗