#VQC

[govindhtech]

Variational Quantum Circuit Framework for Multi-Chip Ensemble

Variable Quantum Circuit

The multi-chip ensemble Variational Quantum Circuit (VQC) framework was established to handle major Quantum Machine Learning (QML) difficulties, especially those caused by Noisy Intermediate-Scale Quantum (NISQ) devices. These include noise, scaling issues, and desolate plateaus.

The multi-chip ensemble VQC system partitions high-dimensional calculations among numerous smaller quantum chips and classically aggregates their measurements. In contrast to this modular method, typical single-chip VQCs compute on a single, bigger quantum circuit.

VQC framework architecture:

A tiny l-qubit quantum subcircuit is in each of the framework’s k disjoint quantum chips. These constitute a larger n-qubit quantum system (n = k × l). There are no gates linking chips, therefore the quantum action is a tensor product of subcircuit actions.

Processing data:

From input data, a high-dimensional vector x, subvectors are produced. Each subvector xi is processed by a separate quantum circuit Ui on a quantum device. Each chip encodes data into a quantum state using a unitary Vi.

Classical Aggregation:

Each chip’s quantum computation is measured. Classically aggregating the classical outputs from each chip using a combination function g yields the model’s final output. This function may be a weighted sum for regression or a shallow neural network for classification.

Training:

The framework remains hybrid quantum-classical. The parameters θ for each subcircuit are tuned collectively to decrease the total loss function. The ability to calculate gradients individually and in parallel for each subcircuit makes training efficient even with several subcircuits. The framework uses parameter-shift rule for backpropagation-based end-to-end training.

Multi-Chip Ensemble VQC Advantages:

Multi-chip ensemble Variational Quantum Circuit (VQC) frameworks have many advantages over single-chip VQCs, notably for NISQ restrictions.

Increased Scalability: It allows high-dimensional data analysis without classical dimension reduction or exponentially deep circuits, which can lose information. Instead than using larger chips, horizontal scalability is achieved by adding more chips that process data. Current NISQ devices with few qubits per chip can employ this method. Better Trainability: The architecture instantly addresses the bleak plateau. It limits entanglement to within-chip boundaries to avoid barren plateaus from global entanglement patterns. According to theoretical analysis and experimental results, partitioning into many chips greatly enhances gradient variation compared to a fully-entangled single-chip solution. The framework reduces barren plateaus without being classically simulable. If l grows with the system size (n) to avoid polynomial subspaces, simulating each subcircuit may become exponentially expensive. Since inter-chip entanglement is absent, the system cannot approximate a global 2-design, reducing exponential gradient degradation. Controlled entanglement provides implicit regularisation for better generalisability. Restricting global entanglement reduces overfitting by limiting the model’s ability to describe complex functions. Navigating the quantum bias-variance trade-off improves the model’s generalisation performance.

The architectural layout reduces quantum error variation and bias, improving noise resilience. When chips have limited operations, qubits are exposed to noise for less time. Classically averaging uncorrelated noise across chip outputs reduces total variance. For this dual reduction, the bias-variance trade-off of typical error mitigation schemes is avoided.

The multi-chip ensemble Variational Quantum Circuit (VQC) framework is compatible with both new modular quantum architectures and NISQ devices. It solves hardware issues including sparse connectivity, coherence time, and qubit count by spreading computations and using classical aggregate instead of noisy inter-chip quantum transmission. The architecture supports IBM, IonQ, and Rigetti’s modular systems and quantum interconnects.

The framework has been validated through experiments utilising genuine noise models (amplitude-damping and depolarising noise) to simulate NISQ settings and confirm effectiveness. These tests used benchmark datasets (MNIST, FashionMNIST, and CIFAR-10) and PhysioNet EEG. Multi-chip ensemble VQCs surpassed single-chip VQCs in performance, speed, convergence, generalisation loss, and quantum errors, especially when processing high-dimensional data without conventional dimension reduction. Using 272 chips with 12 qubits each to apply the multi-chip ensemble approach to a quantum convolutional neural network (QCNN) on 3264-dimensional PhysioNet EEG data yielded better accuracy and less overfitting than single-chip QCNNs and CNNs.

