10 Things You Need to Know About Quantum Computing & AI

In recent years, emerging technologies have become more popular. Quantum computing is one of them. It has the potential to make a significant impact on the world. Learn more about the 10 most important things to know about quantum computing and artificial intelligence. Emerging technologies have been prominent in recent years. Quantum computing is one of the most promising technologies. It has the potential to transform our world in a unique way. Quantum computing has demonstrated promising evidence that it can speed up heuristic calculations in an amazing way. Quantum computing can be used to solve complex problems in finance, pharmaceuticals, materials discovery, artificial intelligence and other areas. Quantum computing can magnify the positive and negative effects of many AI applications.
Sundar Pichai, Google CEO
For better planning and strategy, it is important to keep in mind the upcoming technology changes as organizations strive to become digital. These technological advancements could bring companies real benefits from quantum computing. Let’s take a look at 10 things to be aware of in the worlds AI and quantum computing.
1. The main characteristics of Quantum Computing
Bits are programmed in so-called classic computers as data units. They can have a range of values, including ones and zeros. Quantum computers use quantum bits–qubits to program data units. These can represent a one or zero, or a combination thereof.
A light switch is a good analogy. In classical computers, it can have an on- or off position. Quantum computers have qubits that can hold any number of positions, including the on and off positions. Quantum computing’s two main characteristics are brought to you by the physical capabilities of qubits.
Superposition. This refers the ability of qubits be on and off simultaneously, or somewhere in the middle. The system is able to solve certain types of problems because it incorporates uncertainty and probability.
Entanglement. Entanglement is when qubits are linked together and can affect each other’s independence, even though they are physically distinct. If two qubits are linked together, and one is moved, the other is affected, even if they are not physically separated. This characteristic allows for powerful information movement at extremely high speeds.

2. Faster and better
Quantum computers possess four fundamental capabilities that distinguish them from classical computers today:
Prime factorization is a multidimensional approach to solving large problems. It could revolutionize encryption.
Optimization is achieved by solving complex/larger problems at an unprecedented speed.
Simulation is where quantum computers model complex problems efficiently.
Quantum artificial intelligence has faster and more accurate algorithms. IBM’s quantum research team found that entangling qubits on a quantum computer that ran a data classification experiment reduced the error rate by half compared with unentangled qubits.

Complex problems can be addressed by business applications. Take, for example:
The modeling of substance molecules is crucial for pharmaceutical development. This is because atoms interact with each other in complex ways. This is where quantum computers’ inherit entanglement property lends itself well.
Quantum AI can be used to speed up the accuracy and time required for training systems such that they are autonomous vehicles.

Quantum computing will have a significant impact on many industries, including financial services, medical products, energy, telecom, media travel, logistics, insurance, and healthcare.
3. Bias Amplifier
Quantum computing’s amplifying effects go beyond speed and accuracy. It also highlights the inherent bias in AI/ML models. Applications that are susceptible to algorithmic bias (e.g. in the employment screening space or policing) may be even more vulnerable. This could make applications even more vulnerable to algorithmic bias (e.g., in the employment screening space, policing, etc.) Quantum computing could have a magnifying negative side effect, which could make it too risky to use without special mitigation controls. Anyone working with AI/quantum computing should recognize this unintended effect and account for it in their solutions.
4. Increased Algorithmic Complexity and Transparency, Transparency, and Explainability
AI’s lack of transparency and explanation is a major problem, especially when complex algorithms like deep learning are used. If AI systems are being used to make decisions that directly affect people’s lives,

Author: Victoria