Alternatives to Neuri
Compare Neuri alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to Neuri in 2026. Compare features, ratings, user reviews, pricing, and more from Neuri competitors and alternatives in order to make an informed decision for your business.
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QC Ware Forge
QC Ware
Unique and efficient turn-key algorithms for data scientists. Powerful circuit building blocks for quantum engineers. Turn-key algorithm implementations for data scientists, financial analysts, and engineers. Explore problems in binary optimization, machine learning, linear algebra, and monte carlo sampling on simulators and real quantum hardware. No prior experience with quantum computing is required. Use NISQ data loader circuits to load classical data into quantum states to use with your algorithms. Use circuit building blocks for linear algebra with distance estimation and matrix multiplication circuits. Use our circuit building blocks to create your own algorithms. Get a significant performance boost for D-Wave hardware and use the latest improvements for gate-based approaches. Try out quantum data loaders and algorithms with guaranteed speed-ups on clustering, classification, and regression.Starting Price: $2,500 per hour -
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QX Simulator
Quantum Computing Simulation
The realization of large-scale physical quantum computers appears to be challenging, alongside the efforts to design quantum computers, significant efforts are focusing on the development of useful quantum algorithms. In the absence of a large physical quantum computer, an accurate software simulation of quantum computers on a classical computer is required to simulate the execution of those quantum algorithms and to study the behavior of a quantum computer and improve its design. Besides simulating error-free execution quantum circuits on a perfect quantum computer, the QX simulator can simulate realistic noisy execution using different error models such as the depolarizing noise. The user can activate the error model and define a physical error probability to simulate a specific target quantum computer. This error rate can be defined based on the gate fidelity and the qubit decoherence of the target platform. -
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LIQUi|>
Microsoft
LIQUi|> is a software architecture and tool suite for quantum computing. It includes a programming language, optimization and scheduling algorithms, and quantum simulators. LIQUi|> can be used to translate a quantum algorithm written in the form of a high-level program into the low-level machine instructions for a quantum device. LIQUi|> is being developed by the quantum architectures and computation Group (QuArC) at Microsoft Research. To aid in the development and understanding of quantum protocols, quantum algorithms, quantum error correction, and quantum devices, QuArC has developed an extensive software platform called LIQUi|>. LIQUi|> allows the simulation of Hamiltonians, quantum circuits, quantum stabilizer circuits, and quantum noise models, and supports client, service, and cloud operation. -
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InQuanto
Quantinuum
Quantum computing offers a path forward to rapid and cost-effective development of new molecules and materials. InQuanto, a state-of-the-art quantum computational chemistry platform, represents a critical step toward this goal. Quantum chemistry aims to accurately describe and predict the fundamental properties of matter and hence is a powerful tool in the design and development of new molecules and materials. However, molecules and materials of industrial relevance are complex and not easy to accurately simulate. Today’s capabilities force a trade to either use highly accurate methods on the smallest-sized systems or use approximating techniques. InQuanto’s modular workflow enables both computational chemists and quantum algorithm developers to easily mix and match the latest quantum algorithms with advanced subroutines and error mitigation techniques to get the best out of today’s quantum platforms. -
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Neural Designer
Artelnics
Neural Designer is a powerful software tool for developing and deploying machine learning models. It provides a user-friendly interface that allows users to build, train, and evaluate neural networks without requiring extensive programming knowledge. With a wide range of features and algorithms, Neural Designer simplifies the entire machine learning workflow, from data preprocessing to model optimization. In addition, it supports various data types, including numerical, categorical, and text, making it versatile for domains. Additionally, Neural Designer offers automatic model selection and hyperparameter optimization, enabling users to find the best model for their data with minimal effort. Finally, its intuitive visualizations and comprehensive reports facilitate interpreting and understanding the model's performance.Starting Price: $2495/year (per user) -
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Amazon Braket
Amazon
Easily work with different types of quantum computers and circuit simulators using a consistent set of development tools. Build quantum projects on a trusted cloud with simple pricing and management controls for both quantum and classical workloads. Run hybrid quantum-classical algorithms faster with priority access to quantum computers and no classical infrastructure to manage. Reserve dedicated device access and engage directly with quantum computing specialists using Braket Direct. Accelerate scientific discovery with tools for algorithm development and support from the AWS Cloud Credit for Research Program. Push the boundaries of quantum hardware research with easy access to superconducting, trapped ion, and neutral atom devices. Bring software for quantum computing to market rapidly with Amazon Braket’s software development kit, simple pricing, and workflow management.Starting Price: $0.08000 per month -