Conclusion

In conclusion, the multi-chip ensemble Variational Quantum Circuit (VQC) framework improves QML model scalability, trainability, generalisability, and noise resilience on near-term quantum hardware by using a modular, distributed architecture with classical output aggregation and controlled entanglement.

Read more on Govindhtech.com

[gutachter]

VQC AG stellte Insolvenzantrag

Für die VQC AG, welche Qualitätskontrollen am Bau durchführt, wurde am 1. September 2024 ein Insolvenzverfahren eröffnet. Insolvenzverwalter ist Rechtsanwalt Moritz-K. Polonius, c/o KMP Rechtsanw.- und. Steuerber. GmbB, (Osterbekstraße 90b, 22083 Hamburg)…. Quellend Volltext: diebewertung.de

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[totoshappylife]

Data Encoding for VQC in Qiskit, A Comparison

Excerpt from PDF: Data Encoding for VQC in Qiskit, A Comparison With Novel Hybrid Encoding Hillol Biswas Department of Electrical and Computer Engineering, Democritus University of Thrace, Xanthi 67100, Greece Abstract—If quantum machine learning emulates the ways of classical machine learning, data encoding in a quantum neural network is imperative for many reasons. One of the key ones is the…

[tirupatitourpackages]

Special Darshan for Senior Citizens & Differently-Abled at Tirupati 🙏

Tirumala Tirupati Devasthanams (TTD) offers a dedicated darshan facility for senior citizens (65+ years) and differently-abled devotees to ensure a comfortable and quick visit. With limited slots per day, devotees must carry valid ID proof and report at the Vaikuntam Queue Complex (VQC-2) for a hassle-free experience. Seek Lord Venkateswara’s blessings with ease!

🌟 Tip: Arrive early for smooth entry and a peaceful darshan!

[jeganj]

SRIVANI Break Darshan – Online, Tirumala & Airport Booking

Introduction:

Tirupati, the spiritual capital of Andhra Pradesh, is home to the revered Sri Venkateswara Temple in Tirumala. Devotees from all over the world visit this sacred shrine to seek the blessings of Lord Balaji. To enhance the pilgrimage experience, Padmavathi Travels offers exclusive services, including Srivani VIP Break Darshan, Tirupati Airport Offline Darshan Tickets, and Two Day Tirupati Tour Packages. With priority access, hassle-free travel, and expert guidance, our services ensure a divine and stress-free journey.

Experience the Srivani VIP Break Darshan

The Srivani VIP Break Darshan is a premium privilege extended by the Tirumala Tirupati Devasthanams (TTD) for devotees who contribute to the Sri Venkateswara Aalayala Nirmanam Trust (SRIVANI). This initiative allows devotees to enjoy a faster and more exclusive darshan experience while supporting the construction and renovation of Hindu temples.

Benefits of Srivani VIP Break Darshan

Priority Access: Skip long queues with direct entry.

Exclusive Experience: Stand closer to the deity for a spiritually fulfilling darshan.

Additional Offerings: Receive Aarthi, Thirtham, Jadari, Prasadam, and a Laddu.

How to Avail the Srivani VIP Break Darshan

Donation: Contribute a minimum of ₹10,000 to the SRIVANI Trust via the official TTD website.

Booking: After the donation, select a suitable date and time slot. Additional charges of ₹300-₹500 may apply.

Darshan Day: Report at the Vaikuntam Queue Complex (VQC) at ATC Circle by 5:30 AM. Follow the dress code and carry valid ID proof.

Eligibility for Tirupati Airport Offline Tickets

Must have a valid boarding pass for an inbound flight to Tirupati Airport (TIR).

Offline tickets are issued only on the day of arrival.

