Machine Learning
& Neural Network

Quantum Machine Learning is about how quantum computers and other quantum processors can learn patterns in data, that cannot be learnt by classical machine learning algorithms, to solve real world problems.

“Beating the financial markets consistently requires you to have an edge over your competitors. Our edge is our quantum computing algorithms with machine learning and neural network capabilities.”

Dr David Moche

CEO of Qfinity Labs

The Possibilities of Quantum Computers

Qfinity Labs is accelerating the application of quantum computing to solve the most challenging problems in finance, optimization, and machine learning.

Machine Learning

Using quantum computers to train and develop machine learning algorithms could help solve complex problems more quickly. Machine learning uses advanced algorithms that parse data, learn from it, and use this to discover meaningful patterns of interest.

Neurual Network

Quantum computing processing power could cut AI model training exponentially, and dramatically increase the speed of informed decision making. This is extremely useful when writing neural network algorithms for trading.

Optimization

Quantum computers may potentially find the best solution among varying weighted options, more efficiently than classical computers, and could provide advantages in areas such as portfolio optimization, risk analysis, and Monte-Carlo-like applications.

Finance

Many computationally intensive problems exist, such as optimization of financial portfolios or the risk analysis of such portfolios. For some of these problems, quantum computing may have the potential to achieve a significant advantage compared to classical computing.

APPLICATIONS

Artificial Intelligence

At Qfinity Labs, we focus on quantum computing research, developing artificial intelligence for analyzing financial markets and acting upon the analysis, generating profitable and adaptive trading portfolios/strategies.

Quantum Computing in Finance

Imagine being able to make calculations that reveal dynamic arbitrage possibilities that competitors are unable to see. Beyond that, greater compliance, employing behavioral data to enhance customer engagement, and faster reaction to market volatility are some of the specific benefits we expect quantum computing to deliver.

Applications

6 Uses for Quantum Computers in Finance


High Frequency
Trading (HFT)


Market Analysis


Portfolio
Optimization


Neural
Network AI


Security And
Risk Assessment


Development Of Complex
Quantum Algorithms

What does our “QA" trading algorithm analyze?

Thanks to the availability of high end quantum technology, our “QA” series of trading algorithms can analyze extremely large sizes of data from the 4 categories below, to make accurate trading decisions.


Financial News


Earnings Reports


Cyclical Events


Price Patterns

The Analogy

Using the analogy of teaching a robot how to walk:

1st generation rules-based technology would program the robot to lift one leg followed by another to move forward.

2nd generation algorithms would show the robot hundreds and thousands of videos that demonstrate how to walk and allow it to learn.

3rd generation algorithms will emphasize improvements upon 2nd generation, which throws the robot into different environments and forces it to learn how to walk under these environments with might include different wind speed, different floor textures and with obstacles being thrown at it from all directions.

Next Generation Algorithms

Automated trading algorithms have evolved in a multitude of ways over the past 20 years. Let’s look at how algorithms have evolved over the past decade.

1st

 Generation

1st-generation algorithms usually consisted of only buy or sell orders with simple parameters. It can execute simple buy or sell orders with predetermined rules usually used in combination with simple indicators. It cannot analyze the current situation to use the best rules as the rules are hard coded and not dynamic.

2nd

 Generation

2nd-generation algorithms deployed strategies to break up large orders as to reduce potential direct market impact. For example, selling 500 million worth of euros versus the dollars in a short period of time could cause the price to sharply decline and potentially scare away buyers. However, slicing the large order into smaller orders reduces immediate market impact and help the seller’s order complete at a higher VWAP price which could lead to potentially higher profit margins.

3rd

 Generation

3rd generation trading algorithms are based on quantum computers, which utilizes artificial neural networks with machine learning. 3rd gen algorithms have proven to be highly successful at specific tasks like pattern recognition, image classification, and decision making. This allows the algorithms in real time to think and make the best decision in the constantly changing financial markets.

Monte Carlo Simulation

Monte Carlo methods are a broad class of computational algorithms that rely on repeated random sampling to obtain numerical results. Their essential idea is using randomness to solve problems that might be deterministic in principle. They are most useful when it is difficult or impossible to use other approaches. In principle, Monte Carlo methods can be used to solve any problem having a probabilistic interpretation.

Monte Carlo methods can be used for modeling attack simulations, cyber risks, and cyber security investments. Instead of using point estimates, ranges of loss events and their costs are defined as inputs for the Monte Carlo simulation to identify tens of thousands of possible outcomes. All the outcomes then are put on a graph to show where total loss exposure is likely to fall.

Machine Learning (Trading Algo) Artificial Neural Networks

Quantum Machine Learning is about how quantum computers and other quantum information processors can learn data patterns that cannot be learnt by classical machine learning algorithms to solve real world problems.

Artificial Neural Networks

Artificial neural networks are the heart of machine learning algorithms and artificial intelligence. Historically, the simplest implementation of an artificial neuron traces back to the classical Rosenblatt’s “perceptron”, but its long term practical applications may be hindered by the fast scaling up of computational complexity, especially relevant for the training of multilayered perceptron networks. Here we introduce a quantum information-based algorithm implementing the quantum computer version of a binary-valued perceptron, which shows exponential advantage in storage resources over alternative realizations. That is why our quantum algorithms are able to beat the markets consistently.

“We created our QuantumAlgo series of algorithms which leverages simulated data from various types of market conditions to select the best placement and execution strategies to maximize returns. It then uses reinforcement learning, a subset of machine learning, to assess the performance and utilize neural networks to consistently learn and refine its performance.”

Dr David Moche

CEO of Qfinity Labs

Did you know?

“AlphaGo, is the most used algorithm developed at the moment , to beat the best competitive human player in the strategy game “Go”. The algorithm used machine learning combined with neural network capabilities, to learn and predict the player’s every move in order to beat him. Qfinity Lab’s “QuantumAlgo” series of trading algorithms uses exactly the same technology to consistently beat the market. It is expected that the performance can be further improved with the launch of QuantumAlgo 2.0 in 2022”