{"id":16904,"date":"2026-06-22T15:43:19","date_gmt":"2026-06-22T13:43:19","guid":{"rendered":"https:\/\/qilimanjaro.tech\/?p=16904"},"modified":"2026-06-23T11:08:46","modified_gmt":"2026-06-23T09:08:46","slug":"nvidia-cuda-q-comes-to-qilisdk","status":"publish","type":"post","link":"https:\/\/qilimanjaro.tech\/en\/nvidia-cuda-q-comes-to-qilisdk\/","title":{"rendered":"NVIDIA CUDA-Q Comes to QiliSDK: Upgrade Brings GPU Power to Qilimanjaro\u2019s Multimodal Quantum Stack"},"content":{"rendered":"\t\t
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NVIDIA's CUDA-Q now powers GPU emulation inside QiliSDK, extending Qilimanjaro's unique multimodal approach across classical and quantum backends, from CPU and GPU to analog and digital QPUs.<\/h2>\t\t\t\t<\/div>\n\t\t\t\t
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Most quantum software runs on a single kind of quantum hardware, using either the digital or the analog paradigm, or on CPU\/GPU emulators. QiliSDK spans them all. It runs across a variety of backends: CPU, GPU, digital QPUs (dQPU) and Qilimanjaro’s analog QPUs (aQPU). This is the multimodality Qilimanjaro is building from, and the vision behind it: that the coexistence of modalities will be the future of supercomputing.<\/span><\/p>

QiliSDK<\/span> is Qilimanjaro’s Python framework for developing, running and emulating both digital and analog quantum algorithms. Its modular design makes it easy to prototype circuits, build Hamiltonians, design variational workflows and quantum-reservoir models, then deploy them on local or remote backends, classical or quantum. The same high-level program can target a laptop CPU, a GPU, a digital QPU, or Qilimanjaro’s analog QPU through the SpeQtrum cloud and on-prem.<\/span><\/p>

Today we are adding a new backend to that set: an NVIDIA CUDA-Q-powered GPU backend, built for production-scale emulation of quantum workflows on classical hardware, including state-vector and tensor-network emulation. Nothing else in the codebase changes.<\/span><\/p>

The following snippet illustrates how a user can create an analog evolution, then dispatch it for GPU emulation:<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\n\t\t\t\t\tfrom qilisdk.analog import Schedule, X, Y, Z\nfrom qilisdk.backends import CudaBackend\nfrom qilisdk.core import QTensor\nfrom qilisdk.functionals import AnalogEvolution\nfrom qilisdk.readout import Readout\n\n# Simple quantum annealing example \nnqubits = 2\ndriver = -sum(X(i) for i in range(nqubits))\nproblem = sum(Z(i)*Z(i+1) for i in range(nqubits-1))\nanalog_functional = AnalogEvolution(\n\tschedule=Schedule.linear(driver, problem, total_time=10, dt=0.1),\n\tinitial_state=QTensor.uniform(nqubits)\n)\nanalog_readout = Readout().with_expectation(observables=[problem])\n\n# Run on CUDA-Q GPU backend\ncuda_backend = CudaBackend()\nanalog_result = cuda_backend.execute(analog_functional, analog_readout)\nprint(analog_result)\n<\/xmp>\n\t\t\t\t<\/code>\n\t\t\t<\/pre>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d608db1 elementor-widget elementor-widget-text-editor\" data-id=\"d608db1\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">This creates a simple quantum annealing example: a linear ramp between an initial Hamiltonian <img decoding=\"async\" src=\"https:\/\/qilimanjaro.tech\/wp-content\/uploads\/2026\/06\/initial.svg\" alt=\"\" width=\"150 \" height=\"100\" \/><br \/>to a final Hamiltonian <img decoding=\"async\" src=\"https:\/\/qilimanjaro.tech\/wp-content\/uploads\/2026\/06\/final.svg\" alt=\"\" width=\"150 \" height=\"100\" \/>, here just for two qubits. By starting in the ground state of the initial Hamiltonian and slowly transitioning to the final one, the <\/span><strong><a href=\"https:\/\/en.wikipedia.org\/wiki\/Adiabatic_theorem\">adiabatic theorem<\/a><\/strong><span style=\"font-weight: 400;\"> guarantees we remain in the ground state, as long as the evolution is slow compared to the gap between the ground state and the first excited state. This leads us to the ground state of our problem Hamiltonian, which is the solution we are after. Here it correctly gives -1 as our final energy, as one would expect from trying to minimize <\/span><span style=\"font-weight: 400;\">Z<\/span><span style=\"font-weight: 400;\">0<\/span> <span style=\"font-weight: 400;\">Z<\/span><span style=\"font-weight: 400;\">1<\/span><span style=\"font-weight: 400;\">.