Processing Unit Research Articles

Life cycle assessment and energy return of investment of nutritionally-enhanced snacks supplemented with Spanish quinoa

The trend in the consumption of unconventional, more nutritious foods is leading to the globalization and decentralization of their production, giving rise to the adaptation and innovation of traditional products to make them healthier and more sustainable. This article focuses on quinoa and aims to estimate the environmental impacts of its production adapted to the Spanish conditions and of potential derived snacks enriched with this pseudo cereal by applying conventional and nutritional life cycle assessment (LCA) methodologies. Besides, an exhaustive study of the energy flows by measuring the cumulative energy demand and the calculation of the energy return of investment (EROI) is carried out to assess the most impactful aspect of the processing industries. The application of cradle to gate LCA revealed that polluting emissions of Spanish quinoa are rather similar to those of the Andean grain, with an impact on climate change of 1.03 kg CO2 eq./kg. However, high resource footprints were obtained, for instance a water deprivation potential of 60 m3/kg due to the scarcity in this country. Besides, the consideration of a nutrient profile model as functional unit led to the conclusion that quinoa-based snacks are generally more environmentally sustainable than their conventional counterparts in terms of climate change, resources consumption or water degradation. EROI scores were relatively low for all options, with only between 1.77 and 4.35 % of the energy invested returned, which evidences the unsustainable agricultural practices and low efficiency of processing units. Based on this research, producers can reorient production systems in support of nature, and consumers are able to guide their choices towards improved eating patterns.

CGH calculation algorithm for expressing reflection on a curved mirror surface

Rendering techniques are important in computer-generated holograms (CGHs) for expressing various types of holo-realistic 3D images. Rendering techniques such as hidden surface removal and reflection by a planar mirror have been proposed thus far, but reflection on a curved mirror surface has yet to be achieved. In this study, we propose a calculation algorithm that can express the reflection of the surrounding environment on a mirror surface defined as a Bézier surface. The results of optical experiments demonstrate that the proposed algorithm enables reflection on such a mirror surface and that the calculation can be accelerated by using a graphics processing unit (GPU).

High recovery design of reverse osmosis process with high permeate water quality and low wastewater discharge for ultra-pure water production

Ultra-pure water (UPW) for electronic industry is produced via many unit processes including reverse osmosis (RO), pretreatments, membrane degasifier, and ion exchange processes. Generally, 2-pass RO system is adopted in UPW production process to remove ionic and organic matters to meet water quality requirements of post-treatments. Since RO concentrate occupies most of wastewater discharged from UPW production process, high recovery design is beneficial to reduce wastewater discharge. According to the solution-diffusion model, permeate concentrations increase at higher feed concentrations, which may limit to increase RO recovery. Interestingly, the bench-scale 2-pass RO test using five commercial membrane modules revealed permeate concentrations were not affected by feed concentrations below a critical value (35 mg/L as NaCl in this work). This means the first-pass RO recovery can be increased without decreasing permeate water quality as far as second-pass RO feed concentrations are below the critical values. Various 2-pass UPW-RO system designs (250 mg/L as NaCl of feed concentration) were analyzed using an industrial RO system simulator to investigate the effect of the first- and second-pass recovery. The simulation works found that increasing the first- and second-pass recovery decreased wastewater discharge, energy consumption, and the number of membrane modules without degrading permeate water quality.

Rotation-invariant image recognition using interconnected floating-gate phototransistor

Rotational invariance is fundamental for robust image recognition systems, ensuring accurate analysis irrespective of image orientation. However, existing systems predominantly reliant on software often encounter challenges such as increased computational demands and compromises between processing speed and accuracy. In this study, we propose leveraging the interconnected floating-gate (FG) structure as an effective hardware-level solution to achieve rotational invariance in image recognition. Our design features a reconfigurable two-dimensional material FG phototransistor array, where each processing unit integrates four sensory devices sharing a common FG. This configuration facilitates uniform distribution of stored charges across the interconnected FG layer, which is typically made of metal, enabling consistent application of a single weight matrix to images across varied rotational conditions. The photoactive material, tungsten diselenide (WSe2), possesses a distinctive bipolar property that facilitates both hole and electron tunneling into the FG layer. This property directly contributes to the efficiency of state transition within the setup and improves its overall adaptability. In this manner, our design achieves stable and predictable outputs in recognizing identical digital numbers regardless of their rotation, while also demonstrating variable performance essential for accurately distinguishing between different digital numbers. This dual capability guarantees both the adaptability and precision required for rotation-invariant image recognition, suggesting that our work may open up a promising venue for exploring advanced hardware designs, such as optimized interconnected FG architectures, tailored for enhancing recognition accuracy and efficiency in the field of intelligent visual systems.

