#cudaq

National Quantum Initiative Reauthorization

As the global competition for computational supremacy heats up, technological and government leaders are calling for a major change in how the UK and its allies, especially the US, approach next-generation computing. To safeguard national security, economic competitiveness, and scientific leadership in the 21st century, a new strategy plan to reauthorize and evolve the National Quantum Initiative (NQI) was provided

The NQI Effect

The National Quantum Initiative became law in December 2018. The multi-agency approach in this bipartisan act united universities, government laboratories, and the corporate sector under a national agenda. To date, the initiative has moved quantum systems from “isolated demonstrations” to “scalable architectures,” achieving qubit coherence and gate fidelities.

Technology has changed significantly since 2018, according to experts. The original method predates our understanding of how AI and quantum computing must converge. Congress must urgently reauthorize the NQI to reflect this new reality and ensure that it is considered as an integrated part of an accelerated computing environment.

Genesis Mission: Ten Years of Success

The “Genesis Mission,” outlined by Under Secretary for Science Dr. Darío Gil in December 2025, is central to this renewed viewpoint. The goal is to create an integrated discovery platform using national resources. This program aims to increase R&D production in a decade.

Dr. Gil called this the “precipice of a scientific revolution,” powered by AI, HPC, and quantum systems. He famously compared these converging systems to telescopes and microscopes, calling them the “new scientific instruments of our time” that will help humans understand nature.

Introducing Quantum-GPU Supercomputer

This revolution depends on quantum-GPU supercomputing. The capacity to generate hundreds of logical qubits and perform millions of operations, not just hardware, makes a quantum system “scientifically useful”. This requires flawless integration between CPUs, GPUs, and QPUs.

To aid this, NVIDIA has highlighted two key technologies:

The Bridge (NVQLink): Real-time feedback loops for quantum error correction require low-latency, high-throughput interconnects.

The Platform (CUDA-Q): An open-source programming framework that lets scientists write QPUs and GPUs together, integrating theoretical physics with domain science.

The Importance of AI

Perhaps the biggest change is AI's position in the proposed National Quantum Initiative Reauthorization. AI is now “central to overcoming key challenges in scaling and deploying quantum computing”. This involves real-time quantum algorithm optimization and hardware calibration.

The private sector can build “bridges” and “platforms,” but only federal funding can build open science's massive, fault-tolerant infrastructure. The DOE has set a “bold goal” to install a scientifically meaningful quantum supercomputer by 2028.

A Five-Pillar Congress Strategy

Congress is instructed to change the NQI from a “discovery-focused” program to one that allows “system-level deployment” to achieve these goals. Five main suggestions:

Digital Twins: Funding electronic design automation innovation to model quantum hardware digitally before building, speeding development.

To build large-scale systems of logical qubits with AI, research and infrastructure investment should be focused on quantum error correction.

AI Integration: Establishing a “AI+Quantum” center to share resources and deliver quantum-simulated datasets for AI model training.

Launching flagship hybrid applications in chemistry, materials science, and biology to set practical performance standards.

Benchmarking and Standards: Allowing the QED-C to define “true utility” and provide transparent progress metrics.

Conclusion

New Amazon Braket Notebook Instances Natively Support NVIDIA CUDA-Q, Revolutionising Quantum Development on AWS

An important quantum computing platform upgrade from AWS is that Amazon Braket Notebook settings now natively support CUDA-Q, NVIDIA's open-source hybrid quantum-classical computing architecture. Increasing NVIDIA's accessibility streamlines hybrid quantum-classical system developers' and scholars' workflows.

The connection lets customers run CUDA-Q programs in Amazon Braket Jupyter notebook instances without configuration. Native support is enabled by upgrading laptop instances to Amazon Linux 2023, which improves security, performance, and compatibility for quantum development workflows.

Smooth Hybrid Workflow Development

With this intrinsic feature, quantum researchers and developers can simply create and test hybrid quantum-classical algorithms. The platform simplifies hybrid algorithm creation, modelling, and execution in Braket-managed notebooks.

Native CUDA-Q support provides several key features:

GPU-accelerated calculations and quantum simulations can be combined in hybrid workflows. GPU acceleration: Amazon Braket makes NVIDIA GPUs available to developers for faster algorithm creation and simulation.

Access to Quantum Hardware: The platform lets users simply switch from simulation to Braket's quantum hardware. IonQ, Rigetti, and IQM offer single-managed Quantum Processing Units (QPUs). Amazon This release strengthens Braket's position as a comprehensive quantum computing research and development platform. It simplifies CUDA-Q use in managed notebooks by eliminating the need for developers to control local deployment or route execution via Hybrid Jobs.

