Justify with the help of an example why probabilistic computing is better than deterministic computing in solving some real-world problems.

Probabilistic computing is better than deterministic computing in solving some real-world problems because many real-life situations involve uncertainty, incomplete data, and changing conditions, which deterministic computing cannot handle effectively.

In deterministic computing, the output is completely defined by predefined rules and fixed algorithms. It does not allow flexibility or adaptability. For example, in inventory management, deterministic computing uses fixed rules based on demand and storage capacity. Once the rules are defined, the system cannot adjust itself to changes in customer demand or market conditions.

On the other hand, probabilistic computing uses past data, uncertainty, and probability to make decisions. It can learn from data and adapt to changing situations. For example, in inventory management, probabilistic computing uses past sales data, seasonal factors, promotions, and market trends to predict future demand more accurately. Using AI and machine learning algorithms, it can dynamically adjust inventory levels and respond to changes in customer behaviour.

Thus, probabilistic computing is better than deterministic computing for real-world problems because it can handle uncertainty, learn from data, adapt to changes, and provide flexible and realistic solutions, which deterministic computing fails to do in many real-life scenarios.