Cubic q-Bézier Triangular Patch for Scattered Data Interpolation and Its Algorithm

This paper presents an approach to scattered data interpolation using q-Bézier triangular patches via an efficient algorithm. While existing studies have formed q-Bézier triangular patches through convex combination, their application to scattered data interpolation has not been previously explored. Therefore, this study aims to extend the use of q-Bézier triangular patches to scattered data interpolation by achieving C1 continuity throughout the data points. We test the proposed scheme using both established data points and real-life engineering problems. We compared the performance of the proposed interpolation scheme with a well-known existing scheme by varying the q parameter. The comparison was based on visualization and error analysis. Numerical and graphical results were generated using MATLAB. The findings indicate that the proposed scheme outperforms the existing scheme, demonstrating a higher coefficient of determination (R2), smaller root mean square error (RMSE), and faster central processing unit (CPU) time. These results highlight the potential of the proposed q-Bézier triangular patches scheme for more accurate and reliable scattered data interpolation via the proposed algorithm.

Kinetic Study and Process Optimization of Plutonium Barrier Units for Enhanced Plutonium Stripping in the PUREX Process

In the PUREX (the plutonium uranium reduction extraction) process, a plutonium barrier unit (1BXX) is used to achieve deep plutonium stripping. According to the operating experience of the French reprocessing plant, after the separation of uranium and plutonium in the first cycle (1B + 1BXX), the plutonium barrier unit has excellent stripping effect, such that the removal of plutonium from uranium can already be achieved in the first cycle, and the second cycle only needs to focus on the removal of neptunium from uranium in order to obtain a qualified uranium product. In recent decades, China has also been actively conducting research on the plutonium barrier unit process to reduce the plutonium concentration in the primary uranium product in the first cycle to avoid the need to remove neptunium and plutonium at the same time in the second cycle, and to improve the efficiency and feasibility of reprocessing. Due to the lack of design basis for plutonium barriers to achieve deep plutonium stripping at present, this study conducts a basic study on the plutonium barrier unit, aiming to provide data for the optimization of plutonium barriers in the actual reprocessing process at a later date. In this work, a kinetic study on the reduction and stripping of trace plutonium from dibutyl phosphate-containing organic phases was carried out first, and the kinetic equations for the reduction and stripping of Pu(IV) by U(IV) under flow process conditions were obtained. The effects of U(IV) addition on the extraction loss of U(IV) and the concentration distribution of U(IV) at various stages were investigated by process simulation. Additionally, the oxidation of U(IV) under process conditions was investigated to clarify the process chemistry of U(IV) oxidation and to provide a reference for the oxidation consumption of U(IV). Finally, the process parameters of the plutonium barrier unit were preliminarily designed based on the above research.

Computing the QRPA level density with the finite amplitude method

We describe a new algorithm to calculate the vibrational nuclear level density of an atomic nucleus. Fictitious perturbation operators that probe the response of the system are generated by drawing their matrix elements from some probability distribution function. We use the Finite Amplitude Method to explicitly compute the response for each such sample. With the help of the Kernel Polynomial Method, we build an estimator of the vibrational level density and provide the upper bound of the relative error in the limit of infinitely many random samples. The new algorithm can give accurate estimates of the vibrational level density. Since it is based on drawing multiple samples of perturbation operators, its computational implementation is naturally parallel and scales like the number of available processing units.

Cascaded lattice Boltzmann simulation of Newtonian and non-Newtonian mixture nanofluids with variable thermophysical properties in a cavity with vertical heat radiator

