Tech startup InfinityQ Technologies promises quantum computing with all its advantages, but without the boredom of a sub-zero refrigerator filled with unstable materials.
The secret is to take the analog qubit and find a way to make qubits in hardware that is much less exotic than the usual quantum hardware.
According to InfinityQ, the solution lies in analog, especially CMOS silicon chips and the way the circuits are connected.
Using a paradigm of energy flow, the use of analog chips can mimic what is measured in quantum systems, which is different from running qubits through a gate that copies classical switching electrons.
Simulated annealing is one of the first approaches to modern deep learning.
You take an optimization problem that consists of many elements of a system, a problem with many variables, such as optimal travel routes between cities or the layout of chip circuits.
It loosens the constraints on what is called “heating up” the system and keeps the possible configurations in check. This stems from the practice of smelting metals in the real world, first melting them and then slowly cooling them down.
This approach can lead to super positional solutions without the need for masses of qubits to perform the fault connection and with much less power than quantum hardware.
This paradigm has become more relevant because of the growing importance of energy models in AI. Yann LeCun of Facebook, an AI luminary, recently praised the merits of these approaches from the 1980s.
In addition, InfinityQ will build “the full stack,” a complete quantum computer similar to the special AI machines built by other AI startups.
Infinity Q’s existing analog chip is manufactured locally in Canada, with the plan to use conventional CMOS transistors shrinking to achieve even greater circuit density – much faster than the mainstream quantum qubits can multiply.
If the tech giant achieves ever-greater miniaturization of circuits, it will provide an on-premise version of its computer.
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