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QSimulate
QSimulate
QSimulate offers a suite of quantum simulation platforms that leverage quantum mechanics to solve complex, industrial-scale problems in life sciences and materials science. The QSP Life platform provides unique quantum-powered methods for drug discovery and optimization, enabling first-of-a-kind quantum simulations of ligand-protein interactions applicable throughout the computational drug discovery process. The QUELO platform performs hybrid quantum/classical free energy calculations, offering users the ability to run relative free energy calculations using the free energy perturbation (FEP) approach. Additionally, QSimulate's technology enables groundbreaking advances in quantum mechanics/molecular mechanics (QM/MM) simulations for large protein modeling. For materials science, the QSP Materials platform democratizes quantum mechanical simulations, allowing experimentalists to automate complex workflows without the need for specialization. -
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QuEST
QuEST
The Quantum exact simulation toolkit is a high-performance simulator of quantum circuits, state-vectors and density matrices. QuEST uses multithreading, GPU acceleration and distribution to run lightning first on laptops, desktops and networked supercomputers. QuEST just works; it is stand-alone, requires no installation, and is trivial to compile and get running. QuEST has no setup; it can be downloaded, compiled and run in a matter of seconds. QuEST has no external dependencies and compiles natively on Windows, Linux and MacOS. Whether on a laptop, a desktop, a supercomputer, a microcontroller, or in the cloud, you can almost always get QuEST running with only a few terminal commands. -
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Neuralhub
Neuralhub
Neuralhub is a system that makes working with neural networks easier, helping AI enthusiasts, researchers, and engineers to create, experiment, and innovate in the AI space. Our mission extends beyond providing tools; we're also creating a community, a place to share and work together. We aim to simplify the way we do deep learning today by bringing all the tools, research, and models into a single collaborative space, making AI research, learning, and development more accessible. Build a neural network from scratch or use our library of common network components, layers, architectures, novel research, and pre-trained models to experiment and build something of your own. Construct your neural network with one click. Visually see and interact with every component in the network. Easily tune hyperparameters such as epochs, features, labels and much more. -
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Google Cirq
Google
Cirq is a Python software library for writing, manipulating, and optimizing quantum circuits, and then running them on quantum computers and quantum simulators. Cirq provides useful abstractions for dealing with today’s noisy intermediate-scale quantum computers, where details of the hardware are vital to achieving state-of-the-art results. Cirq comes with built-in simulators, both for wave functions and for density matrices. These can handle noisy quantum channels using monte carlo or full density matrix simulations. In addition, Cirq works with a state-of-the-art wavefunction simulator: qsim. These simulators can be used to mock quantum hardware with the quantum virtual machine. Cirq is used to run experiments on Google's quantum processors. Learn more about the latest experiments and access the code to se how to run them yourself. -
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Quantum Inspire
QuTech
Run your own quantum algorithms on one of our simulators or hardware backends and experience the possibilities of quantum computing. Note that Spin-2 is currently being upgraded and is no longer available. We have multiple simulators and real hardware chips available. Find out what they can do for you. Quantum Inspire is built using first-rate engineering practices. Starting from experimental setups, a layered and modular system was designed to end up with a solid and robust hardware system. This quantum computer consists of a number of layers including quantum chip hardware, classical control electronics, a quantum compiler, and a software front-end with a cloud-accessible web interface. They can act as technology accelerators because only through careful analysis of the individual system layers and their interdependencies it become possible to detect the gaps and necessary next steps in the innovation roadmap and supply chain. -
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Azure Quantum
Microsoft
Use state-of-the-art cloud tools and learning resources to help you build and refine quantum algorithms. Gain access to a diverse portfolio of today’s quantum hardware. Access a diverse portfolio of today’s quantum hardware to build toward the emergence of fault-tolerant quantum systems. Navigate complexity and develop new skills with world-class onboarding and education resources including Microsoft Learn, Quantum katas tutorials, industry case studies, and a university curriculum. Use the Azure Quantum resource estimator tool to estimate the number of logical and physical qubits and runtime required to execute quantum applications on future-scaled quantum computers. Determine the number of qubits needed for a quantum solution and evaluate the differences across qubit technologies. Prepare and refine quantum solutions to run on future-scaled quantum machines. -
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QANplatform
QANplatform
