Author: Mohamed Abdel-Kareem

    1. UCLA, Caltech, and NVIDIA Develop Fourier Neural Operator for Quantum Control Sequence Synthesis

      A research collaboration involving UCLA, Caltech, and NVIDIA has developed a machine-learning framework utilizing a Fourier Neural Operator (FNO) for the inverse design of quantum control pulse sequences. This FNO-based method significantly accelerates the synthesis of quantum control sequences, achieving a 10⁷x speedup and reducing computation time from 10 hours to 10-20 minutes, with an 86.2% success rate in preparing target quantum states. The framework, built on NVIDIA's CUDA-Q platform, offers a differentiable approach for optimizing laser pulse parameters in molecular quantum dynamics.

    2. QCentroid Integrates QuantumOps Platform with NVIDIA CUDA-Q for Enterprise Hybrid Application Workflows

      QCentroid has integrated NVIDIA CUDA-Q into its QuantumOps platform, creating an enterprise hybrid application design framework. This new system automates the placement of quantum components within classical HPC software, allowing for the optimization of classical-quantum boundaries in AI, simulation, and generative modeling. Demonstrated on a cGAN for catalyst materials discovery, the platform evaluates various hybrid Generator designs against classical baselines, leveraging GPU-accelerated simulation and quantum processing units for performance assessment.

    3. BlueQubit Launches $150,000 “Quantum Flywheel” Compute Grant Program Supported by AWS, IBM, and NVIDIA

      BlueQubit has launched the "Quantum Flywheel" grant program, offering $150,000 in compute credits over three months for quantum algorithm discovery, adversarial classical simulation, and quantum error correction research. Supported by IBM, AWS, and NVIDIA, the program provides access to QPU, GPU, and CPU hardware, along with BlueQubit's development environment, for selected research teams. The initiative aims to advance quantum computing through open-source contributions, peer-reviewed research, and AI-driven QEC code discovery.

    4. Quandela and NVIDIA Outline Photonic QPU Integration Architecture via NVQLink

      Quandela and NVIDIA have collaborated on a technical white paper outlining an architecture to integrate photonic Quantum Processing Units (QPUs) with classical AI and HPC environments using NVIDIA NVQLink. This framework connects Quandela's Quantum System Controller (QSC) to NVIDIA GPU nodes via a low-latency interconnect, enabling GPU-accelerated simulations, quantum error correction, and QPU pulse calibration within a unified host process running NVIDIA CUDA-Q and the MerLin Quantum Machine Learning framework. This initiative aims to facilitate the commercial adoption of hybrid quantum-classical computing across various phases, from cloud-hosted experimentation to scalable, fault-tolerant deployments.

    5. Anyon Computing Unveils Open-Source Real-Time Control Plane Powered by NVIDIA NVQLink

      Anyon Computing has introduced an open-source, real-time quantum control system featuring NVIDIA NVQLink integration. This system unifies microwave control hardware and superconducting QPUs with CPUs and GPUs over a shared RDMA-over-Ethernet fabric, enabling microsecond execution loops vital for quantum error correction and hybrid quantum-classical machine learning. The initiative aims to standardize control layers across multi-QPU and multi-GPU supercomputing clusters.

    6. Iceberg Quantum and Diraq Validate Pinnacle qLDPC Architecture on Silicon Spin Hardware via NVIDIA CUDA-Q Logical

      Iceberg Quantum and Diraq have successfully compiled and validated Iceberg's Pinnacle qLDPC architecture on Diraq's silicon spin-qubit hardware using NVIDIA CUDA-Q Logical. This integration demonstrates that high-rate qLDPC error correction can be achieved on silicon spin hardware without extensive global wiring, utilizing electron shuttling for localized qubit routing. This breakthrough allows for supporting 1,000 logical qubits with 150,000 physical qubits, significantly reducing hardware requirements compared to traditional methods.

    7. Quantum Motion Integrates Silicon Spin Architecture with NVIDIA CUDA-Q Logical Compiler Stack

      Quantum Motion has integrated its silicon spin architecture with NVIDIA CUDA-Q Logical, enabling automated quantum error correction-aware compilation. This integration translates high-level algorithms into hardware instructions for Quantum Motion's looped pipeline architecture, which uses electron shuttling to create a 3D virtual-z topology for surface codes. This advancement aims to facilitate large-scale electronic structure calculations for complex molecular systems.

    8. Quantum Motion Finalizes Second Close of Series C Funding Round for Silicon QPU Scaling

      Quantum Motion has finalized the second close of its Series C funding round, co-led by DCVC and Kembara, with new and returning investors including imec.ventures, Sony Innovation Fund, and Lansdowne Partners. This capital will accelerate the industrial scale-up of their fault-tolerant silicon quantum processors, supporting custom silicon co-development using 300mm CMOS semiconductor foundries, international expansion with a new U.S. research lab, and ongoing technical execution under DARPA’s Quantum Benchmarking Initiative. Their strategy focuses on dense silicon spin-qubit architectures fabricated in commercial foundries, aiming for significant cost, size, and energy reductions compared to alternative quantum computing methods.

    9. Infleqtion Integrates Open-Source qLDPC Library with NVIDIA CUDA-Q Logical Architecture

      Infleqtion has integrated its open-source qLDPC software library with NVIDIA CUDA-Q Logical, creating an end-to-end software pipeline for quantum error correction (QEC) codes. This integration, announced at IEEE Quantum Week 2026, allowed Infleqtion to construct and validate a Hypergraph-Product Simplex (HGPS) quantum low-density parity-check (qLDPC) code block with significantly reduced data-qubit overhead compared to standard surface codes, marking a step towards practical quantum computing.

    10. Sandia, Quantinuum, and NVIDIA Introduce QUOPS Framework to Benchmark Physical and Logical Quantum Performance

      A new Quantum Universal Operations Performance System (QUOPS) framework has been introduced by Sandia National Laboratories, Quantinuum, and NVIDIA to benchmark quantum computer performance. This framework measures integrated performance across both physical and logical-qubit platforms, using metrics like QUOPS score (Q) for circuit size and QUOPS rate (Ω) for operational throughput. Initial benchmarks show Quantinuum’s Helios-1 achieving a physical score of Q = 1,504, while Google’s Willow and IBM’s ibm_boston reached Q = 216 and Q = 204 respectively.