Aadhaar card or government ID proof required for verification.

Tickets are distributed on a first-come, first-served basis.

Only 100 tickets are issued daily to control crowd flow.

Why Choose Tirupati Airport Offline Tickets?

Exclusive for Air Travelers: Ensures last-minute access to Special Entry Darshan.

Hassle-Free Process: Avoids the need for advance online booking.

Direct Access to Tirumala: No competition with other transport users.

Steps to Get Tirupati Airport Darshan Tickets

Arrive at Tirupati Airport and go to the designated ticket counter.

Present your boarding pass and valid ID proof.

Complete registration and receive your Special Entry Darshan ticket.

Travel to Tirumala Temple and experience a blissful darshan.

Two Day Tirupati Tour Package with Offline VVIP Darshan

For a seamless and comfortable pilgrimage, Padmavathi Travels offers a Two Day Tirupati Tour Package. This package covers major temples and ensures a stress-free VIP Darshan experience.

Travel Details:

Day 1:

Departure from Chennai at 7:00 AM.

Reach Tirupati and collect darshan tickets from the JEO Office in Tirumala.

Check for hotel availability in Tirumala or Tirupati.

Overnight stay at Tirupati.

Day 2:

Early morning pickup at 6:00 AM for VIP Darshan at Tirumala.

After darshan, return to Chennai by evening.

Pickup & Drop Locations

Available within Chennai city limits.

Why Choose Offline VVIP Darshan at Tirupati?

Guaranteed Entry with Minimal Waiting

Hassle-Free Priority Access

Special Arrangements for Senior Citizens & Differently-Abled Pilgrims

Personalized Guidance for a Smooth Darshan

Tirupati Car Rental Packages from Chennai

Pricing & Vehicle Options:

Etios / Swift Dzire (4+1 Seater) – ₹11,000

Ertiga / Innova (6+1 & 7+1 Seater) – ₹16,000

Innova Crysta (6+1 & 7+1 Seater) – ₹17,000

Tempo Traveller (12+1 Seater) – ₹19,500

Tour Highlights:

Pickup Time: 5:00 AM

Drop Time: 10:00 PM

Cab Options: Swift Dzire / Etios A/C

Temples Covered:

Tirumala Balaji Temple

Alamelumangapuram Padmavathi Temple

Varahaswamy Temple

Hanuman Temple

Govindarajan Temple

Kapila Theertham

Dress Code for Pilgrims

To maintain the spiritual sanctity, devotees must adhere to traditional attire:

Men: Dhoti and shirt or Kurtha and Pyjama.

Women: Saree, Half Saree, or Chudidar with Dupatta.

Why Choose Padmavathi Travels?

Trusted Tirupati Tour Operator

Guaranteed VIP Darshan Tickets

Comfortable and Reliable Car Rental Services

Expert Guidance for Hassle-Free Travel

Book Your VIP Tirupati Darshan Today!

Experience the divine blessings of Lord Venkateswara with Padmavathi Travels. Whether you opt for the Srivani VIP Break Darshan, Tirupati Airport Offline Darshan, or a Two Day Tirupati Tour Package, we ensure a smooth, spiritual, and unforgettable pilgrimage.

[govindhtech]

VQC-MLPNet: A Hybrid Quantum-Classical Architecture For ML

Variational Quantum Circuit-Multi-Layer Perceptron Networks (VQC-MLPNet) are revolutionary quantum machine learning methods. A “unconventional hybrid quantum-classical architecture for scalable and robust quantum machine learning” describes it. VQC-MLPNet improves machine learning training stability and data representation by combining classical multi-layer perceptrons (MLPs) and variational quantum circuits (VQCs). This innovative system uses quantum mechanics to boost computational capabilities, possibly outperforming traditional techniques.