The central moments-based cascaded lattice Boltzmann method (CLBM) for then Newtonian and non-Newtonian Buongiorno’s model mixture nanofluids (CuO, ZnO, Al2O3-water) has been implemented and applied in a square chamber with a vertical heat radiator using accelerated graphics processing unit (GPU) computing through compute unified device architecture (CUDA) C/C++ platform. Due to the higher numerical stability, the CLBM is a superior numerical tool to the raw moments-based MRT-LBM (multiple-relaxation-time lattice Boltzmann method). Three different models for the viscosity and thermal conductivity of the nanofluids: (i) the Binkmann model for the constant viscosity and the Maxwell model for the constant thermal conductivity (ii) Binkmann and Maxwell model with temperature dependent Brownian motion and (iii) Corcione model with non-Newtonian fluid where the temperature and strain rate determine the nanofluid effective thermal conductivity and viscosity, have been used. The enclosure’s upper and bottom walls are thermally adiabatic, but the left and right walls are uniformly cold. A vertical heater is immersed in the middle position of the cavity. The benchmark results for non-Newtonian, Newtonian, and nanofluids for the various computational domains are used to validate the current code adequately. The Bingham number (Bn), the Rayleigh number (Ra), and The volume fraction of the nanoparticles (ϕ) are the three key parameters that are varied in this investigation to demonstrate the effects of natural convection on the isotherms, streamlines, isolines of nanoparticle volume fractionation, yielded and unyeilded zone, and average Nusselt number (Nu¯). The Brownian motion effects of the nanoparticles augmented the average rate of heat transfer and the use of the Bingham nanofluids reduced the heat transfer enhancement. For the CuO-water nanofluid, the augmentation of the rate of heat transfer is 15.42% from ϕ=0 to 4% while Ra=106 and the corresponding heat transfer enhancement for the ZnO-water nanofluid is 11.11%. For the Bingham fluid, the rate of heat transfer increases 7% from Ra=105 to Ra=106 while ϕ=2% and Bn=0.3. The findings can be applied to optimize automotive radiator systems, which are crucial for maintaining engine temperature.

Dynamics of the perceptive field size in human adults

The receptive field (RF) is the fundamental processing unit of human vision; both masking and crowding depend on its size. The RF has a psychophysical corresponding term, the perceptive field (PF); whereas the RF is measured physiologically, the PF is measured psychophysically (a perceptual response). We investigated how spatial (lateral interactions), temporal (the stimulus presentation time), and the procedure affect the PF size for both monocular and binocular viewing. The stimuli consisted of a central vertically oriented Gabor target and high-contrast Gabor flankers positioned in two configurations (orthogonal or collinear) with target-flanker separations of either 2 or 3 wavelengths (λ). We used two main methods to control the monocular and binocular vision: mono-optic glasses vs. stereo glasses. The presentation order was either mixed or non-mixed for the presentation time and the eye condition. We estimated the PF size for both monocular and binocular viewing at 4 different presentation times (40, 80,120, and 200 ms) with different orders of presentation in each experiment (mono-optic glasses vs. stereo glasses, utilizing the lateral masking paradigm). In each experiment we explored one variable: how changing one parameter would affect the PF size in both monocular and binocular viewing (the temporal duration, the testing order of conditions, and the spatial distance) while keeping the others constant. We found that both the monocular and binocular PF size were dynamic and were significantly affected by the presentation order, leading to reduced lateral suppression under the collinear 2λ condition. Hence, both the monocular and binocular PF size depended on the sequence of the stimulus presentation time and the testing order of the conditions. Furthermore, we found that the binocular PF size was significantly larger than the monocular PF size.

Local and Low-Cost Filter Media of Simple Water Filtration

Commonly the Installation of a Drinking Water Supply System (SPAM) in Indonesia consists of Coagulation-Flocculation, Sedimentation, Filtration and Disinfection Processes as a complete Process Unit for surface feed water such as rivers or lakes. Similarly, the clean water treatment process in Banjarbaru, South Kalimantan, Indonesia uses intake water sources from irrigation. The intake water from irrigation used by the local Regional Drinking Water Company has good quality with a low turbidity value below the quality standards set by the central government. Based on these qualities, the right process unit to treat the water is by filtration only. Therefore, it is necessary to conduct evaluation and research to improve the efficiency of the process unit and the economic value of the water treatment. The purpose of this study is to investigate and evaluate irrigation feed water treatment by filtration using local and low-cost filter media. The filter reactor is designed with a rapid media filter type. The media is characterized using energy dispersive X-ray spectroscopy (EDS) and a scanning electron microscope (SEM) which is plugged in with EDS to find out the primary constituents and morphological information and after knowing the characterization of the media, and followed by tested the media placed in the water filter reactor with data collection. The results show that the size of the filter media has great effect on the quality of the processing water, which the smaller the media diameter conducted high selectivity and speed up filtration time. The greatest result obtained from testing feed water with media is 87.64%. Turbidity of the feed water sample process, which was previously 0.34 NTU, could be decreased to 0.02 NTU. Effective size of 0.4 mm and uniformity coefficient of 1.35 mm are the optimal allowable values.

