arXiv subjects

Ilkka Tittonen

Publications and source records attributed to Ilkka Tittonen.

11 recordsLinked to original sources

arXiv2026

A NISQ-friendly Coined Quantum Walk Algorithm for Chaos-based Cryptographic Applications

We present a novel lackadaisical alternating quantum walk (LAQW) algorithm whose circuit depth scales as $\mathcal{O}(n^2+nt)$ for a $n\times n$ lattice over $t$ time steps. We show that this is a significant depth reduction compared to the existing controlled alternating quantum walk (CAQW) model, which has a circuit depth that scales as $\mathcal{O}(n^2t)$ (Li et al., 2017, arXiv:1707.07389). This makes the implementation of the LAQW viable for Noisy Intermediate-scale Quantum (NISQ) devices. We then showcase the applicability of the LAQW algorithm by proposing a chaos-based symmetric-key generation scheme. Our approach uses the LAQW as a quantum entropy source from which reproducible random bitstring sequences are generated using the underlying probability distribution and subsequent post-processing methods. We provide a comprehensive evaluation of the LAQW algorithm and demonstrate the reproducibility of 128-bit keys under simulated quantum noise provided by IBM's FakeTorino backend. A direct comparison with the CAQW model, which has been used in image encryption and hash function schemes (Li et al., 2017, arXiv:1707.07389; Abd EL-Latif et al., 2020, ScienceDirect; Abd El-Latif, Abd El-Atty, and Venegas-Andraca, 2020, ScienceDirect), highlights the potential and usefulness of the LAQW model in cryptographic applications.

quant-ph↗

arXiv2026

Gate Freezing Method for Gradient-Free Variational Quantum Algorithms in Circuit Optimization

Parameterized quantum circuits (PQCs) are pivotal components of variational quantum algorithms (VQAs), which represent a promising pathway to quantum advantage in noisy intermediate-scale quantum (NISQ) devices. PQCs enable flexible encoding of quantum information through tunable quantum gates and have been successfully applied across domains such as quantum chemistry, combinatorial optimization, and quantum machine learning. Despite their potential, PQC performance on NISQ hardware is hindered by noise, decoherence, and the presence of barren plateaus, which can impede gradient-based optimization. To address these limitations, we propose novel methods for improving gradient-free optimizers Rotosolve, Fraxis, and FQS, incorporating information from previous parameter iterations. Our approach conserves computational resources by reallocating optimization efforts toward poorly optimized gates, leading to improved convergence. The experimental results demonstrate that our techniques consistently improve the performance of various optimizers, contributing to more robust and efficient PQC optimization.

quant-ph↗

arXiv2025

Improving Variational Quantum Circuit Optimization via Hybrid Algorithms and Random Axis Initialization

Variational quantum circuits (VQCs) are an essential tool in applying noisy intermediate-scale quantum computers to practical problems. VQCs are used as a central component in many algorithms, for example, in quantum machine learning, optimization, and quantum chemistry. Several methods have been developed to optimize VQCs. In this work, we enhance the performance of the well-known Rotosolve method, a gradient-free optimization algorithm specifically designed for VQCs. We develop two hybrid algorithms that combine an improved version of Rotosolve with the free quaternion selection (FQS) algorithm, which is the main focus of this study. Through numerical simulations, we observe that these hybrid algorithms achieve higher accuracy and better average performance across different ansatz circuit sizes and cost functions. For shallow variational circuits, we identify a trade-off between the expressivity of the variational ansatz and the speed of convergence to the optimum: a more expressive ansatz ultimately reaches a closer approximation to the true minimum, but at the cost of requiring more circuit evaluations for convergence. By combining the less expressive but fast-converging Rotosolve with the more expressive FQS, we construct hybrid algorithms that benefit from the rapid initial convergence of Rotosolve while leveraging the superior expressivity of FQS. As a result, these hybrid approaches outperform either method used independently.

