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  • Classiq and ParityQC Partner to Improve Quantum Circuit Execution Across Hardware

    Classiq and ParityQC Partner to Improve Quantum Circuit Execution Across Hardware

    Classiq and ParityQC have announced a partnership to integrate ParityQC’s Parity Twine technology with Classiq’s quantum software engineering platform. This collaboration is intended to create a more direct path from high-level algorithm design to execution on quantum hardware.

    This addresses one of the more persistent challenges in quantum computing, which is translating an algorithm into a circuit that can run efficiently on hardware where qubits cannot all interact directly with one another.

    Most quantum processors have limited connectivity, meaning information must be moved between qubits before an operation can be performed. This movement is usually done through SWAP gates, which increase circuit depth, execution time and exposure to noise. On current quantum systems, where errors accumulate quickly, inefficient routing can determine whether an experiment produces a meaningful result at all.

    Classiq and ParityQC plan to combine Classiq’s universal optimization protocols with ParityQC’s algorithm-aware optimization methods. The goal is to reduce circuit complexity and the number of SWAP operations required when mapping quantum programs onto physical devices.

    “Quantum computing will only become practical at scale if the software layer can automatically bridge the gap between algorithmic intent and the constraints of real machines,” said Nir Minerbi, co-founder and CEO of Classiq.

    Classiq’s platform uses a model-first approach, allowing developers to define the function and constraints of a quantum program before the platform synthesizes an optimized circuit. ParityQC’s technology introduces additional hardware-aware methods for representing and distributing quantum information across a processor’s connectivity layout.

    The integration is intended to place these architecture-specific considerations directly within a higher-level development workflow. Rather than requiring developers to manually redesign algorithms for each processor, the combinatio could automate more of the optimization process while preserving portability across different hardware systems.

    This is increasingly important as the quantum hardware market becomes more diverse. Superconducting, trapped-ion, neutral-atom, photonic and other architectures each have distinct connectivity, control and compilation requirements. Software that can retain a degree of hardware independence while still accounting for the physical characteristics of individual devices may become a critical layer of the emerging quantum stack.

    The collaboration will target both current noisy quantum processors and future fault-tolerant systems. The companies also indicated that the partnership could extend beyond product integration into academic research, workforce development, benchmarking and future quantum software standards.

    The project is supported by Germany’s Federal Ministry for Economic Affairs and Energy following a decision by the German Bundestag.

  • Classiq and UC Chile Launch Quantum Machine Learning Project for Biomedical Image Analysis

    Classiq and UC Chile Launch Quantum Machine Learning Project for Biomedical Image Analysis

    PRESS RELEASE — Classiq and Pontificia Universidad Católica de Chile (UC Chile) today announced a joint research project to develop hybrid quantum algorithms for biomedical image analysis, assisted by classical machine learning and the NVIDIA CUDA-Q platform for quantum-classical computing.

    The 12-month engagement, titled “Enhancing Pathology through Quantum Computing,” is funded through Avanza UC 2025, the Internal Research and Creation Competition of UC Chile. To the collaborators’ knowledge, it is the first announced consortium in Latin America to combine quantum computing, machine learning and computational pathology.

    The engagement marks quantum computing’s and Classiq’s growing presence in Latin America and reflects the company’s expanding work with academic, research and public-sector institutions, including in health innovation. It also reinforces Chile’s emerging role in quantum computing, AI and advanced technology development.

    Quantum machine learning applies quantum computing methods to machine learning problems, including classification, pattern recognition and complex data analysis. The initial project focus is on renal pathology, an area of growing public health importance in Chile and across Latin America. This includes applying quantum machine learning to computational pathology, with an initial emphasis on kidney lesion classification, automated glomerular segmentation and semantic pattern search across full histological slides. The work will be conducted in collaboration with Dr. Luciano Rebouças and Dr. Washington Conrado, researchers at Fundação Oswaldo Cruz (FIOCRUZ) and professors/researchers at Universidade Federal da Bahia (UFBA) in Brazil, combining expertise in digital pathology, computer vision and biomedical data analysis using curated histopathology datasets, provided by the Brazilian institutions. The research will leverage the Classiq quantum computing software platform and the NVIDIA CUDA-Q platform to leverage a seamless workflow from algorithm development through to simulation and execution.

    “Latin America has the scientific talent, institutional momentum and public health needs to support this next stage of quantum computing applications,” said Nir Minerbi, CEO and co-founder of Classiq.

    “This collaboration brings together quantum software engineering, machine learning and biomedical data expertise in a workflow and project that can help strengthen the regional quantum ecosystem while exploring a practical research path for health.”

    The project will be led by Dr. Dardo Goyeneche of the Faculty of Physics at Pontificia Universidad Católica de Chile. Dr. Goyeneche is the founder and director of QuDIT, the Quantum Development of Information Theory group at UC, which brings together more than 20 students working on quantum information theory and quantum computing. He also directs Project QuAntü, Chile’s first universal quantum computer initiative, currently under construction since December 2025 at the UC Faculty of Physics. The team also includes Dr. Daniel Uzcátegui from Universidad Católica de la Santísima Concepción (UCSC), Chile, whose research at the interface between machine learning and quantum information theory provides a key bridge between the two core domains of this collaboration.

    “This project connects fundamental quantum research with an important biomedical challenge,” said Dr. Goyeneche. “By working with Classiq and collaborators in Chile and Brazil, we are creating a regional platform for quantum machine learning in health, while giving researchers experience with modern quantum software engineering workflows used internationally in research and industry.”

    The research team will use Classiq’s quantum software platform to model, synthesize and optimize quantum convolutional neural networks, variational quantum classifiers and quantum kernel methods. Selected algorithms will be simulated on NVIDIA AI infrastructure, executed on IonQ quantum hardware, and benchmarked against classical machine learning approaches using standard computer vision metrics.