Developers and enterprises can build Quantum-resistant smart-contracts, DApps, DeFi solutions, NFTs, tokens, Metaverse on top of the QAN blockchain platform in any programming language. QANplatform is the first Hyperpolyglot Smart Contract platform where developers can code in any programming language and also get rewarded for writing high-quality code reusable by others. The Quantum threat is very real. Existing chains can not defend against it. QAN is resistant against it from ground up, your future funds are safe. Quantum-resistant algorithms — also known as post-quantum, quantum-secure, or quantum-safe — are cryptographic algorithms that can fend off attacks from quantum computers. Quantum-resistant algorithms — also known as post-quantum, quantum-secure, or quantum-safe — are cryptographic algorithms that can fend off attacks from quantum computers. -
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Microsoft Cognitive Toolkit
Microsoft
The Microsoft Cognitive Toolkit (CNTK) is an open-source toolkit for commercial-grade distributed deep learning. It describes neural networks as a series of computational steps via a directed graph. CNTK allows the user to easily realize and combine popular model types such as feed-forward DNNs, convolutional neural networks (CNNs) and recurrent neural networks (RNNs/LSTMs). CNTK implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers. CNTK can be included as a library in your Python, C#, or C++ programs, or used as a standalone machine-learning tool through its own model description language (BrainScript). In addition you can use the CNTK model evaluation functionality from your Java programs. CNTK supports 64-bit Linux or 64-bit Windows operating systems. To install you can either choose pre-compiled binary packages, or compile the toolkit from the source provided in GitHub. -
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NeuroIntelligence
ALYUDA
NeuroIntelligence is a neural networks software application designed to assist neural network, data mining, pattern recognition, and predictive modeling experts in solving real-world problems. NeuroIntelligence features only proven neural network modeling algorithms and neural net techniques; software is fast and easy-to-use. Visualized architecture search, neural network training and testing. Neural network architecture search, fitness bars, network training graphs comparison. Training graphs, dataset error, network error, weights and errors distribution, neural network input importance. Testing, actual vs. output graph, scatter plot, response graph, ROC curve, confusion matrix. The interface of NeuroIntelligence is optimized to solve data mining, forecasting, classification and pattern recognition problems. You can create a better solution much faster using the tool's easy-to-use GUI and unique time-saving capabilities.Starting Price: $497 per user -
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NVIDIA DIGITS
NVIDIA DIGITS
The NVIDIA Deep Learning GPU Training System (DIGITS) puts the power of deep learning into the hands of engineers and data scientists. DIGITS can be used to rapidly train the highly accurate deep neural network (DNNs) for image classification, segmentation and object detection tasks. DIGITS simplifies common deep learning tasks such as managing data, designing and training neural networks on multi-GPU systems, monitoring performance in real-time with advanced visualizations, and selecting the best performing model from the results browser for deployment. DIGITS is completely interactive so that data scientists can focus on designing and training networks rather than programming and debugging. Interactively train models using TensorFlow and visualize model architecture using TensorBoard. Integrate custom plug-ins for importing special data formats such as DICOM used in medical imaging. -
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Qiskit
IBM
Qiskit includes a comprehensive set of quantum gates and a variety of pre-built circuits so users at all levels can use Qiskit for research and application development. The transpiler translates Qiskit code into an optimized circuit using a backend’s native gate set, allowing users to program for any quantum processor. Users can transpile with Qiskit's default optimization, use a custom configuration or develop their own plugin. Qiskit helps users schedule and run quantum programs on a variety of local simulators and cloud-based quantum processors. It supports several quantum hardware designs, such as superconducting qubits and trapped ions. Ready to explore Qiskit’s capabilities for yourself? Learn how to run Qiskit in the cloud or your local Python environment. -
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Quantinuum Nexus
Quantinuum
Quantinuum Nexus is a cloud-based platform that enables users to seamlessly run, review, and collaborate on quantum computing projects. It integrates support for various quantum hardware providers using the pytket quantum programming tools to optimize circuit performance and translation between different backends. Key features include a single, cloud-based interface for multiple quantum backends; preinstalled and dedicated simulators, including our emulator; a hosted and preconfigured JupyterHub environment; automated storage of circuits, compilation passes, and results; and secure sharing of data with team members. Nexus also stores everything you need to recreate your experiment in one place, meaning a full snapshot of the backend, the settings and variables you used, and more. Combined with easy data sharing and storage, you can stop worrying about the logistics of data management. -
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Quantum Programming Studio
Quantum Programming Studio
Circuit can be exported to multiple quantum programming languages/frameworks and can be executed on various simulators and quantum computers. You can use simple drag & drop user interface to assemble circuit diagram which automatically translates to code, and vice versa - you can type the code and the diagram is updated accordingly. QPS Client is running on your local machine (or in the cloud) where your quantum programming environment is installed. It opens a secure websocket connection with Quantum Programming Studio server and executes quantum circuits (that you design in the web UI) on your local simulator or on a real quantum computer. -
