Entangled Qubits: Quantum Computing and Quantum Information
Quantum Computing and Quantum Information: Erwin Schrodinger:- “The best possible knowledge of a whole does not necessarily include the best knowledge of all its parts, even though they may entirely separated and therefore virtually capable of being ‘best possibly known”.
John Preskill:- “The whole is definite, the part is random”
Quantum Computing & Quantum Information
Why Entanglement is important?
Each satellite will generate a constant. Stream of entangled pairs.
Each member of the pair will sent to separate stations on the ground. Where it will be stored in quantum memories.
Once the entanglement stored on the ground, it can then used as needed to send secure messages, or even sent locally across the quantum Internet using short optical fibers.
Quantum Logic: Quantum Computing and Quantum Information
Cost of Information Loss: Quantum Computing and Quantum Information
Fundamental logic dictates that energy must dissipated when information erased
Energy dissppated = kT . Ln2 per bit erased
One way of suppressing this unwanted heat is by modifying the chip design to use only reversible logic gates.
In reversible gates there always a unique input associated with a unique output and vise versa.
So reversible logic never erase any information when they act
Quantum Neural Networks(QNN)
Reversibility & Dissipation
Hopfield network. An example of ANN.
Hopfield network used. As an associative memory.
In associative memory multiple patterns. Mapped to single pattern.
Associative memory irreversible circuit
This raises an important question about how 100 billion. Neurons processes information with energy dissipation.
Non Linear Activation
Non-Linear activation functions an important. Characteristic of neural networks.
Sigmoid function the most famous example.
One open issue of Quantum Neural Networks is how to incorporate non-linear functions in quantum systems which is linear
Qubit Neurons (Qurons)
A quron is a qubit in which the two levels stand for active an resting neural firing states.
This allows for neural network to be in a superposition of firing patterns.
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