Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
Cirq is a Python library designed for creating, modifying, and optimizing quantum circuits, which can be executed on both quantum computers and simulators. It offers valuable abstractions tailored for the current generation of noisy intermediate-scale quantum computers, where understanding the hardware specifics is crucial for achieving optimal outcomes. The library includes integrated simulators that can manage both wave function and density matrix representations, capable of simulating noisy quantum channels through Monte Carlo methods or complete density matrix techniques. Additionally, Cirq is compatible with an advanced wavefunction simulator known as qsim, allowing users to replicate quantum hardware experiences through a quantum virtual machine. By utilizing Cirq, researchers can conduct experiments on Google's quantum processors, providing a platform for innovative exploration in quantum computing. For those interested in delving deeper, resources are available to learn about recent experiments and access the code needed to replicate these experiments independently.
Description
Discover innovative and effective turn-key algorithms designed specifically for data scientists, alongside robust circuit components tailored for quantum engineers. These turn-key implementations cater to the needs of data scientists, financial analysts, and various engineers alike. Delve into challenges related to binary optimization, machine learning, linear algebra, and Monte Carlo sampling, whether on simulators or actual quantum hardware. No background in quantum computing is necessary to get started. Utilize NISQ data loader circuits to transform classical data into quantum states, thereby enhancing your algorithmic capabilities. Leverage our circuit components for linear algebra tasks, such as distance estimation and matrix multiplication. You can also customize your own algorithms using these building blocks. Experience a notable enhancement in performance when working with D-Wave hardware, along with the latest advancements in gate-based methodologies. Additionally, experiment with quantum data loaders and algorithms that promise significant speed improvements in areas like clustering, classification, and regression analysis. This is an exciting opportunity for anyone looking to bridge classical and quantum computing.
API Access
Has API
No
API Access
Has API
Yes
Integrations
Amazon Web Services (AWS)
No
Bayesforge
Yes
NVIDIA DRIVE
No
Python
Yes
Superstaq
Yes
Integrations
Amazon Web Services (AWS)
Yes
Bayesforge
No
NVIDIA DRIVE
Yes
Python
No
Superstaq
No
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Pricing Details
$2,500 per hour
Free Trial
No
Free Version
Yes
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
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
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
No
In Person
Yes
Vendor Details
Company Name
Founded
1998
Country
United States
Website
quantumai.google/cirq
Vendor Details
Company Name
QC Ware
Founded
2014
Country
United States
Website
forge.qcware.com
Product Features
Product Features
Machine Learning
Deep Learning
No
ML Algorithm Library
No
Model Training
No
Natural Language Processing (NLP)
No
Predictive Modeling
No
Statistical / Mathematical Tools
No
Templates
No
Visualization
No