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Q-CTRL
Q-CTRL
Infrastructure software to power the quantum future. Quantum technology promises to transform the economy. We expand the utility of quantum computers and deliver new quantum sensing capabilities, all through software. Quantum infrastructure software transforms bare-metal quantum processors into useful computational tools. Our technology unleashes the hidden performance inside of the most powerful computers so you can achieve more. Add automation and performance management to my QC platform. Professional-grade toolkits to design, automate, and scale quantum hardware and controls. Unleash latent hardware performance with fully integrated performance management for cloud quantum computing platforms. Automatically reduce error and boost algorithmic success on cloud-accessible quantum computers. Professional-grade toolkits to design, automate, and scale quantum hardware and controls. -
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TFLearn
TFLearn
TFlearn is a modular and transparent deep learning library built on top of Tensorflow. It was designed to provide a higher-level API to TensorFlow in order to facilitate and speed up experimentations while remaining fully transparent and compatible with it. Easy-to-use and understand high-level API for implementing deep neural networks, with tutorial and examples. Fast prototyping through highly modular built-in neural network layers, regularizers, optimizers, metrics. Full transparency over Tensorflow. All functions are built over tensors and can be used independently of TFLearn. Powerful helper functions to train any TensorFlow graph, with support of multiple inputs, outputs, and optimizers. Easy and beautiful graph visualization, with details about weights, gradients, activations and more. The high-level API currently supports most of the recent deep learning models, such as Convolutions, LSTM, BiRNN, BatchNorm, PReLU, Residual networks, Generative networks. -
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Zebra by Mipsology
Mipsology
Zebra by Mipsology is the ideal Deep Learning compute engine for neural network inference. Zebra seamlessly replaces or complements CPUs/GPUs, allowing any neural network to compute faster, with lower power consumption, at a lower cost. Zebra deploys swiftly, seamlessly, and painlessly without knowledge of underlying hardware technology, use of specific compilation tools, or changes to the neural network, the training, the framework, and the application. Zebra computes neural networks at world-class speed, setting a new standard for performance. Zebra runs on highest-throughput boards all the way to the smallest boards. The scaling provides the required throughput, in data centers, at the edge, or in the cloud. Zebra accelerates any neural network, including user-defined neural networks. Zebra processes the same CPU/GPU-based trained neural network with the same accuracy without any change. -
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Rigetti Quantum Cloud Services (QCS)
Rigetti Computing
We make it possible for everyone to think bigger, create faster, and see further. By infusing AI and machine learning, our quantum solutions give you the power to solve the world’s most important and pressing problems. Thermodynamics sparked the Industrial revolution. Electromagnetism ushered in the information age, now, quantum computers are harnessing the unique information processing capability of quantum mechanics to exponentially reduce the time and energy needed for high-impact computing. With the first paradigm-shifting advance since the integrated circuit, quantum computing is poised to transform every global market. The gap between first movers and fast followers will be difficult to overcome. -
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ConvNetJS
ConvNetJS
ConvNetJS is a Javascript library for training deep learning models (neural networks) entirely in your browser. Open a tab and you're training. No software requirements, no compilers, no installations, no GPUs, no sweat. The library allows you to formulate and solve neural networks in Javascript, and was originally written by @karpathy. However, the library has since been extended by contributions from the community and more are warmly welcome. The fastest way to obtain the library in a plug-and-play way if you don't care about developing is through this link to convnet-min.js, which contains the minified library. Alternatively, you can also choose to download the latest release of the library from Github. The file you are probably most interested in is build/convnet-min.js, which contains the entire library. To use it, create a bare-bones index.html file in some folder and copy build/convnet-min.js to the same folder. -
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Deci
Deci AI
Easily build, optimize, and deploy fast & accurate models with Deci’s deep learning development platform powered by Neural Architecture Search. Instantly achieve accuracy & runtime performance that outperform SoTA models for any use case and inference hardware. Reach production faster with automated tools. No more endless iterations and dozens of different libraries. Enable new use cases on resource-constrained devices or cut up to 80% of your cloud compute costs. Automatically find accurate & fast architectures tailored for your application, hardware and performance targets with Deci’s NAS based AutoNAC engine. Automatically compile and quantize your models using best-of-breed compilers and quickly evaluate different production settings. Automatically compile and quantize your models using best-of-breed compilers and quickly evaluate different production settings. -
