A common approach for detecting outliers using descriptive statistics is the use of interquartile ranges (IQRs). This method works by analyzing the points that fall within a range specified by quartiles, where quartiles are four equally divided parts of the data. Although IQR works well for data containing a single shape or pattern, it is not able to distinguish different types of shapes or groups of data points within a data set.
Fortunately, clustering techniques address the limitations of IQR by effectively separating samples into different shapes. A commonly used clustering method for outlier detection is DBSCAN, which is an unsupervised clustering method that addresses many of the limitations of IQR….. https://bit.ly/3SNuILx

Tensor networks can help to alleviate the complexity of representing quantum states and operations by exploiting redundancies in the topological structure of the quantum circuit – instead of the vectors/ matrices. To translate a quantum circuit into a tensor network, each object, be it a state or an operation, is represented by a multi-dimensional array of complex numbers – a tensor. For a circuit, tensors are connected to other tensors according to the underlying quantum circuit. https://bit.ly/3fXlluR

Though hype may suggest otherwise, quantum computers aren’t all-powerful computational devices capable of solving intractable problems like the halting problem. Rather, they’re processors built on an architecture that we hope will allow them to do anything a classical computer can do, with extra capabilities or increased performance for some problems afforded by their quantum nature. https://bit.ly/3gm9eHM

“But unlike classical bits, qubits are extremely fragile. The physical objects that represent classical bits are made up of semiconductors. You can drop them on a table and they would still work fine. But if you so much as bumped against a table on which there is a functional qubit, it will break. Qubits are even affected by seemingly insignificant disturbances like stray electromagnetic waves, vibrations, temperature fluctuations and possibly cosmic rays.” https://tinyurl.com/5bzkfzdx

“Symplectic geometry is more complicated. Here, the answer depends on the ellipsoid’s “eccentricity,” a number that represents how elongated it is. A long, thin shape with a high eccentricity can be easily folded into a more compact shape, like a snake coiling up. When the eccentricity is low, things are less simple.” https://bit.ly/3DNw8B8

"In this article, it have discussed three waves of quantum machine learning, each harnessing a particular aspect of quantum computers and targeting particular problems. The first scrutinizes the power of quantum computers to work with high-dimensional data and speed-up algebra, but raises the caveat of input/output due to the quantum measurement rules. The second domain circumvents this problem by using a hybrid architecture, performing optimization on a classical computer while evaluating parameterized states on a quantum circuit, chosen based on a particular problem. Finally, the third domain is inspired by brain-like computation and uses the natural interaction and unitary dynamic of a given quantum system as a source for learning." shorturl.at/ackU5

“One of the key obstacles to accurate quantum simulations is noise—random errors in both the switching of the “gates” that perform quantum logic operations and in the reading of their output states. These errors accumulate and restrict the number of gate operations a computation can enact before the noise dominates. The researchers found that simulations with more than 300 gates were overwhelmed by noise. But the more complex the system, the more gates are needed.” shorturl.at/kpLPW

"But a century ago, the pioneers of quantum mechanics made a surprising discovery — one that elevated unitarity from common sense to a hallowed principle. The surprise was that, mathematically, the quantum world operates not by probabilities but by more complicated numbers known as amplitudes. An amplitude is essentially the degree to which a particle is in a certain state; it can be a positive, negative or imaginary number. To calculate the probability of actually observing a particle in a certain state, physicists square the amplitude (or, if the amplitude is an imaginary number, they square its absolute value), which gets rid of the imaginary and negative bits and produces a positive probability. Unitarity says the sum of these probabilities (really, the squares of all the amplitudes) must equal 1." https://bit.ly/3gF70DT

“It can be easy to take math for granted. But the field has evolved over millennia, and concepts that seem obvious today had to be invented. Although zero is the beginning of counting, it arrived late to the math party. But not as late as its counterpart, infinity. Four thought experiments featured in NOVA’s “Zero to Infinity” illustrate how people used the world around them to explore these revolutionary concepts.” https://to.pbs.org/3XSqS78

Experimental Entropy Analysis Over Quantum NN´s & IRL Agent (Lab1-Exp3).

In this experiment we focus on one of the properties of information that says that as the amount of available information increases, uncertainty decreases. To increase the amount of information available in our scenario we are going to use the LZC algorithm. The reward function is built from the behavior of the market using different synthetic signals (i.e. Quantum Signal) to establish policies and be able to act "optimally".

Link Dashboard-> https://bit.ly/3UD2ynb

4 Highlights from IRL Agent (Datasets until 26/Nov/2022):
-Detect High Volatility (Last 4 Weeks)
-The relation skewness & Average Price is very low
-The probability of transition to the 26K-38K price bins is below 0.07
-Buy probability is below 0.08

*Comments: Recall this CloudLab its just only for experimental purpose, the main objective is not to give a financial advice but give some experimental perspectives.

*Report: Next experiment Nov/2023 (Lab1-Exp4)

Who was Marvin Minsky - this is an essay written by chatgpt - https://open.substack.com/pub/vzocca/p/who-was-marvin-minsky-by-chatgpt?r=1nqhf3&utm_medium=ios&utm_campaign=post

Let’s finish the year on a light note: chocolate consumption and Nobel laureates correlation - https://www.sciencedirect.com/science/article/pii/S2590291120300711

Is the AI hype just a lie? - https://www.youtube.com/watch?v=PBdZi_JtV4c&t=5s

In 2022, mathematicians solved a centuries-old geometry question, proved the best way to minimize the surface area of clusters of up to five bubbles and proved a sweeping statement about how structure emerges in random sets and graphs. https://bit.ly/3GaaVRE

From this channel we want to wish you all a Happy 2023,that the next year be full of great opportunities, challenges, creativity, experiments and adventures...

Quantum Complexity Tamed by Machine Learning

If scientists understood exactly how electrons act in molecules, they’d be able to predict the behavior of everything from experimental drugs to high-temperature superconductors. Following decades of physics-based insights, artificial intelligence systems are taking the next leap. https://bit.ly/3GAR8LC

“As quantum computing attracts more attention and funding, Aaronson says, researchers may mislead investors, government agencies, journalists, the public and, worst of all, themselves about their work’s potential. If researchers can’t keep their promises, excitement might give way to doubt, disappointment and anger, Aaronson warns. The field might lose funding and talent and lapse into a quantum-computer “winter” like those that have plagued artificial intelligence.” https://bit.ly/3kuzBNK

“Rotational invariance is a symmetry exhibited by the circle: Rotate it any number of degrees and it looks the same. In the context of physical systems on the brink of phase changes, it means many properties of the system behave the same regardless of how a model of the system is rotated.” https://bit.ly/3HkwjnX

“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

“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