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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 

Screenshots View All

Screenshots View All

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

Product Features

Product Features

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