The AI & Quantum Computing Chronicle
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This channel covers Artificial Intelligence, Data Science, Machine Learning & Quantum Computing to help you extract valuable information through our posts.

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“In 2020, two researchers at the Massachusetts Institute of Technology led a team that introduced a new kind of neural network based on real-life intelligence — but not our own. Instead, they took inspiration from the tiny roundworm, Caenorhabditis elegans, to produce what they called liquid neural networks. After a breakthrough last year, the novel networks may now be versatile enough to supplant their traditional counterparts for certain applications.”

“Liquid networks also differ in how they treat synapses, the connections between artificial neurons. The strength of those connections in a standard neural network can be expressed by a single number, its weight. In liquid networks, the exchange of signals between neurons is a probabilistic process governed by a “nonlinear” function, meaning that responses to inputs are not always proportional. A doubling of the input, for instance, could lead to a much bigger or smaller shift in the output. This built-in variability is why the networks are called “liquid.” The way a neuron reacts can vary depending on the input it receives.” https://bit.ly/3DRwWEC
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“Imagine you had some useful knowledge — maybe a secret recipe, or the key to a cipher. Could you prove to a friend that you had that knowledge, without revealing anything about it? Computer scientists proved over 30 years ago that you could, if you used what’s called a zero-knowledge proof” https://bit.ly/3IyMSyi
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“The quantum energy teleportation protocol was proposed in 2008 and largely ignored. Now two independent experiments have shown that it works.”
“Now in the past year, researchers have teleported energy across microscopic distances in two separate quantum devices, vindicating Hotta’s theory. The research leaves little room for doubt that energy teleportation is a genuine quantum phenomenon.” https://bit.ly/3Zirini
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Do we have a conducive environment for Quantum Machine Learning?
There are several software packages and platforms that provide environments for quantum machine learning, including:

Qiskit: An open-source quantum computing framework for writing, running, and debugging quantum programmes. It includes tools for working with quantum circuits and algorithms as well as a variety of quantum simulators and quantum hardware backends.

IBM Quantum Experience: A cloud-based platform for accessing IBM’s quantum computers and experimenting with quantum algorithms. It includes tools for writing and running quantum programmes, as well as a variety of quantum simulators and quantum hardware backends.
https://bit.ly/3KVIJGl
Physicists have coaxed particles of light into undergoing opposite transformations simultaneously, like a human turning into a werewolf as the werewolf turns into a human. In carefully engineered circuits, the photons act as if time were flowing in a quantum combination of forward and backward…. https://bit.ly/3Fg6J3l
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“Boston Dynamics’ Atlas robot over the years progressed from unstable approaches to a set of stairs to the equivalent of human parcours and dancing by leveraging ever more sophisticated machine learning. The device autonomously vacuuming your carpets demonstrated progression over time, where they now continuously map their environments then estimate their location within that environment in order to move. Robot vacuums rely on simultaneous localisation and mapping (SLAM) algorithms, which innovatively have been crossed with the quantum qubit. A University of Sydneyteam created an “adaptive algorithm that measures the performance of one qubit and uses that information to estimate the capabilities of nearby qubits…called Noise Mapping for Quantum Architectures. Instead of considering the singular environment of one qubit, the team automated and sped up the process by reducing the number of measurements and qubits required making quantum computing more effective from traditional robot-inspired techniques.” https://bit.ly/40dVfpD
“One of the most formidable obstacles to building functional quantum computers is that qubits don’t stick around very long. Vibration, temperature, and other environmental factors can cause them to lose their quantum-mechanical properties, resulting in errors. Today, the rate at which errors occur in qubits limits the duration of algorithms that can be run. Scientists are working to build environments in which many physical qubits act together to create error-protected logical qubits, which can survive for much longer periods of time—long enough to support commercially viable applications. It will most likely take some 1,000 physical qubits to make a single logical qubit; the most advanced quantum computers today have only 50 to 100 physical qubits.” https://bit.ly/40Oq8R6
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“A qubit, or quantum bit, is a unit of quantum information that is physically constructed of circuits made of superconductors and cooled to very low temperatures to optimize the circuits’ efficiency. Yale researchers in the Devoret research group have successfully extended the lifetime of a qubit beyond the break-even point, seeing a gain in the preservation of information and the amount of operations that can be performed on a qubit in one lifetime.” https://bit.ly/41uQQP9
“The basic idea behind counterfactuals is to ask what would have happened in a situation had certain things been different. It’s like rewinding the world, changing a few crucial details, and then hitting play to see what happens. By tweaking the right things, it’s possible to separate true causation from correlation and coincidence.” https://bit.ly/3L223Af
Data should not be approached as a monolithic asset, rather as a multi-faceted asset that comprises several dimensions. It is important for companies to move beyond the concept of big data volume as the only dimension to be interested in. In fact, big data has at least four important flavours, sometimes referred to as the four V's of data: volume, variety, veracity and velocity. Focusing on volume only can produce a negative linear impact on firm performance - https://vzocca.substack.com/p/how-to-handle-big-data
“In the research paper, published on February 8, 2023, in the journal Nature Communications, the scientists demonstrate how they have used a new and powerful technique, which they dub ‘UQ Connect’, to use electric field links to enable qubits to move from one quantum computing microchip module to another with unprecedented speed and precision. This allows chips to slot together like a jigsaw puzzle to make a more powerful quantum computer.” https://bit.ly/40UWkC2
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One of the promising technologies for quantum computing makes use of superconducting circuits. Anton Potočnik, senior researcher in quantum computing at IMEC, says, "The energy states of superconducting qubits are relatively easy to control, and, throughout the years, researchers have been able to couple an increasing number of qubits together. This enables an ever-higher level of entanglement—which is one of the pillars of quantum computing. On top of that, research groups worldwide have demonstrated superconducting qubits with long coherence times (up to several 100 µs) and sufficiently high gate fidelities—two important benchmarks for quantum computation." https://bit.ly/3LHahy3
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The IBM Quantum Computer is built to take quantum computing out of the lab and into a commercial environment. Kids and adults alike can use this LEGO set to discover and learn about the composition of a Quantum Computer system while recreating a slice of a real-life Quantum computer data center used by quantum computing users in industry, academia, research and national labs. https://bit.ly/3pySodp
“The next generation of quantum devices requires high-coherence qubits that are less error-prone. Responding to this need, researchers at the AQT at Berkeley Lab, a state-of-the-art collaborative research laboratory, developed a blueprint for a novel quantum processor based on "fluxonium" qubits. Fluxonium qubits can outperform the most widely used superconducting qubits, offering a promising path toward fault-tolerant universal quantum computing.” https://bit.ly/3WpEeaH
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You can find online material including slides, recordings, and a virtual machine for François Fleuret's deep-learning course.
This course is a thorough introduction to deep-learning, with examples in the PyTorch framework:
• machine learning objectives and main challenges,
• tensor operations,
• automatic differentiation, gradient descent,
• deep-learning specific techniques,
• generative, recurrent, attention models.

See material at https://fleuret.org/dlc/
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“Scientists have long been interested in understanding how materials self-organize into complex structures, such as crystals. In the often-arcane world of quantum physics, this sort of self-organization of particles is seen in "density waves," where particles arrange themselves into a regular, repeating pattern or order; like a group of people with different colored shirts on standing in a line but in a pattern where no two people with the same color shirt stand next to each other.” https://rb.gy/epnim