Author: Neel Achary

AVer Goes “Modus Match” Official: TR Series Auto Tracking and Professional PTZ Cameras Now Verified for Precision 3D Installation Planning

Taipei, Taiwan – Apr 02: AVer Information Inc., an award-winning provider of AI audio-video solutions, today announced its official entry into the Modus Match Partner Program. Following comprehensive technical integration, AVer’s high-performance TR Series AI Auto Tracking cameras and PTZ Series cameras are now fully verified and supported within the ModusVR ecosystem — bringing manufacturer-certified 3D planning to one of the industry’s most trusted camera lines.

Don’t Just Place a Camera. Simulate the Experience.

In the world of Pro AV, “guessing” camera placement is a recipe for a costly truck roll. With this official Modus Match integration, integrators can now move beyond 2D blueprints and experience AVer cameras in fully immersive 3D environments before a single bracket is mounted.

When deploying the latest TR Series models alongside PTZ cameras, ModusVR users can now:

• Visualize Camera Sightlines: Instantly see the exact Field of View (FoV) to ensure the camera can capture presenters over podiums, monitors, or pillars
• Test Pan-Tilt-Zoom Coverage: Adjust PTZ positions in real-time to verify the camera’s mechanical range covers the entire stage, classroom, or conference room
• Validate Mounting Locations: Virtually position cameras at different heights and angles to find optimal placement before installation begins
• Eliminate Installation Guesswork: Identify blind spots, mounting challenges, and coverage gaps in the virtual environment, not during the truck roll
• Optimize the Design Experience: Enable system integrators and end users to preview TR and PTZ cameras within their specific spaces, experiencing the layout to ensure optimal performance and accelerated AV project adoption
• Generate Accurate BoMs: Transition seamlessly from design to purchase with manufacturer-verified specifications

The “Modus Match” Guarantee: Digital Twins, Not Generic Models

The “Modus Match” seal means these aren’t just generic 3D shapes: these are digital twins. Every focal length, pan/tilt limit, zoom range, and physical dimension has been manufacturer-verified by AVer’s engineering team. When you drag an AVer TR or PTZ camera into your ModusVR project, the Bill of Materials (BoM) is 100% accurate, and the field-of-view simulation reflects real-world optical performance.

“Presentation cameras are an important part of many of the spaces our users design in Modus,” says Ken Brueck, Co-Founder of Modus VR. “With AVer’s TR Series now part of the Modus Match program, integrators can accurately visualize placement and confidently define camera coverage in classrooms, divisible training spaces, and auditoriums before installation begins.”

Total Design Certainty for Professional AV Installations

“Our TR Series cameras are built for intelligence and movement,” says David Kuo, President at AVer Information Inc. “By joining the Modus Match program, we’re giving our partners the power to validate camera placement and coverage in a virtual twin of their client’s space. It’s about total design certainty and ending the era of onsite adjustments. Whether it’s a university lecture hall, corporate boardroom, or K-12 classroom, integrators can now design with confidence knowing exactly what AVer cameras will capture.”

Key Benefits for AV Integrators:

• Pre-Installation Confidence: Test multiple camera placements and validate coverage before purchasing equipment
• Faster Client Approval: Present immersive 3D walkthroughs showing exactly what cameras will capture, accelerating stakeholder buy-in
• Reduced Site Visits: Eliminate unnecessary truck rolls by identifying coverage issues in the virtual environment
• Time Savings: Compress weeks of planning into hours with real-time, accurate 3D simulation

Availability

The AVer TR Series and PTZ Series camera libraries are available now for all ModusVR subscribers at no additional cost. Simply update your ModusVR library to start building more accurate room designs with precise camera coverage validation. For demonstrations or technical specifications, visit www.modusvr.com or contact your local AVer representative.

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Quanscient and Haiqu Announce Breakthrough Algorithm for Scalable Computational Fluid Simulations on Quantum Computers

This advancement in quantum algorithms could help accelerate some of the most computationally intensive simulations used in engineering today.

Helsinki, Finland – April 2, 2026 – Researchers from Quanscient, a leader in cloud-based multiphysics simulation technology and quantum algorithms, and Haiqu, a leading developer of quantum middleware, today announced a new algorithm that can significantly advance the use of quantum computing in real-world engineering applications. The teams conducted a 15-step nonlinear fluid benchmark with an obstacle, making this the most physically complex, publicly documented variant of a Quantum Lattice Boltzmann Method (QLBM) hardware demonstration to date.

Developed and tested on IBMs largest-available quantum computer, the IBM Heron R3, the algorithm reduces the number of qubits required to run complex simulations in computational fluid dynamics (CFD) on quantum computers, demonstrating a viable path toward future industrial-scale solutions.

CFD is widely used to model how air, water, and other fluids behave around objects, such as airflow over an aircraft wing. It plays a critical role in product development and testing across industries, including aerospace, automotive, and energy. However, these simulations are extremely demanding for even today’s most powerful supercomputers, often taking days or even weeks to complete, if possible at all.

The new algorithm addresses one of the key challenges in applying quantum computing to CFD: high resource requirements. By significantly reducing the number of qubits and computational operations needed, this approach makes it more practical to run complex simulations on quantum computers. It demonstrates a more efficient path toward using quantum systems for real-world applications, and ultimately could help companies design better products and optimize complex systems more quickly.

“This is an interesting and timely contribution to quantum CFD,” said Oleksandr Kyriienko, Professor and Chair in Quantum Technologies at the University of Sheffield. “It proposes a more flexible quantum LBM framework while keeping the core algorithm efficient, and it strengthens the case with applications ranging from linear acoustics to IBM-QPU-assisted nonlinear flow simulations. We need more works like this to achieve industrially relevant quantum solutions.”

“This is one of the most realistic CFD simulations ever executed on a quantum computer. It is an important signal that quantum CFD research is moving toward simulating how fluids interact with real-world shapes and obstacles on quantum hardware,” Mykola Maksymenko, CTO of Haiqu. “This is the direction that any industrially meaningful workflow would have to take to reach commercial viability.” 

“CFD is one of the most computationally difficult branches of simulation with some of the largest impact on the world’s biggest sectors,” said Valtteri Lahtinen, Chief Scientist of Quanscient. “Quantum computers offer a future path to simulations that are far more complex than what classical computers can handle, which may allow for the design of more efficient vehicles and aircraft, better energy systems and more. Our work with Haiqu is a critical step toward making this a reality.”

Researchers from Quanscient and Haiqu developed and tested a novel One-Step Simplified LBM (OSSLBM) based on a quantum Lattice Boltzmann Method (QLBM) algorithm, which is a powerful generalization of an important classical CFD technique. Their approach allowed them to run a nonlinear fluid‑flow simulation with an obstacle, such as fluid moving around a solid object, over multiple steps on IBM quantum hardware. Haiqu’s algorithmic and runtime layer was critical to making this possible, reducing circuit depth, improving and developing new key algorithmic subroutines, and applying targeted error‑reduction techniques that allowed the quantum system to execute a multi‑step, complex workflow that would otherwise be out of reach for today’s devices.

The researchers believe their work represents a new algorithmic framework that reshapes how fluid simulations are performed on quantum computers, turning a complicated sequence of calculations into a simpler, more efficient process designed for quantum hardware. The hybrid quantum-classical OSSLBM can be executed on current hardware, outlining a practical path for moving beyond simple linear demonstrations toward more realistic, engineering‑relevant quantum fluid simulations as quantum systems continue to mature.