    The collaboration aligns with Chile’s National Strategy for Quantum Technologies 2025–2035, a recently launched government initiative aimed at strengthening the country’s quantum ecosystem and expanding national capabilities in advanced computing, secure communications and scientific innovation. The project also supports UC’s efforts to expand quantum computing research and education as part of the Faculty of Physics’ 2025–2029 strategic plan.

    About Pontificia Universidad Católica de Chile

    Pontificia Universidad Católica de Chile (UC Chile) is one of Latin America’s leading research universities, dedicated to the creation and transfer of knowledge and to providing a values-based education rooted in its Catholic tradition. With rigorous academic standards and international best practices adopted from top universities worldwide, UC Chile maintains a permanent commitment to excellence in service to the Church and society.

    Ranked 116th globally and first in Chile by the QS World University Rankings 2026, UC Chile also leads the country in invention patent applications filed by academic institutions, reflecting a strong focus on research, innovation, and technology transfer. The University is made up of 18 faculties, which include 26 schools and institutes, 7 interdisciplinary institutes, the UC College program, and the Villarrica Campus, together covering all areas of knowledge.

    About Classiq

    Classiq is the leading quantum computing software company, providing the technology that makes it practical for enterprises and researchers to access and harness quantum computing. Classiq’s quantum software engineering platform transforms high-level functional models into optimized, hardware-ready quantum circuits automatically. This enables teams to develop algorithms faster, optimize them for cost and performance, and make quantum applications usable sooner, without deep hardware expertise.

    Through partnerships with global leaders in quantum cloud computing, including major hyperscalers and hardware providers, Classiq ensures that customers including Rolls Royce, Comcast, The BMW Group, Intesa Sanpaolo and many others, can design once and deploy anywhere. Its synthesis technology workflow enables organizations to produce scalable, efficient quantum code that accelerates research and reduces execution cost.

    Classiq, a Fast Company ‘Next Big Thing in Tech 2025’ award winner, is backed by leading global VCs and CVCs, including SoftBank, AMD, Qualcomm and HSBC. Classiq is the global category leader at the forefront of enabling advanced quantum computing applications. Follow Classiq on LinkedIn, X or YouTube, visit the Slack community, GitHub repository and www.classiq.io to learn more.

  • Classiq Integrates with CUDA-Q, Cutting Hybrid Quantum Workflow Times from 67 Minutes to 2.5

    Classiq Integrates with CUDA-Q, Cutting Hybrid Quantum Workflow Times from 67 Minutes to 2.5

    Classiq and NVIDIA have demonstrated a working integration between the Classiq platform and NVIDIA’s CUDA-Q framework, connecting high-level quantum circuit modeling with GPU-accelerated hybrid execution. In a published benchmark, a 31-qubit options pricing workflow that previously took approximately 67 minutes completed in roughly 2.5 minutes on a single NVIDIA A100 GPU.

    The integration is available now through the Classiq development environment.

    What the integration does

    Classiq’s platform operates at the model level. Developers describe quantum algorithms functionally rather than constructing circuits gate by gate. The platform synthesizes optimized circuits based on constraints like qubit count and target hardware compatibility.

    CUDA-Q, NVIDIA’s hybrid quantum-classical framework, handles execution. It allows quantum kernels to run alongside classical code while drawing on GPU acceleration, which happens to be especially useful for hybrid algorithms that invoke quantum circuits repeatedly inside classical optimization loops.

    The integration connects these two stages directly. Developers can generate CUDA-Q kernels from Classiq programs and incorporate them into CUDA-Q hybrid workflows without manually reconstructing circuits at the lower level.

    Why iteration speed matters for hybrid algorithms

    Hybrid quantum algorithms such as VQE, QAOA, Iterative Quantum Amplitude Estimation, are structurally iterative. A classical optimizer drives repeated quantum circuit executions, adjusting parameters between runs. In practice, a single algorithm development session can involve hundreds or thousands of circuit evaluations.

    When each evaluation is slow, the feedback loop stretches. Teams test fewer variants, explore parameter spaces less thoroughly, and identify bottlenecks later. The 27x reduction demonstrated in the IQAE benchmark is significant because experiments that required significant waiting can now run inside a normal development session.

    The benchmark used an options pricing circuit synthesized in Classiq, executed via CUDA-Q on an A100. The problem domain, financial modeling with IQAE, is one where hybrid quantum algorithms are actively being studied, making it a reasonably representative test case rather than a synthetic one.

    What developers can do with this today

    The CUDA-Q integration is accessible through the Classiq platform. The workflow is:

    • Model your quantum algorithm in Classiq using its functional modeling tools
    • Export as a CUDA-Q kernel
    • Integrate the kernel into a CUDA-Q hybrid program alongside classical control logic or optimization routines
    • Execute in a CUDA-Q environment with GPU acceleration

    Classiq’s documentation includes a step-by-step walkthrough for the integration. Access to CUDA-Q environments requires NVIDIA GPU infrastructure, locally or via cloud.

    What to watch

    The integration also currently flows one direction. Classiq models out to CUDA-Q kernels. Whether the reverse, CUDA-Q programs pulling Classiq synthesis inline, s on the roadmap is not stated.

    The benchmark covers simulation. Performance on real QPU hardware through CUDA-Q is a separate question the announcement does not address. Developers targeting actual quantum processors rather than GPU-simulated circuits should evaluate that pathway independently before drawing conclusions from the simulation figures.

    For teams already using Classiq for algorithm design and evaluating hybrid execution environments, this is a concrete integration worth testing now. For those not yet in the Classiq ecosystem, the benchmark makes a case for the combination but adoption would involve evaluating both platforms independently.