Ratings and Reviews 0 Ratings
Ratings and Reviews 0 Ratings
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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 Classiq?
Classiq serves as a cutting-edge platform for quantum computing software, facilitating the design, refinement, evaluation, and execution of quantum algorithms. It adeptly transforms high-level functional models into optimized quantum circuits, allowing users to quickly construct circuits with a variety of qubit counts, including 100, 1,000, or even 10,000, which can seamlessly operate on any gate-based architecture or cloud service. The platform offers a holistic environment for developing quantum applications, nurturing in-house knowledge and enabling the creation of reusable quantum intellectual property. By automating the complex process of converting high-level functional models into optimized quantum circuits, Classiq's Quantum Algorithm Design platform simplifies the design and coding process at a more abstract level. This empowers users to focus on the conceptual elements of their algorithms, as the system takes care of the technical execution, delivering circuits that meet both functional requirements and system constraints. This pioneering methodology not only boosts productivity but also encourages more innovative approaches in quantum algorithm development, leading to breakthroughs that could redefine the field. As a result, Classiq plays a crucial role in advancing quantum computing capabilities for various applications.
Integrations Supported
Azure Marketplace
API Availability
Has API
API Availability
Has API
Pricing Information
Pricing not provided.
Free Trial Offered?
Free Version
Pricing Information
Pricing not provided.
Free Trial Offered?
Free Version
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Supported Platforms
SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Customer Service / Support
Standard Support
24 Hour Support
Web-Based Support
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Training Options
Documentation Hub
Webinars
Online Training
On-Site Training
Company Facts
Organization Name
Quantinuum
Company Location
United States
Company Website
www.quantinuum.com/computationalchemistry/inquanto
Company Facts
Organization Name
Classiq
Date Founded
2020
Company Location
Global
Company Website
www.classiq.io
Categories and Features
Chemical
Certificates of Analysis
Chemical Process Simulation
Computer-Assisted Structure Elucidation (CASE)
ISO Management
Inventory Management
Particle Tracking
Reporting & Statistics
Samples Tracking
Traceability
Uncertainty Analysis