quant-ph↗

arXiv2025

Unlocking the full potential of jumping condensation on microstructured surfaces

Water condensation on superhydrophobic surfaces can generate spontaneous droplet jumping, enabling rapid condensate removal and improved thermal and mass transfer. Although this effect has been extensively demonstrated on densely packed nanostructures, the capability of microscale textures to support jumping condensation remains poorly understood. Here, we show that engineered microscale conical arrays can achieve efficient microdroplet jumping and reveal a previously unreported spacing-dependent critical transition between jumping and non-jumping regimes. In the jumping regime, by varying only the cone pitch, we identify a geometric threshold below which sub-10 micron droplets are rapidly removed, and above which jumping is suppressed, resulting in slower dynamics and larger departing droplets. In situ optical and environmental scanning electron microscopies reveal the mechanistic origin of this transition: dense arrays favour full Cassie droplets, which depart cleanly, while wider spacing favours partial Cassie droplets that retain a localized wet region initiating new nucleation. From these results, we construct a geometry-wetting design map linking microstructure spacing, droplet morphology, and nucleation density. These findings establish design principles for scalable, mechanically robust microstructured surfaces capable of high-performance condensation management for anti-fogging, water harvesting, and heat-transfer applications.

cond-mat.soft↗

arXiv2025

Noisy Quantum Simulation: Performance and Resource Considerations for the Tavis-Cummings and Heisenberg Models

Fault-tolerant quantum computers promise the simulation of complex quantum systems beyond the reach of classical computation. In contrast, current noisy intermediate-scale quantum (NISQ) devices are constrained by hardware noise. Consequently, quantum simulation methods remain limited in their near-term applicability. Two prominent techniques addressing these challenges are zero-noise extrapolation (ZNE) and incremental structural learning (ISL). In this work, ZNE and ISL are benchmarked for simulating the Trotterized time evolution of two models: the Tavis-Cummings model (TCM) and the Heisenberg spin chain (HSC), using a classically simulated noisy hardware backend. The methods are evaluated on the basis of the accuracy of expectation values relative to noiseless simulations and their resource demands such as circuit depths and shot counts. The impact of noise on optimization routines in ISL, previously underexplored, is also investigated. Results indicate that ISL performs more favorably in HSC systems, consistently surpassing ZNE in expectation value accuracy. Conversely, for the TCM, ISL generally yields lower accuracies despite reduced Trotter circuit depths, with weak interactions often leading to pronounced phase lags or flat expectation curves. Notably, when performing ISL optimization under noiseless conditions, the protocol is generally able to reduce dephasing errors, but average accuracies still vary on the simulated Hamiltonian. Our findings highlight the sensitivity of quantum simulation protocols to the structure of the Hamiltonian encoding system dynamics. Trends across systems suggest that ISL optimization benefits from Trotter circuits with stronger interactions, and that ansatz construction favors isotropic couplings. Moreover, although ISL introduces approximation errors, it demonstrates greater robustness than ZNE in systems with deeper Trotter circuits.

quant-ph↗

arXiv2025

Enhancing Hybrid Methods in Parameterized Quantum Circuit Optimization

Parameterized quantum circuits (PQCs) play an essential role in the application of variational quantum algorithms (VQAs) in noisy intermediate-scale quantum (NISQ) devices. The PQCs are a leading candidate to achieve a quantum advantage in NISQ devices and have already been applied in various domains such as quantum chemistry, quantum machine learning, combinatorial optimization, and many others. There is no single definitive way to optimize PQCs. The most commonly used methods are based on computing the gradient via the parameter-shift rule to use classical gradient descent (GD) optimizers like Adam, stochastic GD, and others. In addition, sequential single-qubit optimizers have been proposed, such as Rotosolve, Free-Axis Selection (Fraxis), Free-Quaternion Selection (FQS), and hybrid algorithms from the aforementioned optimizers. We further develop hybrid algorithms than those represented in the previous work by drawing inspiration from the early stopping method used in classical machine learning. The switch between the optimizers depends on the previous cost function values compared to the previous ones. We introduce two new hybrid algorithms that are more robust and scalable, and they outperform previous hybrid methods in terms of convergence towards the global minima across various cost functions. In addition, we find that they are feasible for NISQ devices with different noise profiles.

quant-ph↗

arXiv2023

Nonequilibrium Green's Function simulation of Cu2O photocathodes for photoelectrochemical hydrogen production

In this work we present a simulation of the semiconductor electrodes of photoelectrochemical (PEC) water splitting cells based on the nonequilibrium Green's function (NEGF) formalism. While the performance of simple PEC cells can be adequately explained with semi-classical drift-diffusion theory, the increasing interest towards thin film cells and nanostructures in general requires theoretical treatment that can capture the quantum phenomena influencing the charge carrier dynamics in these devices. Specifically, we study a p-type Cu2O electrode and examine the influence of the bias voltage, reaction kinetics and the thickness of the Cu2O layer on the generated photocurrent. The NEGF equations are solved in a self-consistent manner with the electrostatic potential from Poisson's equation, sunlight induced photon scattering and the chemical overpotential required to drive the water splitting reaction. We show that the NEGF simulation accurately reproduces experimental results from both voltammetry and impedance spectroscopy measurements, while providing an energy resolved solution of the charge carrier densities and corresponding currents inside the semiconductor electrode at nanoscale.

