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What is InQuanto?

Quantum computing enables the rapid and economical development of new molecules and materials, revolutionizing the way we approach these challenges. InQuanto, a state-of-the-art platform for quantum computational chemistry, represents a significant leap forward in this endeavor. The discipline of quantum chemistry aims to accurately define and predict the fundamental characteristics of different materials, proving essential for the creation and design of innovative substances. However, the complexity of molecules and materials relevant to industry introduces hurdles that complicate precise simulation efforts. Existing technologies often force a trade-off, requiring users to select between employing highly accurate techniques on small systems or accepting less precise approximations. With its flexible workflow, InQuanto allows both computational chemists and quantum algorithm developers to effectively combine advanced quantum algorithms with intricate subroutines and error correction methods, thereby optimizing results on current quantum hardware. This adaptability not only improves research findings but also cultivates collaboration among specialists in the field, thereby propelling further advancements in quantum chemistry and material science. The ongoing exchange of ideas and methodologies within this community is vital for overcoming the existing limitations and unlocking the full potential of quantum computing in material innovation.

What is Atinary SDLabs Platform?

Atinary's Self-Driving Labs (SDLabs) platform provides a no-code approach for artificial intelligence and machine learning, designed to revolutionize research and development workflows by enabling traditional laboratories to transition from manual experiments to fully autonomous experimentation. This innovative platform bolsters the design and improvement of experiments through a robust closed-loop system that integrates AI-generated hypotheses, predictions, and decision-making processes. Among its key functionalities are multi-objective optimization, effective database management, seamless workflow orchestration, and immediate data analysis. Users can define experimental parameters with specific limitations, allowing machine learning algorithms to guide the subsequent phases of the process, carry out experiments either manually or with robotic assistance, evaluate results, and refresh models with the most recent data, thereby accelerating the journey toward more efficient, cost-effective, and environmentally sustainable products. Furthermore, Atinary provides exclusive algorithms such as Emmental for addressing non-linear constrained optimization challenges, SeMOpt for facilitating transfer learning within Bayesian optimization, and Falcon, all of which significantly boost the platform's capabilities and performance. By utilizing these sophisticated tools, researchers are empowered to enhance their experimental workflows, fostering greater efficiency and driving innovation in their fields. Ultimately, the SDLabs platform represents a transformative shift in how laboratories approach experimentation, paving the way for groundbreaking discoveries.

Media

Media

Integrations Supported

Azure Marketplace

Integrations Supported

API Availability

API Availability

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided

Supported Platforms

Windows
Mac
Linux

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Standard Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training

Training Options

Documentation Hub
On-Site Training

Company Facts

Organization Name

Quantinuum

Company Location

United States

Company Website

www.quantinuum.com/computationalchemistry/inquanto

Company Facts

Organization Name

Atinary

Date Founded

2019

Company Location

Switzerland

Company Website

atinary.com/solutions/

Categories and Features

Chemical

Not specified

Materials Science

Not specified

Quantum Computing

Not specified

Categories and Features

Materials Science

Not specified

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