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d19ebdf elementor-widget elementor-widget-text-editor\" data-id=\"d19ebdf\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<h2><strong>Classical Emulation as a Development Layer<\/strong><\/h2><p><span style=\"font-weight: 400;\">Classical emulation is a permanent part of how quantum teams work. Before running anything on real hardware, teams use emulation to prototype circuits, study system behavior, characterize noise, and establish the benchmarks that quantum results are measured against. It also handles the classical side of hybrid workflows, the pre- and post-processing that wraps every quantum call. Building without it is not a realistic option at any stage of development.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-225cf32 elementor-widget elementor-widget-text-editor\" data-id=\"225cf32\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<h2><strong>Why NVIDIA GPUs shine for emulating quantum workflows on classical systems<\/strong><\/h2><p><span style=\"font-weight: 400;\">The question then is how to run that emulation efficiently. Emulating a quantum workflow on a classical computer means tracking the full quantum state, whose size grows exponentially with qubit count. On a CPU, this stays practical up to roughly 25 qubits before memory and bandwidth become hard ceilings; beyond that, run times climb from seconds to hours. GPUs, with their wide memory buses, massive parallel arithmetic and multi-GPU NVLink topologies, are today&#8217;s state of the art for this kind of workload. They push the practical state-vector frontier to 30 qubits on a single node, and well beyond when tensor-network or distributed-memory methods come into play. NVIDIA&#8217;s CUDA-Q is, today, the most productive way to target those GPUs: a clean kernel-based model that maps directly onto NVIDIA hardware, well-tested state-vector and tensor-network engines, and multi-GPU and multi-node execution out of the box.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e86e02c elementor-widget elementor-widget-text-editor\" data-id=\"e86e02c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<h2><strong>CUDA-Q as the GPU layer<\/strong><\/h2><p><span style=\"font-weight: 400;\">NVIDIA&#8217;s CUDA-Q is, today, the most performant\u00a0 way to target GPUs for quantum emulation. It exposes a clean kernel-based model that maps directly onto NVIDIA hardware. CUDA\u00b4Q also\u00a0 integrates\u00a0 well-tested state-vector and tensor-network engines, supports multi-GPU and multi-node execution, and is actively adopted by\u00a0 the broader quantum-software ecosystem. In summary, CUDA-Q is an open-source quantum development platform that orchestrates the hardware and software needed to run useful, large-scale <\/span><strong><a href=\"https:\/\/www.nvidia.com\/en-us\/glossary\/quantum-computing\/\">quantum computing<\/a><\/strong><span style=\"font-weight: 400;\"> applications. CUDA-Q streamlines hybrid application development and promotes productivity and scalability in quantum computing. It offers a unified programming model designed for a hybrid setting\u2014that is, CPUs, GPUs, and QPUs working together. CUDA-Q contains support for programming in Python and in C++.\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">Rather than reinventing GPU integration, QiliSDK wraps CUDA-Q as a backend, so users get NVIDIA-grade performance without writing CUDA-Q code themselves.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-356a3cf elementor-widget elementor-widget-text-editor\" data-id=\"356a3cf\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<h2><strong>CUDA-Q backend in QiliSDK<\/strong><\/h2><p><span style=\"font-weight: 400;\">QiliSDK exposes CUDA-Q through a single class: <\/span><b>CudaBackend<\/b><span style=\"font-weight: 400;\">. At construction time, the user picks a emulation method and optionally attaches a noise model.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-f38f7c8 e-con-full e-flex e-con e-child\" data-id=\"f38f7c8\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-e96abb6 elementor-widget elementor-widget-image\" data-id=\"e96abb6\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img fetchpriority=\"high\" decoding=\"async\" width=\"800\" height=\"455\" src=\"https:\/\/qilimanjaro.tech\/wp-content\/uploads\/2026\/06\/QiliSDK_Cuda-1024x582.jpg\" class=\"attachment-large size-large wp-image-16920\" alt=\"\" srcset=\"https:\/\/qilimanjaro.tech\/wp-content\/uploads\/2026\/06\/QiliSDK_Cuda-1024x582.jpg 1024w, https:\/\/qilimanjaro.tech\/wp-content\/uploads\/2026\/06\/QiliSDK_Cuda-300x171.jpg 300w, https:\/\/qilimanjaro.tech\/wp-content\/uploads\/2026\/06\/QiliSDK_Cuda-768x437.jpg 768w, https:\/\/qilimanjaro.tech\/wp-content\/uploads\/2026\/06\/QiliSDK_Cuda-1536x873.jpg 1536w, https:\/\/qilimanjaro.tech\/wp-content\/uploads\/2026\/06\/QiliSDK_Cuda.jpg 1810w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d2ff868 elementor-widget elementor-widget-text-editor\" data-id=\"d2ff868\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Figure 1: Diagram of the integration of CudaBackend class in QiliSDK involving CUDA-Q backend targets and noise model.