cond-mat.mes-hall↗

arXiv2023

Exploring the optimality of approximate state preparation quantum circuits with a genetic algorithm

We study the approximate state preparation problem on noisy intermediate-scale quantum (NISQ) computers by applying a genetic algorithm to generate quantum circuits for state preparation. The algorithm can account for the specific characteristics of the physical machine in the evaluation of circuits, such as the native gate set and qubit connectivity. We use our genetic algorithm to optimize the circuits provided by the low-rank state preparation algorithm introduced by Araujo et al., and find substantial improvements to the fidelity in preparing Haar random states with a limited number of CNOT gates. Moreover, we observe that already for a 5-qubit quantum processor with limited qubit connectivity and significant noise levels (IBM Falcon 5T), the maximal fidelity for Haar random states is achieved by a short approximate state preparation circuit instead of the exact preparation circuit. We also present a theoretical analysis of approximate state preparation circuit complexity to motivate our findings. Our genetic algorithm for quantum circuit discovery is freely available at https://github.com/beratyenilen/qc-ga .

quant-ph↗

arXiv2022

On quantum factoring using noisy intermediate scale quantum computers

We study the performance and resource usage of the variational quantum factoring (VQF) algorithm for different instance sizes and optimization algorithms. Our simulations show better chance of finding the ground state when using VQE rather than QAOA for optimization. In gradient-based optimization we find that the time required for quantum circuit gradient estimation is a significant problem if VQF is to become competitive with classical factoring algorithms. Further, we compare entangled and non-entangled circuits in VQE optimization and fail to see significant evidence in favour of including entanglement in the VQE circuit.

quant-ph↗

arXiv2019

Large-area implementation and critical evaluation of the material and fabrication aspects of a thin-film thermoelectric generator based on aluminum-doped zinc oxide

A large-area thermoelectric generator (TEG) utilizing a folded thin-film concept is implemented and the performance evaluated for near room temperature applications having modest temperature gradients (< 50 K). The TEGs with the area of ~0.33 m^2 are shown capable of powering a wireless sensor node of multiple sensors suitable e.g. for monitoring environmental variables in buildings. The TEGs are based on a transparent, non-toxic and abundant thermoelectric material, i.e. aluminium-doped zinc oxide (AZO), deposited on flexible substrates. After folding, both the electrical current and heat flux are in the plane of the thermoelectric thin-film. Heat leakage in the folded TEG is shown to be minimal (close to that of air), enabling sufficient temperature gradients without efficient heat sinks, contrary to the conventional TEGs having the thermal flux and electrical current perpendicular to the plane of the thermoelectric films. The long-term stability studies reveal that there are no significant changes in the electrical or thermoelectric properties of AZO over several months, while the contact resistance between AZO and silver ink is an issue exhibiting a continuous increase over time. The performance of the TEGs and technological implications in relation to a state-of-the-art thermoelectric material are further assessed via a computational study.

physics.app-ph↗

arXiv2015

Broadband laser polarization control with aligned carbon nanotubes

We introduce a simple approach to fabricate aligned carbon nanotube (ACNT) device for broadband polarization control in fiber laser systems. The ACNT device was fabricated by pulling from as-fabricated vertically-aligned carbon nanotube arrays. Their anisotropic property is confirmed with optical and scanning electron microscopy, and with polarized Raman and absorption spectroscopy. The device was then integrated into fiber laser systems (at two technologically important wavelengths of 1 and 1.5 um) for polarization control. We obtained a linearly-polarized light output with the maximum extinction ratio of ~12 dB. The output polarization direction could be fully controlled by the ACNT alignment direction in both lasers. To the best of our knowledge, this is the first time that ACNT device is applied to polarization control in laser systems. Our results exhibit that the ACNT device is a simple, low-cost, and broadband polarizer to control laser polarization dynamics, for various photonic applications (such as material processing, polarization diversity detection in communications), where the linear polarization control is necessary.

physics.optics↗