Addressing Quantum Machine Learning Limitations

The invention of VQC-MLPNet quickly addresses critical concerns with existing variational quantum circuit (VQC) implementations. Quantum machine learning aims to improve computation using quantum principles, yet current VQCs often lack expressivity and are subject to quantum hardware noise. In the age of noisy intermediate-scale quantum (NISQ) devices, these restrictions significantly limit quantum machine learning algorithm implementation.

VQC-MLPNet solves standalone VQCs' restricted expressivity and tough optimisation challenges by dynamically addressing them. Modern quantum systems are noisy, but it may lead to more robust quantum machine learning. The research positions VQC-MLPNet as a non-traditional computing paradigm for NISQ devices and beyond due to its theoretical and practical base.

VQC-MLPNet: A Hybrid Innovation

VQC-MLPNet's main novelty is its hybrid quantum-classical architecture. VQC-MLPNet creates classical multi-layer perceptrons (MLPs) parameters using quantum circuits instead of direct computing. This distinguishes hybrid models and improves training stability and representational power.

The method uses amplitude encoding and parameterized quantum processes. By portraying classical data as quantum state amplitudes, “amplitude encoding” can compress data exponentially. VQC-MLPNet uses quantum circuits to inform and dynamically construct traditional MLP parameters, increasing the model's capacity to represent and learn from complex input. This approach provides “exponential gains over existing methods” in representational capacity, training stability, and computational power.

Investigation, Verification

They developed VQC-MLPNet with Min-Hsiu Hsieh from the Hon Hai (Foxconn) Quantum Computing Research Centre, Pin-Yu Chen from IBM's Thomas J. Watson Research Centre, Chao-Han Yang from NVIDIA Research, and Jun Qi from Georgia Tech. The paper, “VQC-MLPNet: An Unconventional Hybrid Quantum-Classical Architecture for Scalable and Robust Quantum Machine Learning,” details their findings.

Using statistical methodologies and Neural Tangent Kernel analysis, the authors have carefully built theoretical VQC-MLPNet performance assurances. The Neural Tangent Kernel can reveal the model's generalisation capabilities and training dynamics for infinitely broad neural networks.

Both theoretical and practical experiments have confirmed the procedure. Importantly, these validations passed with simulated hardware noise. Predicting genomic binding sites and identifying semiconductor charge states were goals. In noisy quantum computing, the design may hold up. The researchers published entire experimental setup, including quantum hardware, noise models, and optimisation methods, as open science to assure repeatability and encourage further research. They meticulously document code and data.

Future implications and directions

The work has major implications for machine learning. They believe VQC-MLPNet and other hybrid quantum-classical methods can overcome the disadvantages of exclusively classical algorithms. Quantum computers may help researchers construct more powerful and effective machine learning models that can solve complex problems in many fields.

Future research may focus on scaling the VQC-MLPNet architecture to larger, more complex datasets and applying it to new issue areas. Future study should investigate various parameter encoding methods and maximise quantum-classical interaction to increase the model's performance and efficiency. The authors want to apply VQC-MLPNet to challenging real-world problems in materials science, drug development, and financial modelling to show its adaptability and promise.

More research will examine the architecture's resilience to alternate noise models and hardware constraints to ensure its reliability and usability in numerous quantum computing situations. Using circuit simplification or qubit reduction strategies to reduce quantum resource needs will make it easier to deploy on increasingly accessible quantum technology. Comparing VQC-MLPNet to other cutting-edge hybrid quantum-classical architectures will illuminate the system's pros and cons and guide future study.

The authors acknowledge that their original work had certain drawbacks, such as the small datasets and the difficulty of recreating quantum noise. This honest appraisal emphasises their scientific integrity and encourages future study to maximise VQC-MLPNet's potential. They stress quantum machine learning research and game-changing improvements. Climate change, pharmaceutical development, and health issues may be solved using quantum computers and machine learning algorithms.