Increasing the Measured Effective Quantum Volume with Zero Noise Extrapolation

Quantum volume is a full-stack benchmark for near-term quantum computers. It quantifies the largest size of a square circuit which can be executed on the target device with reasonable fidelity. Error mitigation is a set of techniques intended to remove the effects of noise present in the computation of noisy quantum computers when computing an expectation value of interest. Effective quantum volume is a proposed metric that applies error mitigation to the quantum volume protocol to evaluate the effectiveness not only of the target device but also of the error mitigation algorithm. Digital zero-noise extrapolation is an error mitigation technique that estimates the noiseless expectation value using circuit folding to amplify errors by known scale factors and then extrapolating computed expectation values to the zero-noise limit. Here we demonstrate that zero-noise extrapolation, with global and local unitary folding with fractional scale factors, in conjunction with dynamical decoupling, can increase the effective quantum volume over the vendor-measured quantum volume. Specifically, we measure the effective quantum volume of four IBM Quantum superconducting processor units, obtaining values that are larger than the vendor-measured quantum volume on each device. This is the first such increase reported.

Enhanced seagull optimization for enhanced accuracy in CUDA-accelerated Levenberg–Marquardt backpropagation neural networks for earthquake forecasting

Hyperparameter tuning is crucial for enhancing the accuracy and reliability of artificial neural networks (ANNs). This study presents an optimization of the Levenberg–Marquardt backpropagation neural network (LM-BPNN) by integrating an improved seagull optimization algorithm (ISOA). The proposed ISOA-LM-BPNN model is designed to forecast earthquakes in the Caribbean region. The study further explores the impact of data and model parallelism, revealing that hybrid parallelism effectively mitigates the limitations of both. This leads to substantial gains in throughput and overall performance. To address computational demands, this model leverages the compute unified device architecture (CUDA) framework, enabling hybrid parallelism on graphics processing units (GPUs). This approach significantly enhances the model’s computational speed. The experimental results demonstrate that the ISOA-LM-BPNN model achieves a 20% improvement in accuracy compared to four baseline algorithms across three diverse datasets. The integration of ISOA with LM-BPNN refines the neural network’s hyperparameters, leading to more precise earthquake predictions. Additionally, the model’s computational efficiency is evidenced by a 56% speed increase when utilizing a single GPU, and an even greater acceleration with dual GPUs connected via NVLink compared to traditional CPU-based computations. The findings underscore the potential of ISOA-LM-BPNN as a robust tool for earthquake forecasting, combining high accuracy with enhanced computational speed, making it suitable for real-time applications in seismic monitoring and early warning systems.

Multi-Stream Scheduling of Inference Pipelines on Edge Devices - a DRL Approach

Low-power edge devices equipped with Graphics Processing Units (GPUs) are a popular target platform for real-time scheduling of inference pipelines. Such application-architecture combinations are popular in Advanced Driver-assistance Systems for aiding in the real-time decision-making of automotive controllers. However, the real-time throughput sustainable by such inference pipelines is limited by resource constraints of the target edge devices. Modern GPUs, both in edge devices and workstation variants, support the facility of concurrent execution of computation kernels and data transfers using the primitive of streams , also allowing for the assignment of priority to these streams. This opens up the possibility of executing computation layers of inference pipelines within a multi-priority, multi-stream environment on the GPU. However, manually co-scheduling such applications while satisfying their throughput requirement and platform memory budget may require an unmanageable number of profiling runs. In this work, we propose a Deep Reinforcement Learning (DRL)-based method for deciding the start time of various operations in each pipeline layer while optimizing the latency of execution of inference pipelines as well as memory consumption. Experimental results demonstrate the promising efficacy of the proposed DRL approach in comparison with the baseline methods, particularly in terms of real-time performance enhancements, schedulability ratio, and memory savings. We have additionally assessed the effectiveness of the proposed DRL approach using a real-time traffic simulation tool IPG CarMaker.