<\/span><\/p><p><span style=\"font-weight: 400;\"> <br \/>The backend then auto-configures the underlying CUDA-Q target: for <strong>state-vector<\/strong> emulation it uses the <span style=\"color: #339966;\">nvidia<\/span> target with fp32 or fp64 precision when a GPU is detected, <span style=\"color: #339966;\">nvidia-mgpu<\/span> when multiple GPUs are available, or falls back to cpu otherwise; <strong>tensor_network<\/strong> routes to CUDA-Q&#8217;s <span style=\"color: #339966;\">tensornet<\/span> engine for full tensor-network contraction; and <strong>matrix_product_state<\/strong> routes to the <span style=\"color: #339966;\">tensornet-mps<\/span> target, which exploits 1D MPS structure with bond-dimension truncation to keep shallow or low-entanglement circuits cheap; and analog workflows switch CUDA-Q to its <span style=\"color: #339966;\">dynamics<\/span> target. The same CudaBackend object therefore covers single-GPU state-vector, multi-GPU pooled emulation, tensor-network and matrix-product-state engines, and open-system dynamics.<\/span><\/p><p><span style=\"font-weight: 400;\"><br \/>For <strong>digital circuits<\/strong>, CudaBackend builds a <span style=\"color: #339966;\">cudaq.Kernel<\/span>, transpiles multi-controlled gates into native form, and dispatches each QiliSDK gate through dedicated handler functions to its CUDA-Q equivalent, finishing by either efficiently sampling from the state or returning the final full state of the system. For<strong> analog workflows<\/strong> it compiles the schedule&#8217;s time-dependent Hamiltonians into a CUDA-Q <span style=\"color: #339966;\">OperatorSum<\/span>, maps Pauli operators directly onto <span style=\"color: #339966;\">spin.x\/y\/z\/i<\/span>, and runs <span style=\"color: #339966;\">cudaq.evolve<\/span> over the schedule&#8217;s time grid.<\/span><\/p><p><span style=\"font-weight: 400;\"><br \/>If the user wants a noisy emulation, they can provide a QiliSDK NoiseModel, which is converted into a <span style=\"color: #339966;\">cudaq.NoiseModel<\/span>: bit-flip, phase-flip, and depolarising channels map to their native efficient CUDA-Q counterparts, while the more expressive Kraus- and Lindblad-based channels map to the general CUDA-Q noise channels. Noise can be registered globally, per gate type, per qubit, or per (gate, qubit) pair. Parameter perturbations and readout-assignment errors (i.e. p01\/p10) are also available, and are handled as pre\/post processing.<\/span><\/p><p><span style=\"font-weight: 400;\"><br \/>To summarize, the user can write their quantum workflow as high-level QiliSDK code, which is then automatically converted into an optimized representation that can be efficiently executed on NVIDIA GPUs.<br \/><\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4e9f8f4 elementor-widget elementor-widget-text-editor\" data-id=\"4e9f8f4\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<h2><strong>QiliSDK CudaBackend performance<\/strong><\/h2><p><span style=\"font-weight: 400;\">QiliSDK\u2019s CudaBackend <\/span><span style=\"font-weight: 400;\">scales efficiently from single-GPU to multi-GPU execution<\/span><span style=\"font-weight: 400;\">, as the following benchmarks illustrate. We consider the same case as in the above example: an annealing task from an X Hamiltonian to a ZZ Hamiltonian. We do this by splitting the evolution into 100 steps, and then taking 1.000 samples of the final state. Although this is an analog evolution, it can be emulated on digital hardware using Trotterization. By first Trotterizing and then emulating via the various digital CudaBackend emulation methods, we observe great scaling performance across both single- and multi-GPU configurations.