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Bayesforge
Quantum Programming Studio
Bayesforge™ is a Linux machine image that curates the very best open source software for the data scientist who needs advanced analytical tools, as well as for quantum computing and computational mathematics practitioners who seek to work with one of the major QC frameworks. The image combines common machine learning frameworks, such as PyTorch and TensorFlow, with open source software from D-Wave, Rigetti as well as the IBM Quantum Experience and Google's new quantum computing language Cirq, as well as other advanced QC frameworks. For instance our quantum fog modeling framework, and our quantum compiler Qubiter which can cross-compile to all major architectures. All software is made accessible through the Jupyter WebUI which, due to its modular architecture, allows the user to code in Python, R, and Octave. -
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D-Wave
D-Wave
Our singular focus is to help customers achieve real value by using quantum computing for practical business applications. You may be surprised to learn that our enterprise customers have already built hundreds of quantum applications across many industries. The powerful combination of the Advantage™ quantum system and the Leap™ hybrid solver services enable the first in-production quantum applications demonstrating business benefit. D-Wave is the practical quantum computing company delivering real business value for manufacturing, supply chain and logistics, scheduling, and mobility applications today. Quantum computing is already helping to optimize many key parts of the value chain in Industry 4.0. -
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Fido
Fido
Fido is a light-weight, open-source, and highly modular C++ machine learning library. The library is targeted towards embedded electronics and robotics. Fido includes implementations of trainable neural networks, reinforcement learning methods, genetic algorithms, and a full-fledged robotic simulator. Fido also comes packaged with a human-trainable robot control system as described in Truell and Gruenstein. While the simulator is not in the most recent release, it can be found for experimentation on the simulator branch. -
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DeepCube
DeepCube
DeepCube focuses on the research and development of deep learning technologies that result in improved real-world deployment of AI systems. The company’s numerous patented innovations include methods for faster and more accurate training of deep learning models and drastically improved inference performance. DeepCube’s proprietary framework can be deployed on top of any existing hardware in both datacenters and edge devices, resulting in over 10x speed improvement and memory reduction. DeepCube provides the only technology that allows efficient deployment of deep learning models on intelligent edge devices. After the deep learning training phase, the resulting model typically requires huge amounts of processing and consumes lots of memory. Due to the significant amount of memory and processing requirements, today’s deep learning deployments are limited mostly to the cloud. -
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BQSKit
Berkeley Lab
BQSKit stands on its own as an end-to-end compiling solution by combining state-of-the-art partitioning, synthesis, and instantiation algorithms. The framework is built in an easy-to-access and quick-to-extend fashion, allowing users to best tailor a workflow to suit their specific domain. Global circuit optimization is the process of taking a quantum program, given as a quantum circuit, and reducing (optimizing) its depth. The depth of a quantum circuit is directly related to the program’s runtime and the probability of error in the final result. BQSKit uses a unique strategy that combines circuit partitioning, synthesis, and instantiation to optimize circuits far beyond what traditional optimizing compilers can do. -
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Automaton AI
Automaton AI
With Automaton AI’s ADVIT, create, manage and develop high-quality training data and DNN models all in one place. Optimize the data automatically and prepare it for each phase of the computer vision pipeline. Automate the data labeling processes and streamline data pipelines in-house. Manage the structured and unstructured video/image/text datasets in runtime and perform automatic functions that refine your data in preparation for each step of the deep learning pipeline. Upon accurate data labeling and QA, you can train your own model. DNN training needs hyperparameter tuning like batch size, learning, rate, etc. Optimize and transfer learning on trained models to increase accuracy. Post-training, take the model to production. ADVIT also does model versioning. Model development and accuracy parameters can be tracked in run-time. Increase the model accuracy with a pre-trained DNN model for auto-labeling. -
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Deep learning frameworks such as TensorFlow, PyTorch, Caffe, Torch, Theano, and MXNet have contributed to the popularity of deep learning by reducing the effort and skills needed to design, train, and use deep learning models. Fabric for Deep Learning (FfDL, pronounced “fiddle”) provides a consistent way to run these deep-learning frameworks as a service on Kubernetes. The FfDL platform uses a microservices architecture to reduce coupling between components, keep each component simple and as stateless as possible, isolate component failures, and allow each component to be developed, tested, deployed, scaled, and upgraded independently. Leveraging the power of Kubernetes, FfDL provides a scalable, resilient, and fault-tolerant deep-learning framework. The platform uses a distribution and orchestration layer that facilitates learning from a large amount of data in a reasonable amount of time across compute nodes.