Thermodynamic quantum Fokker–Planck equations and their application to thermostatic Stirling engine

We developed a computer code for the thermodynamic quantum Fokker–Planck equations (T-QFPE), derived from a thermodynamic system–bath model. This model consists of an anharmonic subsystem coupled to multiple Ohmic baths at different temperatures, which are connected to or disconnected from the subsystem as a function of time. The code numerically integrates the T-QFPE and their classical expression to simulate isothermal, isentropic, thermostatic, and entropic processes in both quantum and classical cases. The accuracy of the results was verified by comparing the analytical solutions of the Brownian oscillator. In addition, we illustrated a breakdown of the Markovian Lindblad-master equation in the pure quantum regime. As a demonstration, we simulated a thermostatic Stirling engine employed to develop non-equilibrium thermodynamics [S. Koyanagi and Y. Tanimura, J. Chem. Phys. 161, 114113 (2024)] under quasi-static conditions. The quasi-static thermodynamic potentials, described as intensive and extensive variables, were depicted as work diagrams. In the classical case, the work done by the external field is independent of the system–bath coupling strength. In contrast, in the quantum case, the work decreases as the coupling strength increases due to quantum entanglement between the subsystem and bath. The codes were developed for multicore processors using Open Multi-Processing (OpenMP) and for graphics processing units using the Compute Unified Device Architecture. These codes are provided in the supplementary material.

Hybrid quantum-classical computation for automatic guided vehicles scheduling

Motivated by recent efforts to develop quantum computing for practical, industrial-scale challenges, we demonstrate the effectiveness of state-of-the-art hybrid (not necessarily quantum) solvers in addressing the business-centric optimization problem of scheduling Automatic Guided Vehicles (AGVs). Some solvers can already leverage noisy intermediate-scale quantum (NISQ) devices. In our study, we utilize D-Wave hybrid solvers that implement classical heuristics with potential assistance from a quantum processing unit. This hybrid methodology performs comparably to existing classical solvers. However, due to the proprietary nature of the software, the precise contribution of quantum computation remains unclear. Our analysis focuses on a practical, business-oriented scenario: scheduling AGVs within a factory constrained by limited space, simulating a realistic production setting. Our approach maps a realistic AGVs problem onto one reminiscent of railway scheduling and demonstrates that the AGVs problem is better suited to quantum computing than its railway counterpart, the latter being denser in terms of the average number of constraints per variable. The main idea here is to highlight the potential usefulness of a hybrid approach for handling AGVs scheduling problems of practical sizes. We show that a scenario involving up to 21 AGVs, significant due to possible deadlocks, can be efficiently addressed by a hybrid solver in seconds.

Correlation mining of multimodal features based on higher-order partial least squares for emotion recognition in conversations

In fields requiring an understanding of emotions, such as digital human interaction and public opinion analysis, achieving a dependable and interpretable model for mining correlations among multimodal features remains a primary objective. However, current deep learning methods often lack transparency and suffer from low interpretability. To address these challenges, we propose a novel Correlation Mining method based on Higher-Order Partial Least Squares (HOPLS) for multimodal Emotion Recognition in conversations (CMHER) in this paper. CMHER innovatively combines HOPLS with transformers and Gated Recurrent Units (GRUs) to compute correlation matrices within unimodal data streams and between cross-modal sources. HOPLS projects source data into a latent space to predict target data via correlation matrix computations, eliminating the need for Graphical Processing Unit (GPU) acceleration and making it suitable for experimental and edge systems. The integration of HOPLS with deep neural networks involves preprocessing multimodal features into suitable dimensions and latent representations, followed by HOPLS computing correlation matrices for cross-modal latent vectors and final labels through optimal joint subspace approximation, which aims at the improvements of both interpretability and reliability. Additionally, a generalization error fitting module further refines the predicted correlation matrices to improve predictive capability and overall model performance. Experiments on two public datasets validate the superiority of our proposed method.

AI-Enhanced Smart Cooking Pot: A Culinary Companion with Intelligent Sensing

This research introduces SmartCook, an avant-garde kitchen appliance that amalgamates sensor technologies, artificial intelligence (AI), and machine learning to redefine the art of cooking. The device employs an array of sensors, including temperature, moisture, infrared, proximity, electronic tongue, and electronic nose, ingeniously positioned in the lid with foodgrade silicon transparent sealing. This design ensures a non-intrusive manner of sensing, preserving the integrity of both the food and the sensors. SmartCook operates in two modes: Cooking Mode, where experienced users store their personalized cooking patterns, and Guide Mode, designed for beginners seeking expert guidance. The AI processing unit, analysing vapours produced during cooking, intricately captures the nuances of smell, texture, timing, temperature, and moisture to create and store unique recipe patterns for each prepared meal. The addition of a texture-sensing camera placed in the lid, also sealed with foodgrade silicon, enhances the system’s capability to analyze and replicate food textures. This innovative approach transforms SmartCook into a comprehensive, guided, and customizable culinary assistant. The system’s safety mechanisms, including automatic shut-off, prioritize user safety throughout the cooking process. SmartCook revolutionizes home cooking, transforming it into an interactive, guided, and enjoyable journey. With its emphasis on replicating the unique patterns of individual cooks, SmartCook introduces a novel approach to the culinary arts, empowering users to virtually learn and recreate meals from their favourite chefs. This research presents a ground-breaking contribution to the evolving landscape of smart kitchen appliances, offering users an innovative and enjoyable way to explore the world of gastronomy.

