Quantum Computing – The Unexplored Miracle

What is Quantum Computing?
Quantum computing is the use of quantum-mechanical phenomena such as superposition and entanglement to perform computation. A quantum computer is specifically used to perform such calculation, which can be implemented theoretically or physically. The field of quantum computing is a sub-field of quantum information science, which includes quantum cryptography and quantum communication. The idea of Quantum Computing took shape in the early 1980s when Richard Feynman and Yuri Manin expressed the idea that a quantum computer had the potential to simulate things that a classical computer could not.

The year 1994 saw further development of Quantum Computing when Peter Shor published an algorithm that was able to efficiently solve problems that were being used in asymmetric cryptography that were considered very hard for a classical computer. There are currently two main approaches to physically implementing a quantum computer: analog and digital. Analogue methods are further divided into the quantum simulation, quantum annealing, and adiabatic quantum-computation.

Basic Fundamentals of Quantum Computing
Digital quantum computers use quantum logic gates to do computation. Both approaches use quantum bits or qubits. These qubits are fundamental to Quantum Computing and are somewhat analogous to bits in a classical computer. Like a regular bit, Qubit resides in either 0 or 1 state. The specialty is that they can also be in the superposition of 1 and 0 states. However, when qubits are measured, the result is always either a 0 or a 1; the probabilities of the two outcomes depends on the quantum state they were in.

Principle of Operation of Quantum Computing
A quantum computer with a given number of quantum bits is fundamentally very different from a classical computer composed of the same number of bits. For example, representing the state of an n-qubit system on a traditional computer requires the storage of 2n complex coefficients, while to characterize the state of a classical n-bit system it is sufficient to provide the values of the n bits, that is, only n numbers.

A classical computer has a memory made up of bits, where each bit is represented by either a one or a zero. A quantum computer, on the other hand, maintains a sequence of qubits, which can represent a one, a zero, or any quantum superposition of those two qubit states; a pair of qubits can be in any quantum superposition of 4 states, and three qubits in any superposition of 8 states. In general, a quantum computer with n qubits can be in any superposition of up to different states. Quantum algorithms are often probabilistic, as they provide the correct solution only with a certain known probability.

What is the Potential that Quantum Computing offers?
Quantum Computing is such a unique field that very few people show their interest in it. There is a lot of room for development. It has a lot of scope. Some of the areas in which this is penetrating today are:

  • Cryptography – A quantum computer could efficiently solve this problem using multiple algorithms. This ability would allow a quantum computer to break many of the cryptographic systems in use today
  • Quantum Search – Quantum computers offer polynomial speedup for some problems. The most well-known example of this is quantum database search, which can be solved by Grover’s algorithm using quadratically fewer queries to the database than that is required by classical algorithms.
  • Quantum Simulation – Since chemistry and nanotechnology rely on understanding quantum systems, and such systems are impossible to simulate efficiently classically, many believe quantum simulation will be one of the most important applications of quantum computing.
  • Quantum Annealing and Adiabatic Optimization
  • Solving Linear Equations – The Quantum algorithm for linear systems of equations or “HHL Algorithm,” named after its discoverers Harrow, Hassidim, and Lloyd, is expected to provide speedup over classical counterparts.
  • Quantum Supremacy

In conclusion, Quantum computers could spur the development of breakthroughs in science, medication to save lives, machine learning methods to diagnose illnesses sooner, materials to make more efficient devices and structures, financial strategies to live well in retirement, and algorithms to direct resources such as ambulances quickly.  The scope of Quantum Computing is beyond imagination. Further developments in this field will have a significant impact on the world.

What is Reinforcement Learning? An Easy Overview

What is Reinforcement Learning?
Reinforcement Learning (commonly abbreviated as RL) is an area and application of Machine Learning. Reinforcement, as described from its meaning, is about taking suitable actions to maximize reward in a particular situation. It is implemented after rigorous testing by various machines and complex software to find the best possible behavior or path that it should take in a specific condition.

Now, the question is how does reinforcement learning work? The primary specifics in the environment of reinforcement learning are summarized as follows:

  • Input: The input is defined to be an initial state from which the model will start.
  • Output: There are many possible outputs as there are a variety of solutions for a particular task.
  • Training: The training is wholly based upon the input provided, in return, the model returns a state, and then it is the user’s decision to decide whether to reward or punish the model based on its output.
  • The model keeps learning.
  • The maximum award determines the best solution.

How is it different from Supervised Learning?
Is reinforcement learning supervised or unsupervised learning? Supervised Learning is implemented based on a training set that acts as an answer key, so the model is trained according to the correct answer itself. In Reinforcement Learning, there is no answer, but the work is done by the Reinforcement Agent who decides what to do to perform the given task. In the absence of a dataset, it is bound to learn from experience. In Reinforcement Learning, each right step gives a reward while each wrong step subtracts the award.

 

REINFORCEMENT LEARNING

SUPERVISED LEARNING
Reinforcement learning is about making decisions sequentially. In more simpler words, we can say that the output depends on the state of the current input, and the next input depends on the output of the previous information. In Supervised learning, the decision is made on the initial data or the feedback given.
Reinforcement learning is decision dependent. So, labels are given to sequences of dependent decisions. Supervised learning the choices are independent of each other, so labels are assigned to each decision.
Example: Chess game Example: Object recognition

 

Applications and Use Cases of Reinforcement Learning
In the era of Convolutional Neural Network (CNN), Reinforcement Learning as a framework seems to be undervalued (Read Neural Networks Made Easy – Tech Crunch). Reinforcement Learning in Machine Learning is unique and has its own importance. The uses and examples of Reinforcement Learning are as follows:

  • Resource Management in Computer Clusters:

Reinforcement Learning can be used to automatically learn to allocate and schedule the computer resources for waiting jobs, with the primary objective to minimize the average job slowdown.

  • Traffic Light Control:

Researchers found a way to solve the traffic congestion problem using Reinforcement Learning. Though tested only on a simulated environment, a significant improvement is seen over conventional traffic methods.

Example: The below figure depicts five agents. These were put in a five-transaction traffic network, with a Reinforcement Learning agent at the central intersection to control traffic signaling. The state was defined as an eight-dimensional vector with each element representing the relative traffic flow of each lane. Eight choices were available to the agent, each representing a phase combination, and the reward function was defined as a reduction in delay compared with the previous time step.

  • Robotics:

Use of Reinforcement Learning techniques in AI and robotics has increased tremendously. It can be used to help the robot to learn policies to map raw video images to the robot’s action.

  • Web System Configuration:

The Reinforcement Learning helps in achieving the targeted response time and measured response time.

  • Chemistry:

Reinforcement Learning can also be applied in optimizing chemical reactions. Combined with LSTM to model the policy function, the Reinforcement Learning agent can optimize the chemical reaction with the Markov decision process (MDP) characterized by {S, A, P, R}, where S was the set of experimental conditions (like temperature, pH, etc.), A was the set all possible actions that can change the experimental conditions, P was the transition probability from current experiment condition to the next term, and R was the reward which is a function of the state.

As a conclusion, Reinforcement Learning is highly helpful in industry and daily life, though it is criticized for its industrial use. Although it has its weaknesses, Reinforcement Learning is useful in the space of corporate research given its vast potential in decision making.

In the future, Reinforcement Learning is assumed to be assisting human and evolve into Artificial General Intelligence (AGI). Imagine about a robot assisting you in your work. Amazing. Isn’t it?

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