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Oxford Quantum Circuits (OQC)
Oxford Quantum Circuits
OQC’s quantum computer is a complete functional unit, including the control system, the hardware and the software. It is the only quantum computer commercially available in the UK. OQC’s Quantum Computing-as-a-Service (QCaaS) platform takes our proprietary quantum technology to the wider market through a private cloud. Register your interest to access our QCaaS. Thanks to a close cooperation with world-leading technical and strategic partners, we ensure that our technology is at the heart of the quantum revolution. -
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Intel Quantum Simulator
Intel Quantum Simulator
It is based on a complete representation of the qubit state but avoids the explicit representation of gates and other quantum operations in terms of matrices. Intel-QS uses the MPI (message-passing-interface) protocol to handle communication between the distributed resources used to store and manipulate quantum states. Intel-QS builds as a shared library which, once linked to the application program, allows to take advantage of the high-performance implementation of circuit simulations. The library can be built on a variety of different systems, from laptops to HPC server systems. -
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NVIDIA GPU-Optimized AMI
Amazon
The NVIDIA GPU-Optimized AMI is a virtual machine image for accelerating your GPU accelerated Machine Learning, Deep Learning, Data Science and HPC workloads. Using this AMI, you can spin up a GPU-accelerated EC2 VM instance in minutes with a pre-installed Ubuntu OS, GPU driver, Docker and NVIDIA container toolkit. This AMI provides easy access to NVIDIA's NGC Catalog, a hub for GPU-optimized software, for pulling & running performance-tuned, tested, and NVIDIA certified docker containers. The NGC catalog provides free access to containerized AI, Data Science, and HPC applications, pre-trained models, AI SDKs and other resources to enable data scientists, developers, and researchers to focus on building and deploying solutions. This GPU-optimized AMI is free with an option to purchase enterprise support offered through NVIDIA AI Enterprise. For how to get support for this AMI, scroll down to 'Support Information'Starting Price: $3.06 per hour -
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Build your deep learning project quickly on Google Cloud: Quickly prototype with a portable and consistent environment for developing, testing, and deploying your AI applications with Deep Learning Containers. These Docker images use popular frameworks and are performance optimized, compatibility tested, and ready to deploy. Deep Learning Containers provide a consistent environment across Google Cloud services, making it easy to scale in the cloud or shift from on-premises. You have the flexibility to deploy on Google Kubernetes Engine (GKE), AI Platform, Cloud Run, Compute Engine, Kubernetes, and Docker Swarm.