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[tirupatitourpackages]

Special Darshan for Senior Citizens & Differently-Abled at Tirupati

Tirumala Tirupati Devasthanams (TTD) offers a dedicated darshan facility for senior citizens (65+ years) and differently-abled devotees to ensure a comfortable and quick visit. With limited slots per day, devotees must carry valid ID proof and report at the Vaikuntam Queue Complex (VQC-2) for a hassle-free experience. Seek Lord Venkateswara’s blessings with ease!

🌟 Tip: Arrive early for smooth entry and a peaceful darshan!

[reallycool12345]

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[pekasairroc]

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[tsasocial]

DiloGroup’s in-house exhibition on the occasion of techtextil

Techtextil in Frankfurt is traditionally one of the most important show events worldwide for DiloGroup as a specialist for complete nonwoven manufacturing lines with a long-term spe- ciality as a provider of state-of-the-art needling lines. Also this year, Dilo and group members TEMAFA and Spinnbau were situated in hall 12, booth B81, their traditional location, adjacent to needle specialist Groz-Beckert as well as partners and competitors.

In addition to the four days in Frankfurt, Dilo had invited customers for a tour of DiloGroup’s headquarters in Eberbach on Thursday as a supplementary event to demonstrate relatively shortly after ITMA 23 its range of latest machine and line innovations.

More than 80 interested visitors chose to accept Dilo’s invitation to get on the shuttle bus and take the audio-guided tour through the company’s R&D and demonstration centres which had been specifically prepared by the deputy CEOs Rebekka and Riccarda Dilo and their strong team from the sales, R&D and technology departments.

The complete lines and individual machinery were on display in a space of approximately 3.100 m2 including the MicroPunch S research and demonstration line for needling light- -weights in a range of 35 up to 160 g/including the MultiCard MCRR CC with FRS-P feeder which was returned from ITMA and reinstalled for the product development of hygiene, cosmetic, medical and technical lightweight nonwovens. This line was accompanied by a Hypertex line for the production of lightweight sandwiches of reinforced nonwoven layers to increase strength and stiffness in MD and CD directions for use as needled filtration and roofing material as well as shoe and garment applications. The layered mesh of filament or yarn is laid inline between a base and a cover of pre-needled material with speeds up to ca. 40 m/min.

The MicroPunch S intensive needling line can thus be combined with the Hypertex process to include all areas of applications for lightweights needled from fine fibre.

Three buses had carried the seven groups of visitors to take part in machine demonstrations which processed the special fibre material until wind-up in the end-of-line section.

In Technology Centres I and II three complete nonwovens lines ran demonstrations including a 7 m wide needling line consisting of the VQC Card (Quadro), the high-speed DLSC three apron layer capable of ca. 200 m/min infeed speed and the 7 m wide pre-needler.

Also, the HyperPunch machine in 3.5 m was included in the demonstration.

At the aerodynamic web forming section “additive manufacturing” was on display through the 3D-Lofter which comprises a series of individual web formers programmable to lay down additional fibre material. This either in an

1. IsoFeed function to improve the regularity of a flock matt to feed cards by up to ca. 4 - 5 % CV.

2. Reinforcement function of needled substrate used as car interior linings of deeply moulded pieces in order to strengthen locations in the felt plain where stress or strain concentrations take place. Using this method, fibre savings can be achieved by approx. 30 – 40 %.

3. An additional feature of the 3D-Lofter is the widely enlarged patterning capability of needled felts for automotive applications or wall and floor coverings. The 3D Lofter offers sliding colours (“Colour Slide”), geometric patterns or inscriptions for achieving the most modern design styles. 3D-Lofter in combination with the DI-LOUR process, which was also demonstrated by the DI-LOUR IV double structuring system, is offering a joint technology which is particularly interesting for lightweight moulded car velours.

The interesting guided tours included enough time to get an in-depth insight into machines, technologies and textile products which also were on display and given as samples along with technical literature. The tours were concluded in a host and service area with finger food and beverages and with related discussions of current needs and demands for preparing a more sustainable and successful future through innovations.

