arXiv subjects

Theodoros D. Bouloumis

Publications and source records attributed to Theodoros D. Bouloumis.

6 recordsLinked to original sources

arXiv2026

Constrained Portfolio Optimization via Quantum Approximate Optimization Algorithm (QAOA) with XY-Mixers and Trotterized Initialization: A Hybrid Approach for Direct Indexing

Portfolio optimization under strict cardinality constraints is a combinatorial challenge that defies classical convex optimization techniques, particularly in the context of "Direct Indexing" and ESG-constrained mandates. In the Noisy Intermediate-Scale Quantum (NISQ) era, the Quantum Approximate Optimization Algorithm (QAOA) offers a promising hybrid approach. However, standard QAOA implementations utilizing transverse field mixers often fail to strictly enforce hard constraints, necessitating soft penalties that distort the energy landscape. This paper presents a comprehensive analysis of a constraint-preserving QAOA formulation against Simulated Annealing (SA) and Hierarchical Risk Parity (HRP). We implement a specific QAOA ansatz utilizing a Dicke state initialization and an XY-mixer Hamiltonian that strictly preserves the Hamming weight of the solution, ensuring only valid portfolios of size K are explored. Furthermore, we introduce a Trotterized parameter initialization schedule inspired by adiabatic quantum computing to mitigate the "Barren Plateau" problem. Backtesting on a basket of 10 US equities over 2025 reveals that our QAOA approach achieves a Sharpe Ratio of 1.81, significantly outperforming Simulated Annealing (1.31) and HRP (0.98). We further analyze the operational implications of the algorithm's high turnover (76.8%), discussing the trade-offs between theoretical optimality and implementation costs in institutional settings.

quant-ph↗

arXiv2026

Non-Convex Portfolio Optimization via Energy-Based Models: A Comparative Analysis Using the Thermodynamic HypergRaphical Model Library (THRML) for Index Tracking

Portfolio optimization under cardinality constraints transforms the classical Markowitz mean-variance problem from a convex quadratic problem into an NP-hard combinatorial optimization problem. This paper introduces a novel approach using THRML (Thermodynamic HypergRaphical Model Library), a JAX-based library for building and sampling probabilistic graphical models that reformulates index tracking as probabilistic inference on an Ising Hamiltonian. Unlike traditional methods that seek a single optimal solution, THRML samples from the Boltzmann distribution of high-quality portfolios using GPU-accelerated block Gibbs sampling, providing natural regularization against overfitting. We implement three key innovations: (1) dynamic coupling strength that scales inversely with market volatility (VIX), adapting diversification pressure to market regimes; (2) rebalanced bias weights prioritizing tracking quality over momentum for index replication; and (3) sector-aware post-processing ensuring institutional-grade diversification. Backtesting on a 100-stock S and P 500 universe from 2023 to 2025 demonstrates that THRML achieves 4.31 percent annualized tracking error versus 5.66 to 6.30 percent for baselines, while simultaneously generating 128.63 percent total return against the index total return of 79.61 percent. The Diebold-Mariano test confirms statistical significance with p less than 0.0001 across all comparisons. These results position energy-based models as a promising paradigm for portfolio construction, bridging statistical mechanics and quantitative finance.

q-fin.CP↗

arXiv2024

Disruptive Forces in Metamaterial Tweezers for Trapping 20 nm Nanoparticles Based on Molecular Graphene Quantum Dots

In recent years, plasmonic optical tweezers have been used to trap nanoparticles and study interactions with their environment. An unavoidable challenge is the plasmonic heating due to resonant excitation and the resulting temperature rise in the surrounding environment. In this work, we demonstrate trapping of custom-synthesized 20 nm nanoparticles based on molecular graphene quantum dots using metamaterial plasmonic tweezers. Superior trap stiffness values as high as 8.8 (fN/nm)/(mW/$μ\mbox{m}^2$) were achieved with optical intensities lower than 1 mW/$μ\mbox{m}^2$. By gradually increasing the laser intensity we identified a critical value beyond which the stiffness values dropped significantly. This value corresponded to a temperature rise of about 16$^o$C, evidently sufficient to create thermal flows and disrupt the trapping performance. We, therefore, identified a safe intensity regime for trapping nanoparticles without unwanted heat. Our platform can be used for efficient nanopositioning of fluorescent particles and quantum emitters in an array configuration, potentially acting as a single-photon source configuration.

physics.optics↗

arXiv2022

Enabling self-induced back-action trapping of gold nanoparticles in metamaterial plasmonic tweezers

The pursuit for efficient nanoparticle trapping with low powers has led to optical tweezers technology moving from the conventional free-space configuration to advanced plasmonic tweezers systems. However, trapping nanoparticles smaller than 10 nm still remains a challenge even for plasmonic tweezers. Proper nanocavity design and excitation has given rise to the self-induced back-action (SIBA) effect offering enhanced trapping stiffness with decreased laser power. In this work, we investigate the SIBA effect in metamaterial tweezers and its synergy with the exhibited Fano resonance. We demonstrate stable trapping of 20 nm gold particles for on-resonant and off-resonant conditions with experimental trap stiffnesses as high as 4.18 fN/(nm*mW/$μ$m$^2$ and very low excitation intensity of about 1 mW/$μ$m$^2$. Simulations reveal the existence of two different groups of hotspots per unit cell of the metamaterial array. The two hotspots exhibit tunable trap stiffnesses and this is a unique feature of these systems. It can allow for sorting of particles and biological molecules based on their size, shape, and refractive index.

physics.optics↗

arXiv2020

Plasmonic tweezers based on connected nanoring apertures

The manipulation of microparticles using optical forces has led to many applications in the life and physical sciences. To extend optical trapping towards the nano-regime, in this work we demonstrate trapping of single nanoparticles in arrays of plasmonic coaxial nano-apertures with various inner disk configurations and theoretically estimate the associated forces. A high normalised experimental trap stiffness of 3.50fN/nm/mW for 20nm polystyrene particles is observed for an optimum design of 149nm for the nanodisk diameter at a trapping wavelength of 980nm. Theoretical simulations are used to interpret the enhancement of the observed trap stiffness. A quick particle trapping time of less than 8sec is obtained at a concentration of 14$\times$10$^{11}$ particles/ml with low incident laser intensity of 0.59mW/$μ$m$^{2}$. This good trapping performance with fast delivery of nanoparticles to multiple trapping sites emerges from a combination of the enhanced electromagnetic near-field and spatial temperature increase. This work has applications in nanoparticle delivery and trapping with high accuracy, and bridges the gap between optical manipulation and nanofluidics.

physics.optics↗

arXiv2020

From far-field to near-field micro- and nanoparticle optical trapping

Optical tweezers is a very well-established technique that has developed into a standard tool for trapping and manipulating micron and submicron particles with great success in the last decades. Although the nature of light enforces restrictions on the minimum particle size that can be efficiently trapped due to Abbe's diffraction limit, scientists have managed to overcome this problem by engineering new devices that exploit near-field effects. Nowadays, metallic nanostructures can be fabricated which, under laser illumination, produce a secondary plasmonic field that does not suffer from the diffraction limit. This advance offers a great improvement in nanoparticle trapping, as it relaxes the trapping requirements compared to conventional optical tweezers. In this work, we review the fundamentals of conventional optical tweezers, the so-called plasmonic tweezers, and related phenomena. Starting from the conception of the idea by Arthur Ashkin until recent improvements and applications, we present some of the challenges faced by these techniques as well as their future perspectives. Emphasis in this review is on the successive improvements of the techniques and the innovative aspects that have been devised to overcome some of the main challenges.

physics.optics↗