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DeepPy
DeepPy
DeepPy is a MIT licensed deep learning framework. DeepPy tries to add a touch of zen to deep learning as it. DeepPy relies on CUDArray for most of its calculations. Therefore, you must first install CUDArray. Note that you can choose to install CUDArray without the CUDA back-end which simplifies the installation process. -
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Superstaq
Infleqtion
Superstaq’s low-level, device-specific optimizations enable users to draw the best performance out of today's hardware across multiple qubit modalities. Qiskit and Cirq open source frontends allow users to submit to leading quantum hardware platforms from IBM, Infleqtion, OQC, Rigetti, and more. Leverage our pre-built library of quantum applications to benchmark the performance on otherwise "impossible" problems with quantum hardware. Superstaq's library of sophisticated compilation and noise mitigation techniques, such as dynamical decoupling, automatically optimizes quantum programs based on the target hardware's native gate set. Whether it's Cirq or Qiskit, Superstaq enables you to write quantum programs that target virtually any quantum computer. -
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SHARK
SHARK
SHARK is a fast, modular, feature-rich open-source C++ machine learning library. It provides methods for linear and nonlinear optimization, kernel-based learning algorithms, neural networks, and various other machine learning techniques. It serves as a powerful toolbox for real-world applications as well as research. Shark depends on Boost and CMake. It is compatible with Windows, Solaris, MacOS X, and Linux. Shark is licensed under the permissive GNU Lesser General Public License. Shark provides an excellent trade-off between flexibility and ease-of-use on the one hand, and computational efficiency on the other. Shark offers numerous algorithms from various machine learning and computational intelligence domains in a way that they can be easily combined and extended. Shark comes with a lot of powerful algorithms that are to our best knowledge not implemented in any other library. -
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Caffe
BAIR
Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR) and by community contributors. Yangqing Jia created the project during his PhD at UC Berkeley. Caffe is released under the BSD 2-Clause license. Check out our web image classification demo! Expressive architecture encourages application and innovation. Models and optimization are defined by configuration without hard-coding. Switch between CPU and GPU by setting a single flag to train on a GPU machine then deploy to commodity clusters or mobile devices. Extensible code fosters active development. In Caffe’s first year, it has been forked by over 1,000 developers and had many significant changes contributed back. Thanks to these contributors the framework tracks the state-of-the-art in both code and models. Speed makes Caffe perfect for research experiments and industry deployment. Caffe can process over 60M images per day with a single NVIDIA K40 GPU. -
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Keras
Keras
Keras is an API designed for human beings, not machines. Keras follows best practices for reducing cognitive load: it offers consistent & simple APIs, it minimizes the number of user actions required for common use cases, and it provides clear & actionable error messages. It also has extensive documentation and developer guides. Keras is the most used deep learning framework among top-5 winning teams on Kaggle. Because Keras makes it easier to run new experiments, it empowers you to try more ideas than your competition, faster. And this is how you win. Built on top of TensorFlow 2.0, Keras is an industry-strength framework that can scale to large clusters of GPUs or an entire TPU pod. It's not only possible; it's easy. Take advantage of the full deployment capabilities of the TensorFlow platform. You can export Keras models to JavaScript to run directly in the browser, to TF Lite to run on iOS, Android, and embedded devices. It's also easy to serve Keras models as via a web API. -
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Accelerate your deep learning workload. Speed your time to value with AI model training and inference. With advancements in compute, algorithm and data access, enterprises are adopting deep learning more widely to extract and scale insight through speech recognition, natural language processing and image classification. Deep learning can interpret text, images, audio and video at scale, generating patterns for recommendation engines, sentiment analysis, financial risk modeling and anomaly detection. High computational power has been required to process neural networks due to the number of layers and the volumes of data to train the networks. Furthermore, businesses are struggling to show results from deep learning experiments implemented in silos.