New Environment Access and Pre-Installed Packages When customers create an Amazon Braket notebook instance, they get the latest kernel. New conda_braket tile may appear when you open the notebook. By selecting this tile under “Notebook” or “Console,” the new conda_braket kernel launches a Jupyter notebook or Python console session.

“CUDA-Q and Braket” tile opens example notebooks using the conda_braket kernel as the default kernel.

Braket notebooks come with the latest compatible packages for the four leading quantum development frameworks Braket, CUDA-Q, PennyLane, and Qiskit. Developers can verify installed packages with a command.

Several critical package versions are confirmed in the output, including:

Amazon Basket Algorithm Library 1.6.2

amazon-braket-default-simulator=1.31.4

Amazon-braket-pennylane-plugin=1.33.5

Amazon-braket-schemas=1.26.1

Amazon-braket-sdk=1.102.6

cudaq=0.12.0.post1

cudaq-qec=0.4.0.post1

cudaq-solvers=0.4.0

PennyLane=0.42.3

PennyLane_Lightning=0.42.0

1.4.4 qiskit

0.17.2 qiskit-aer

Qiskit-algorithms=0.4.0

qiskit-ionq=0.6.1

0.6.0 qiskit_braket_provider

Amazon Braket notebook instances come with quantum applications using Braket, CUDA-Q, PennyLane, and Qiskit. Users can view CUDA-Q examples on the Launcher page by clicking “CUDA-Q and Braket”. This opens the notebook 0_hello_cudaq_jobs.ipynb and shows more examples under nvidia_cuda_q/.

How to Implement and Examples

Small, CPU-based instance types like ml.t3.medium are recommended for laptop instances by AWS. Amazon Braket Hybrid Jobs can perform GPU-intensive CUDA-Q apps. GPU-powered instances like ml.p3.8xlarge are recommended for these intensive tasks. GPU-accelerated instances include:

ml.p3.2xlarge (1 NVIDIA V100 GPU).

ml.p3.8xlarge (4 V100 GPUs).

ml.p3.16xlarge (8 V100 GPUs).

When running GPU-powered instances concurrently through Hybrid Jobs, a customer's Amazon Elastic Compute Cloud (EC2) service quota should exceed the number of instances they want.

The Previous Docker-Based Setup

Developers needed a Jupyter kernel running a CUDA-Q Docker container to execute CUDA-Q apps interactively in Braket notebooks before this native integration. This enables customers to run CUDA-Q simulators on Amazon Braket-supported quantum hardware backends and powerful NVIDIA GPUs using the open-source NVIDIA CUDA-Q platform and Amazon Braket Hybrid Jobs.

Braket Notebook instances come with Docker, allowing controlled development using NVIDIA NGC Container Registry images.

The previous approach required several steps to configure a custom kernel:

Dockerfile creation: Stable Dockerfiles require CUDA-Q images like nvcr.io/nvidia/quantum/cuda-quantum:cu12-0.9.1. This file explained installing ipython, ipykernel, and amazon-braket-sdk.

Next, the image was built in three to five minutes.

For the new kernel (such as docker_cudaq), a kernel.json file was created containing command-line options for starting the kernel with the newly created Docker image.

GPU Access: Add “–gpus=all” to the kernel to access GPU instances like ml.p3.2xlarge, ml.p3.8xlarge, and ml.p3.16xlarge in CUDA-Q program.configuration in JSON.

This configuration mounts /home/ec2-user/amazon-braket-examples/examples inside the CUDA-Q container. If they needed more Python modules or updated CUDA-Q versions, they merely needed to tweak the Dockerfile and rebuild the Docker image.