All the visitors from many countries including South America, Africa, Europe and China expressed their appreciation for a very interesting day at Dilo.

[govindhtech]

ColibriTD Launches QUICK-PDE Hybrid Solver On IBM Qiskit

ColibriTD

The  IBM Qiskit Functions Catalogue now includes ColibriTD's quantum-classical hybrid partial differential equation (PDE) solution QUICK-PDE. Based on IBM's H-DES (Hybrid Differential Equation Solver) technique, QUICK-PDE lets researchers and developers solve domain-specific multiphysics PDEs via cloud access to utility-scale quantum devices.

QUICK-PDE

QUICK-PDE was developed by quantum-powered multiphysics simulation company ColibriTD. IBM Qiskit Functions Catalogue lists it as an application function. QUICK-PDE is part of ColibriTD's QUICK platform.

The function lets researchers, developers, and simulation engineers solve multiphysics partial differential equations using IBM quantum computers in the cloud. For domain-specific partial differential equation solutions, it simplifies and makes development easier.

It works

ColibriTD's unique H-DES algorithm underpins QUICK-PDE. To solve differential equations, trial solutions are encoded as linear combinations of orthogonal functions, commonly Chebyshev polynomials. The function is encoded using $2^n$ Chebyshev polynomials, where $n$ is the number of qubits.

Variable Quantum Circuit (VQC) angles parametrise orthogonal functions.

The function is embedded in an ansatz-created state and evaluated by observable combinations that allow its assessment at any time.

Loss functions encode differential equations. By altering the VQC angles in a hybrid loop, trial solutions are brought closer to real solutions until a good result is achieved.

A solution can use many optimisers. You can chain optimisers to follow a gradient by using a global optimiser like “CMAES” (from the cma Python package) and then a fine-tuned optimiser like “SLSQP” from Scipy for the Material Deformation scenario.

Noise reduction is built into the algorithm. The noise learner strategy can mitigate noise during CMA optimisation by stacking identical circuits and assessing identical observables on various qubits within a larger circuit, reducing the number of shots needed.

Different qubits can encode each variable's function. The function may choose appropriate default values, but users can change them. The ansatz depth per function can also be changed. Adjustable variables include the number of shots needed per circuit. Since there are several optimisation processes, the shots parameter is a list whose length matches the number of optimisers used. Computational Fluid Dynamics and Material Deformation have preset shot values.

Users can choose “RANDOM” or“PHYSICALLY_INFORMED” for VQC angle initialisation. “PHYSICALLY_INFORMED” is the default and often works, but “RANDOM” can be used in other cases.

Use cases and multiphysics capabilities

QUICK-PDE solves complex multiphysics problems. We cover two key use cases:

Computational fluid dynamics

The inviscid Burgers' equation and fundamental CFD model are issues. This equation simulates non-viscous fluid flow and shockwave propagation for automotive and aerospace applications.

The Navier-Stokes equations for fluid motion have an inviscid Burgers' equation at zero viscosity ($\nu = 0$). $fracpartial upartial t + ufracpartial upartial x = 0$1117, where $u(x,t)$ is the fluid speed field

When $a$ and $b$ are random constants and $u(t=0, x) = axe + b$, the current implementation only allows linear functions as initial conditions. Change these constants to see how they affect the solution.

The CFD differential equation arguments are on a fixed grid: space ($x$) between 0 and 0.95 with 0.2375 step size and time ($t$) with 30 sample points. The dynamics of new reactive fluids for heat transfer in tiny modular reactors can be studied using QUICK-PDE.

MD: Material Deformation

Second is Material Deformation (MD), which studies 1D mechanical deformation in a hypoelastic regime like a tensile test. Simulation of material stress is crucial for manufacturing and materials research.

Problem: a bar with one end dragged and one fixed. This system of equations includes a strain function ($\sigma$) and a stress function ($u$).

A surface stress boundary condition ($t$) represents the labour needed to stretch the bar in this use case.