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-c192735 e-con-full e-flex e-con e-child\" data-id=\"c192735\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-9bcda95 elementor-widget elementor-widget-image\" data-id=\"9bcda95\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"800\" height=\"468\" src=\"https:\/\/qilimanjaro.tech\/wp-content\/uploads\/2026\/06\/graph1-1-1024x599.jpg\" class=\"attachment-large size-large wp-image-16921\" alt=\"\" srcset=\"https:\/\/qilimanjaro.tech\/wp-content\/uploads\/2026\/06\/graph1-1-1024x599.jpg 1024w, https:\/\/qilimanjaro.tech\/wp-content\/uploads\/2026\/06\/graph1-1-300x175.jpg 300w, https:\/\/qilimanjaro.tech\/wp-content\/uploads\/2026\/06\/graph1-1-768x449.jpg 768w, https:\/\/qilimanjaro.tech\/wp-content\/uploads\/2026\/06\/graph1-1-1536x898.jpg 1536w, https:\/\/qilimanjaro.tech\/wp-content\/uploads\/2026\/06\/graph1-1.jpg 1543w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-faf3cce elementor-widget elementor-widget-text-editor\" data-id=\"faf3cce\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Figure 2: Benchmarking results for a simple annealing task, showing the time taken to perform the evolution versus the number of qubits in the problem. The results show how QiliSDK\u2019s CudaBackend can leverage multiple NVIDIA GPUs to efficiently scale emulations up to 30 qubits.<\/span><\/p><p><span style=\"font-weight: 400;\">To take full advantage of the CUDA-Q software stack, the NVIDIA Dynamics backend can be used to perform the evolution directly. Following this approach delivers significantly better performance than the Trotterization digital emulation:<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-7ebf8b1 e-con-full e-flex e-con e-child\" data-id=\"7ebf8b1\" data-element_type=\"container\" data-e-type=\"container\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-f1f78c5 elementor-widget elementor-widget-image\" data-id=\"f1f78c5\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"800\" height=\"455\" src=\"https:\/\/qilimanjaro.tech\/wp-content\/uploads\/2026\/06\/image-18-1024x582.jpg\" class=\"attachment-large size-large wp-image-16922\" alt=\"\" srcset=\"https:\/\/qilimanjaro.tech\/wp-content\/uploads\/2026\/06\/image-18-1024x582.jpg 1024w, https:\/\/qilimanjaro.tech\/wp-content\/uploads\/2026\/06\/image-18-300x171.jpg 300w, https:\/\/qilimanjaro.tech\/wp-content\/uploads\/2026\/06\/image-18-768x437.jpg 768w, https:\/\/qilimanjaro.tech\/wp-content\/uploads\/2026\/06\/image-18.jpg 1363w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e9a813b elementor-widget elementor-widget-text-editor\" data-id=\"e9a813b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Figure 3: B<\/span><span style=\"font-weight: 400;\">enchmarking results for a simple annealing task, showing the time taken to perform the evolution versus the number of qubits in the problem. QiliSDK\u2019s CudaBackend using Dynamics significantly outperforms the Trotterized digital evolutions.<\/span><span style=\"font-weight: 400;\"><br \/><\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-45c43db elementor-widget elementor-widget-text-editor\" data-id=\"45c43db\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t\t\t\t\t\t<h1><strong>Bringing CUDA-Q performance to QiliSDK<\/strong><\/h1><p><span style=\"font-weight: 400;\">QiliSDK now runs digital circuits, analog quantum workflows, and everything in between on a single codebase, across CPU, GPU, and QPU backends. The CUDA-Q integration adds GPU acceleration to that set without requiring any changes to existing programs. The same code that runs on a laptop also runs on a multi-GPU cluster.<\/span><\/p><p><span style=\"font-weight: 400;\">That is the stack Qilimanjaro is building toward: one where the boundaries between classical and quantum execution are handled by the software, not by the user.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>NVIDIA&#8217;s CUDA-Q now powers GPU emulation inside QiliSDK, extending Qilimanjaro&#8217;s unique multimodal approach across classical and quantum backends, from CPU and GPU to analog and digital QPUs. Most quantum software runs on a single kind of quantum hardware, using either the digital or the analog paradigm, or on CPU\/GPU emulators. QiliSDK spans them all. It [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":16947,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[42],"tags":[],"class_list":["post-16904","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-technical-blogpost"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.9 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>NVIDIA CUDA-Q Comes to QiliSDK: Upgrade Brings GPU Power to Qilimanjaro\u2019s Multimodal Quantum Stack - Qilimanjaro<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/qilimanjaro.tech\/en\/nvidia-cuda-q-comes-to-qilisdk\/\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"NVIDIA CUDA-Q Comes to QiliSDK: Upgrade Brings GPU Power to Qilimanjaro\u2019s Multimodal Quantum Stack - Qilimanjaro\" \/>\n<meta property=\"og:description\" content=\"NVIDIA&#8217;s CUDA-Q now powers GPU emulation inside QiliSDK, extending Qilimanjaro&#8217;s unique multimodal approach across classical and quantum backends, from CPU and GPU to analog and digital QPUs. 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