Quantum AI Algorithms Norma Speeds Drug Discovery on NVIDIA CUDA-Q with Quantum AI

Leading quantum computing startup Norma has validated its quantum AI algorithms on the NVIDIA CUDA-Q platform, which computes up to 73 times quicker, achieving a drug discovery breakthrough. Running Norma's algorithms on NVIDIA GH200 Grace Hopper Superchips enabled this vital achievement, which advances quantum AI's ability to solve complex scientific problems. Under CEO Hyunchul Jung, the company has successfully demonstrated how quantum AI can be applied in drug development and plans to validate these efforts in other industries. Revolutionising Drug Discovery with Quantum AI The vast chemical search space often creates computational hurdles in the quest for new medicinal options. Traditional AI systems' limitations in this vast sector slow medication development. Norma's Quantum AI team pioneered quantum AI methods to overcome these difficulties. QLSTM, QGAN, and QCBM boost computer power for drug candidate discovery. A combined research study with Kyung Hee University Hospital in Gangdong focused on novel medicine candidates, including the validation trial. Norma is developing quantum AI algorithms for biotechnology, banking, and defence. Quantum-Classical Hybrid Operations Accelerator NVIDIA CUDA-Q The NVIDIA CUDA-Q platform accelerated Norma's quantum algorithm creation and implementation, contributing to their success. CUDA-Q simplifies GPU and QPU integration for quantum-classical hybrid processes. This expertise is needed to execute complex quantum AI algorithms. Norma employed CUDA-Q and NVIDIA H200 GPUs and GH200 Grace Hopper Superchips for validation testing. CUDA-Q testing of these algorithms is underway by the company.

ORCA Computing

ORCA Computing Delivers First Photonic Quantum Computing System to UK's National Quantum Computing Centre

ORCA Computing, a renowned quantum computing vendor, delivered and installed its photonic quantum computing system at the National Quantum Computing Centre (NQCC) in the UK as part of a major UK government investment in quantum technologies.

ORCA Computing, a leading quantum computing company, makes full-stack photonic quantum computers. They want to make quantum computing a reality to expand applications, boost innovation, and alter industries. They want to create error-corrected quantum computers and value-creating quantum accelerators using their modular, fiber-interconnected design.

Special Method and Benefits ORCA's photonic quantum computing technique has several advantages over other quantum systems for current and future applications. Innovative methodologies and rack-mounted telecom and optical fibre components provide their system the performance, scalability, and usability needed to tackle difficult use-cases like generative machine learning and optimisation. Advantages of their technology include:

Modular, upgradeable architecture.

Better scaling.

Lower user entrance barriers.

a viable fault-tolerant company model.

Mature and reliable PT Series system.

ORCA PT Series Quantum-Accelerated Generative AI & Optimisation The ORCA PT Series is essential for developing near-term industrial applications of quantum-accelerated generative AI and optimisation. These systems work, run, and perform reliably based on real-world applications and deployments, unlike the industry's arduous, fragile, and expensive technology.

The PT Series has advantages:

Quantum Accelerated Generative AI & Optimisation: It breaks the unsustainable power demand of traditional AI and improves companies by giving generative AI capabilities not found in classical or other quantum computing platforms.

Data Centre Ready: Air-cooled, rack-mounted systems can be cloud or on-premises.

Developer-friendly: They support PyTorch, Python, and CUDA-Q.

Lowest Total Cost of Ownership: This includes purchase, setup, maintenance, and upgrades.

The ORCA PT Series has been employed in several generative AI and optimisation applications and use-cases, benefiting researchers and enterprises in major scientific and industrial fields. ORCA and bp investigated a hybrid quantum-classical technique that uses GAN methods to produce low-energy conformations of tiny and medium-sized hydrocarbon molecules. This research aims to solve computational molecular exploration problems, which are crucial for biofuel and energy material development.

Founding and Leading In 2019, ORCA Computing was spun off from Oxford University after 30 years of technology achievements and scientific research. Josh Nunn, Richard Murray, and Professor Ian Walmsley founded the company.

Current Events and News Recently, ORCA Computing has earned waves for its innovations.

The UK National Quantum Computing Centre (NQCC) receives the first photonic quantum computing equipment.

On June 11, 2025, ORCA Computing installed the UK's first photonic quantum computing device at the NQCC.

ORCA's selection for the UK's Quantum Computing Testbeds effort in February 2024 fulfilled a major program requirement.

at 36 hours, the PT Series system, the first photonic quantum system at a UK public institution, proved its reliability and maturity.

The ORCA system is the first to combine several photonics. It speeds up hybrid quantum-classical workflows and real-world quantum applications.

This facility backs the £121 million UK quantum strategy.

Early users have benchmarked quantum/classical generative AI models for molecular chemistry, completed over 25,000 jobs without interruption, and accomplished an 81-parameter binary optimisation. At Digital Catapult Quantum Technology Access Programme (QTAP), these achievements were displayed.

The NQCC will now test and evaluate the system to benchmark performance and include it into cooperative R&D programs like the University of Edinburgh's Quantum Software Lab (QSL). Dr Simon Plant, NQCC Deputy Director for Innovation, says early access to advanced photonic technologies shapes quantum applications and accelerates innovation.

