In the modern version of computing and AI-driven technologies, Quantum Computing might sound like a bit of a fantasy. So far into the future, the Quantum Computer platforms have managed to nudge the compass only by a minute. Today, professionals with Python Certification are hired to reinvent the data processing models with quantum computing and Edge processing.
In 2020, researchers are placing their money on Quantum Computing emerging from the theoretical boundaries to AI ML realms.
What is Quantum Computing?
The years between 2020 and 2035 would be the age of Quantum Technology, or Qu Tech.
We are slowly moving away from the binaries of zero and one, on and off and start and end. Today, Quantum systems built by Intel and Microsoft are leveraging the genius of Python engineers to re-imagine the computing models with ‘qubits’ for unprecedented levels of parallelism. Much like poetry at play, Quantum Computing is the central component of the new age of technology, called QuTech.
Top Research Areas that Quantum Engineers Need to Master
Python courses would get a major push from the Quantum technology solely by virtue of the kind of distinct research areas we are inventing these days. The fastest growing research areas in this technology space are related to –
- Superconducting qubit Processors
- Qubit metrics and error correction
- Quantum simulation, assisted optimization and Automation
- Quantum Neural Networks
Why You Should Chase QuTech with Python Certification?
Python engineers with experience in open source programming and AI ML modeling understand the challenges in data collection and access to data. In QuTech, these engineers can test every Qubit platform to determine how every data point is collected, analyzed and processed for scientific results.
In 2018, Intel announced the launch of their qubit technology in the form of a 49-bit superconducting processor or test chip. This is called Tangle Lake. Since its launch in 2018, Tangle Lake has made QuTech relatively simpler to understand for applications in diverse industries – healthcare, drug development, cryogenics, refrigeration, fintech and predictive intelligence.
How QuTech Impacts Other Data Science Platforms?
Well, honestly, it’s too early to really put forth an opinion on how QuTech is staged or propelled to impact data science as a technology. Like AI ML, it is possible that QuTech advancements with Computer Vision, Neural Networking, Machine Learning and Robotics can enable computers to learn and simulate human behavioral norms at 1000 times the pace that they are doing now.
At Google’s AI Lab, we can expect engineers to be working with Quantum Neural Networking algorithms that break the energy barriers and deliver on coherent data transfer and storage.
By 2025, most major Cloud Computing companies would switch to Quantum Machine Learning or near-term QuTech devices for highly efficient neural circuits. While we keep one eye on how QuTech simulation and Optimization harmonize with traditional AI ML algorithms, we would expect quantum supremacy to grow further with Google’s Bristlecone and other advanced enterprise quantum processors.
In the next two years, it would be QuTech replacing Cloud at most places.