Kajian Teknis Kinerja Jig Primer terhadap Kadar Recovery dan Looses Bijih Timah pada SHP KIP Mitra Matras di Bidang Pengolahan Mineral Unit Pengolahan PT Timah Tbk

Bidang Pengolahan Mineral Processing Unit of PT Timah Tbk operates in the field of tin ore processing. The problem at the research location is that there is no standard operating standard for the Primary Jig tool for washing Processing Waste (SHP). The aim of this research is to determine the settings for the Primary Jig variables in order to obtain optimal levels, Sn recovery and looses. The research method used is Grafity Concentration. The feed used is SHP from KIP Mitra Matras. The experiment was carried out 21 times. Each experiment used 7 variations of Primary Jig stroke speed (300 rpm, 350 rpm, 400 rpm, 450 rpm, 500 rpm, 550 rpm, 600 rpm) and Bed thickness (70 mm, 80 mm, 90 mm). Grain Counting Analysis shows a feed Sn content value of 9.96%. GCA analysis shows that the minerals in the bait are cassiterite, ilmenite, monazite, zircone, pyrite, siderite, limonite, tourmaline, quartz and iron rust. Low stroke speed results in high concentrate Sn content with low Sn recovery and little looses wasted and vice versa. A high Bed thickness results in low Sn concentrate content, high Sn recovery, and a lot of looses is wasted, and vice versa. Setting the optimal Primary Jig operating variables with variations in punch speed of 450 rpm and Bed thickness of 80 mm obtained a Sn content of 41.36% and a Sn recovery of 60.11%. Based on the results of calculating the degree of liberation, the highest average value of the degree of liberation was obtained at mesh -100, indicating that the SHP KIP Mitra Matras concentrate is classified as having a fine grain size.

Canalis: A Throughput-Optimized Framework for Real-Time Stream Processing of Wireless Communication

Stream processing, which involves real-time computation of data as it is created or received, is vital for various applications, specifically wireless communication. The evolving protocols, the requirement for high-throughput, and the challenges of handling diverse processing patterns make it demanding. Traditional platforms grapple with meeting real-time throughput and latency requirements due to large data volume, sequential and indeterministic data arrival, and variable data rates, leading to inefficiencies in memory access and parallel processing. We present Canalis, a throughput-optimized framework designed to address these challenges, ensuring high-performance while achieving low energy consumption. Canalis is a hardware-software co-designed system. It includes a programmable spatial architecture, FluxSPU (Flux Stream Processing Unit), proposed by this work to enhance data throughput and energy efficiency. FluxSPU is accompanied by a software stack that eases the programming process. We evaluated Canalis with eight distinct benchmarks. When compared to CPU and GPU in mobile SoC to demonstrate the effectiveness of domain specialization, Canalis achieves an average speedup of 13.4× and 6.6×, and energy savings of 189.8× and 283.9×, respectively. In contrast to equivalent ASICs of the benchmarks, the average energy overhead of Canalis is within 2.4×, successfully maintaining generalizations without incurring significant overhead.

Quantum-inspired clustering with light

This article introduces a novel approach to perform the simulation of a single qubit quantum-inspired algorithm using laser beams. Leveraging the polarization states of photonic qubits, and inspired by variational quantum eigensolvers, we develop a variational quantum-inspired algorithm implementing a clustering procedure following the approach proposed by some of us in SciRep 13, 13284 (2023). A key aspect of our research involves the utilization of non-orthogonal states within the photonic domain, harnessing the potential of polarization schemes to reproduce unitary circuits. By mapping these non-orthogonal states into polarization states, we achieve an efficient and versatile quantum information processing unit which serves as a clustering device for a diverse set of datasets.