MD differential equation arguments use a fixed grid ($x$) between 0 and 1 with a 0.04 step size.

Future versions of QUICK-PDE will include the H-DES algorithm to handle higher-dimensional problems and additional physics domains like electromagnetic simulations and heat transport.

Usability, Accessibility:

IBM Quantum Premium, Dedicated Service, and Flex Plan users can use QUICK-PDE.

The function must be requested by users.

The quantum workflow is simplified by application functions like QUICK-PDE. They use classical inputs (such physical parameters) and return domain-familiar classical outputs to make quantum approaches easier to integrate into present operations without needing to build a quantum pipeline.

This allows domain experts, data scientists, and business developers to study challenges that require HPC resources or are difficult to solve.

The function supports “job,” “session,” and “batch” execution modes. The default mode is “job”. A dictionary contains input parameters.

Use_case (“CFD” or “MD”) and physical_parameters specific to the use case (e.g., a, b for CFD; t, K, n, b, epsilon_0, sigma_0 for MD) are crucial. Users can adjust nb_qubits, depth, optimiser, shots, optimizer_options, initialisation, backend_name, and mode using optional arguments.

The function's output is a dictionary of sample points for each variable and its computed values. For instance, the CFD scenario provides u(t,x) function values and t and x samples. In MD, x samples and function values for u(x) and sigma(x) are presented. The resulting array's structure matches the variables' alphabetic sample points.

Benchmarks for Inviscid Burgers' equation and Hypoelastic 1D tensile test show statistics like qubit usage, initialisation method, error ($\approx 10^{-2}$), duration, and runtime utilisation.

A tutorial on modelling a flowing non-viscous fluid with QUICK-PDE covers setting up starting conditions, adjusting quantum hardware parameters, performing the function, and applying the results. The manual provides MD and CFD examples.

In conclusion, QUICK-PDE can be used to investigate hybrid quantum-classical algorithms for addressing complex multiphysics problems, which may enhance modelling precision and simulation time. It is a significant example of quantum value in scientific computing and a step towards opening doors previously inaccessible with regular instrumentation.

[postsofbabel]

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[electronicsmanufacturing]

https://radiantappliances.com/quality-control-at-radiant.html

Quality Control at Radiant!

“Quality is more important than quantity. One home run is much better than two doubles.” – Steve Jobs

At Radiant, we firmly believe that adherence to world class quality controls and processes enable us to live up to our values of Trust, Transparency, Loyalty and Quality with all our customers thus enabling them to return back to us for world class products and solutions that enable ensures their business to be ahead of the curve at all times. At our factory, Quality consciousness is imbibed into a new joiner from the time he/she accepts our offer to join the organisation by means of detailed instructions and process flowcharts that enables him/her to seamlessly complete the joining process and documentation. During the tenure of the individual at Radiant, he/she is exposed to various facets of quality inside the factory on a recurring, daily and repetitive basis to such an extent that ensuring quality (in all spheres) becomes second nature to all our talent. How do we ensure 100% quality control over every single piece that is manufactured at our facility? We adhere to the below mentioned aspects of Quality Control to ensure that anything that comes inside and moves within and finally outside the factory adheres to our ever persevering standards of quality and fulfilment at all costs. • Inbound Quality Control (IQC) • Warehouse Management System (WMS) • Manufacturing Quality Control (MQC) • Process Quality Control (PQC) • Visual Quality Control (VQC) • Value Stream Mapping (VSM) • Quality champions and cheerleaders • 5 S Process and systems • LEAN Manufacturing Process • Outbound Quality Control (OQC) • Customer Support (CS) helpline for Field Quality redemption • Radiant Helpline (available on Website) for all stakeholders for recording and closure At Radiant, we believe in the concept of Adaptive, Agile and Savvy to run our manufacturing process with the intended outcome of providing Customer Delight always and every time to our valued customers.

[quadcrushuk]

Farmer is literally the nicest person in quidditch

True that