Google not too long ago launched TensorFlow Quantum, a toolset for combining state-of-the-art machine studying strategies with quantum algorithm design. This is a vital step to construct instruments for builders engaged on quantum purposes.
Simultaneously, they’ve targeted on enhancing quantum computing {hardware} efficiency by integrating a set of quantum firmware strategies and constructing a TensorFlow-based toolset working from the {hardware} stage up – from the underside of the stack.
The elementary driver for this work is tackling the noise and error in quantum computer systems. Here’s a small overview of the above and the way the influence of noise and imperfections (important challenges) is suppressed in quantum {hardware}.
https://blog.tensorflow.org/2020/10/boosting-quantum-computer-hardware.html
Noise And Error: The Chinks In Armor When It Comes To Quantum Computers
Quantum computing combines data processing and quantum physics to unravel difficult laptop issues. However, a major difficulty in quantum computer systems is susceptibility to noise and error, limiting quantum computing {hardware} effectivity. Noise refers to all kinds of issues that may trigger interference, just like the electromagnetic indicators from the WiFi or disturbances within the Earth’s magnetic discipline. Most quantum computing {hardware} can run only a few dozen calculations over a lot lower than 1 ms earlier than requiring a reset because of the noise’s affect. That is about 1024 instances worse than the {hardware} in a laptop computer.
Many groups have been working to make the {hardware} immune to the noise to beat these weaknesses. Many theorists have additionally designed a sensible algorithm referred to as Quantum Error Correction. QEA can establish and repair errors within the {hardware}, however it is extremely gradual or incapable of observe. Because the knowledge is to be unfold in a single qubit over a number of qubits, it could take a thousand or extra bodily qubits to appreciate only one error-corrected “logical qubit.”
To overcome this, Q-CTRL’s “quantum firmware” can stabilize the qubits in opposition to noise and decoherence with out the necessity for additional assets. This is finished by including the brand new options that enhance the {hardware}’s robustness to the error on the lowest layer of the quantum computing stack.
The protocols described by the Quantum firmware are there to ship the quantum {hardware} with augmented efficiency to larger ranges of the abstraction within the quantum computing stack.
In normal, quantum computing {hardware} depends on light-matter interplay, which is made to enact quantum logic operations.
Source: https://blog.tensorflow.org/2020/10/boosting-quantum-computer-hardware.html
Consultant Intern: He is Currently pursuing his Third yr of B.Tech in Mechanical discipline from Indian Institute of Technology(IIT), Goa. He is motivated by his imaginative and prescient to carry exceptional adjustments within the society by his data and expertise. Being a ML fanatic with eager curiosity in Robotics, he tries to be updated with the newest developments in Artificial Intelligence and deep studying.
✅ [FREE AI WEBINAR Alert] Live RAG Comparison Test: Pinecone vs Mongo vs Postgres vs SingleStore: May 9, 2024 10:00am – 11:00am PDT
https://www.marktechpost.com/2020/10/04/tensorflow-quantum-boosts-quantum-computer-hardware-performance/