Vodafone Partnership: Starting October 6, 2025, Vodafone and ORCA Computing will use quantum technologies to quickly evaluate client network designs.

ORCA's Chief Science Officer and co-founder Josh Nunn described the PT Series quantum systems in an Optics & Photonics News cover story on September 6, 2025.

ParTec AG Partnership: On July 5, 2025, ParTec said it would integrate ORCA's photonic quantum computing into its AI Factory infrastructure. This partnership is crucial to expanding quantum computing applications in research and industry.

HPCwire Feature Article: A February 6, 2025 post by ORCA Computing Head of Solutions Architecture James Fletcher detailed how the business is allowing scalable AI Factories using quantum acceleration to overcome compute limitations.

Advances in Quantum Use-Cases Exhibition: On April 9, 2025, QTAP attendees learnt how ORCA's platform lets firms design and test quantum application cases in months.

The milestone shipment of ORCA Computing's PT-2 photonic quantum system to the NQCC on March 21, 2025, was covered in Quantum Computing Report before the entire delivery.

The Compal CGA-QX Platform

Compal Electronics will unveil its CGA-QX computing platform at GTC Taipei 2025. This cutting-edge platform accelerates quantum computing application development, which helps solve exceedingly challenging computational issues.

A powerful Compal GPU Annealer and NVIDIA CUDA-Q  Solvers library power the CGA-QX computing platform. This combination allows the platform to solve problems conventional computers cannot. The NSTC-sponsored Quantum Taiwan Program’s acceptance of CGA-QX emphasises its strategic importance. The platform’s potential to boost Taiwan’s quantum computing ecosystem and research is recognised nationally by this adoption.

Compal develops and promotes computer technologies, focussing on Nvidia-accelerated computing. This perspective is grounded in real issues. Many optimisation problems in various industries demand complicated and time-consuming computations that typical computer systems cannot handle. Sorting through vast databases, simulating complex physical processes, or significantly improving logistics are examples. Traditional computer methods are slow, ineffective, or unfeasible due to these inherent problems.

In this case, quantum computing is game-changing. Quantum computing may tackle these optimisation problems faster and more efficiently. Quantum computing does difficult tasks faster and more efficiently, but they don’t explain how. The CGA-QX platform provides industry and researchers with infrastructure and resources, delivering Compal’s promise.

The CGA-QX platform offers two approaches to quantum computing research. It lets users use the Compal GPU Annealer for large-scale quantum-inspired algorithm calculations. Quantum-inspired algorithms solve optimisation issues faster on GPU-accelerated systems by using quantum physics. This allows complex, insoluble problems to be investigated. Researchers may calculate complex quantum programs with CUDA-Q Solvers.

CUDA-Q, Nvidia’s quantum algorithm creation and modelling platform, accelerates quantum simulations using GPUs. These features enable in-depth analysis to accelerate algorithm development and quantum computing research. This integrated environment is necessary to swiftly prototype and test new quantum algorithms before implementing them on quantum hardware.

Executive Vice President Eric Peng of Compal called the CGA-QX a “cutting-edge quantum computing platform” and backed it. His claim emphasises its capacity to use CUDA-Q’s GPU-accelerated quantum computing simulation. This characteristic is significant because it allows GPUs to mimic complex quantum systems and algorithms, connecting theoretical quantum discoveries to real-world applications. Peng said the goal is to solve the sector’s optimisation problems, demonstrating the platform’s direct relevance to business and scientific problems.

The technology could accelerate biological protein-ligand interaction research and computation. Protein-ligand interactions are crucial to drug discovery because they determine how drugs bind to target proteins. Traditional computational methods demand time and money to evaluate these interactions. By dramatically accelerating computations, CGA-QX can screen and anticipate candidate compounds with unprecedented efficiency. Find promising drug candidates faster using this expertise, which speeds up the drug development pipeline and improves biological study success.

Compal is also aggressively demonstrating the platform’s immediate impact with MacKay Memorial Hospital. This partnership aims to improve clinical tumour treatments. Even though the mechanisms are unknown, it suggests that the CGA-QX platform’s computational power is being used to solve difficult oncology problems like personalised medicine, treatment optimisation, and drug efficacy prediction for cancer patients. This alliance shows how quantum computing and quantum-inspired algorithms may tackle some of the healthcare industry’s biggest problems.

In conclusion