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IBM Quantum
IBM
Use our suite of applications to support your quantum research and development needs. Copy your API token, track jobs, and view quantum compute resources. Explore service and API documentation to start working with IBM Quantum resources. -
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ThirdAI
ThirdAI
ThirdAI (pronunciation: /THərd ī/ Third eye) is a cutting-edge Artificial intelligence startup carving scalable and sustainable AI. ThirdAI accelerator builds hash-based processing algorithms for training and inference with neural networks. The technology is a result of 10 years of innovation in finding efficient (beyond tensor) mathematics for deep learning. Our algorithmic innovation has demonstrated how we can make Commodity x86 CPUs 15x or faster than most potent NVIDIA GPUs for training large neural networks. The demonstration has shaken the common knowledge prevailing in the AI community that specialized processors like GPUs are significantly superior to CPUs for training neural networks. Our innovation would not only benefit current AI training by shifting to lower-cost CPUs, but it should also allow the “unlocking” of AI training workloads on GPUs that were not previously feasible. -
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Deeplearning4j
Deeplearning4j
DL4J takes advantage of the latest distributed computing frameworks including Apache Spark and Hadoop to accelerate training. On multi-GPUs, it is equal to Caffe in performance. The libraries are completely open-source, Apache 2.0, and maintained by the developer community and Konduit team. Deeplearning4j is written in Java and is compatible with any JVM language, such as Scala, Clojure, or Kotlin. The underlying computations are written in C, C++, and Cuda. Keras will serve as the Python API. Eclipse Deeplearning4j is the first commercial-grade, open-source, distributed deep-learning library written for Java and Scala. Integrated with Hadoop and Apache Spark, DL4J brings AI to business environments for use on distributed GPUs and CPUs. There are a lot of parameters to adjust when you're training a deep-learning network. We've done our best to explain them, so that Deeplearning4j can serve as a DIY tool for Java, Scala, Clojure, and Kotlin programmers. -
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DataMelt
jWork.ORG
DataMelt (or "DMelt") is an environment for numeric computation, data analysis, data mining, computational statistics, and data visualization. DataMelt can be used to plot functions and data in 2D and 3D, perform statistical tests, data mining, numeric computations, function minimization, linear algebra, solving systems of linear and differential equations. Linear, non-linear and symbolic regression are also available. Neural networks and various data-manipulation methods are integrated using Java API. Elements of symbolic computations using Octave/Matlab scripting are supported. DataMelt is a computational environment for Java platform. It can be used with different programming languages on different operating systems. Unlike other statistical programs, it is not limited to a single programming language. This software combines the world's most-popular enterprise language, Java, with the most popular scripting language used in data science, such as Jython (Python), Groovy, JRuby.Starting Price: $0 -
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Quantum Origin
Quantinuum
Experience the world’s only quantum-computing-hardened encryption keys, ensuring provably superior protection and allowing you to seamlessly strengthen your existing cybersecurity systems for enhanced security today, and into the future. Every organization owns sensitive data that must be kept secret at all costs. Quantum Origin adds unmatched cryptographic strength to existing cybersecurity systems, giving your enterprise a long-term edge against cyber criminals. Maintaining the trust of customers, shareholders, and regulators means adapting and strengthening your cybersecurity foundations. Adopting Quantum Origin showcases your commitment to staying ahead of potential threats. Quantum Origin verifiably strengthens the cryptographic protection around your technology and services, proving you take the privacy and security of your customer's data as seriously as they do. Let your customers know their data is safe with the ultimate in cryptographic protection. -
48
Cryptoyote
Cryptoyote
AI-powered Auto Trading platform for cryptocurrency traders. We use an artificial intelligence algorithm to predict price trends on popular crypto markets. Based on this algorithm we analyze alternative data and use machine learning to generate trading signals. Deep learning helps to exploit the information contained in financial news, social media such as twitter, google, telegram, various traders, news, blogs and transactions. Cryptoyote is not only a platform to assist traders with technical analysis, it also provides fundamental analysis, arbitrage scanning, sentiment analysis and opens a wide range of possibilities that permits Artificial Intelligence to develop financial models. Cryptoyote provides services of AI analyses, portfolio & volume tracker, statistics, news, crypto exchange and etc. for your cryptocurrency trading management. Statistic results of Cryptoyote Artificial Intelligence Bot made in 75 days, with 2122 trades. -
49
Quandela
Quandela
Quandela Cloud offers a wide range of functionalities. First, intensive documentation is available to walk you through Perceval, our photonic quantum computing framework. Perceval's programming language is Python, thus coding on Quandela’s QPUs is seamless. Moreover, you can leverage a wide range of unique algorithms already implemented (resolving PDEs, clustering data, generating certified random numbers, solving logistical problems, computing properties of molecules, and much more). Then, the status and specifications of Quandela’s QPUs are displayed. You can choose the appropriate one, run your job and check its evolution on the job monitoring interface. -
50
MXNet
The Apache Software Foundation
A hybrid front-end seamlessly transitions between Gluon eager imperative mode and symbolic mode to provide both flexibility and speed. Scalable distributed training and performance optimization in research and production is enabled by the dual parameter server and Horovod support. Deep integration into Python and support for Scala, Julia, Clojure, Java, C++, R and Perl. A thriving ecosystem of tools and libraries extends MXNet and enables use-cases in computer vision, NLP, time series and more. Apache MXNet is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision-making process have stabilized in a manner consistent with other successful ASF projects. Join the MXNet scientific community to contribute, learn, and get answers to your questions.
