Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
Bayesforge™ is a specialized Linux machine image designed to assemble top-tier open source applications tailored for data scientists in need of sophisticated analytical tools, as well as for professionals in quantum computing and computational mathematics who wish to engage with key quantum computing frameworks. This image integrates well-known machine learning libraries like PyTorch and TensorFlow alongside open source tools from D-Wave, Rigetti, and platforms like IBM Quantum Experience and Google’s innovative quantum language Cirq, in addition to other leading quantum computing frameworks. For example, it features our quantum fog modeling framework and the versatile quantum compiler Qubiter, which supports cross-compilation across all significant architectures. Users can conveniently access all software through the Jupyter WebUI, which features a modular design that enables coding in Python, R, and Octave, enhancing flexibility in project development. Moreover, this comprehensive environment empowers researchers and developers to seamlessly blend classical and quantum computing techniques in their workflows.
Description
The development of large-scale physical quantum computers is proving to be a formidable task, and in parallel with efforts to create these machines, considerable attention is being directed towards crafting effective quantum algorithms. Without a fully realized large quantum computer, it becomes essential to utilize precise software simulations on classical systems to replicate the execution of these quantum algorithms, allowing researchers to analyze quantum computer behavior and refine their designs. In addition to simulating ideal, error-free quantum circuits on a faultless quantum computer, the QX simulator offers the capability to model realistic noisy executions by incorporating various error models, such as depolarizing noise. Users have the option to activate specific error models and set a physical error probability tailored to mimic a particular target quantum computer. This defined error rate can be based on factors like gate fidelity and qubit decoherence characteristics of the intended platform, ultimately aiding in the realistic assessment of quantum computation capabilities. Thus, these simulations not only inform the design of future quantum computers but also enhance our understanding of the complexities involved in quantum processing.
API Access
Has API
No
API Access
Has API
No
Integrations
AWS Marketplace
Yes
D-Wave
Yes
Google Cirq
Yes
IBM Quantum
Yes
Octave
Yes
PyTorch
Yes
Python
Yes
R
Yes
Rigetti Quantum Cloud Services (QCS)
Yes
TensorFlow
Yes
Integrations
AWS Marketplace
No
D-Wave
No
Google Cirq
No
IBM Quantum
No
Octave
No
PyTorch
No
Python
No
R
No
Rigetti Quantum Cloud Services (QCS)
No
TensorFlow
No
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Deployment
Web-Based
No
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
Yes
Chromebook
No
Deployment
Web-Based
No
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
Yes
Mac
Yes
Linux
Yes
Chromebook
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
No
Webinars
No
Live Training (Online)
No
In Person
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
Quantum Programming Studio
Website
artiste-qb.net/bayesforge/
Vendor Details
Company Name
Quantum Computing Simulation
Website
quantum-studio.net