Quantum computing

Quantum computing is a type of computation that harnesses the collective properties of quantum states, such as superposition, interference, and entanglement, to perform calculations. The devices that perform quantum computations are known as quantum computers.^[1]

Though current quantum computers are too small to outperform usual (classical) computers for practical applications, they are believed to be capable of solving certain computational problems, such as integer factorization (which underlies RSA encryption), substantially faster than classical computers.^[2] The study of quantum computing is a subfield of quantum information science.

Quantum computing began in the early 1980s when physicist Paul Benioff proposed a quantum mechanical model of the Turing machine. Richard Feynman and Yuri Manin later suggested that a quantum computer had the potential to simulate things that a classical computer could not feasibly do.^[3]

Overview

Quantum computers differ fundamentally from classical computers in their basic unit of information. While classical computers use bits that exist in one of two definite states (0 or 1), quantum computers use quantum bits or "qubits" that can exist in a superposition of both states simultaneously.

This fundamental difference allows quantum computers to explore multiple solution paths simultaneously through quantum parallelism. However, quantum computers are not universally faster than classical computers, and their advantage depends on the specific problem being solved.

Quantum mechanics principles

Superposition

Superposition is the ability of a quantum system to be in multiple states at the same time until it is measured. A qubit in superposition can represent both 0 and 1 simultaneously, with different probabilities for each state. This allows quantum computers to process a vast number of possibilities in parallel.

Entanglement

Quantum entanglement is a phenomenon where qubits become correlated in such a way that the state of one qubit instantaneously affects the state of another, regardless of the distance between them. This property enables quantum computers to perform certain calculations exponentially faster than classical computers.

Interference

Quantum interference allows quantum computers to amplify correct answers and cancel out wrong answers. This is achieved through careful manipulation of the quantum states to increase the probability of measuring the desired outcome.

Types of quantum computers

Gate-based quantum computers

Gate-based quantum computers use quantum gates to manipulate qubits, similar to how classical computers use logic gates. These systems follow the quantum circuit model and are the most common approach for universal quantum computing.

Quantum annealers

Quantum annealers are specialized quantum computers designed to solve optimization problems. They use quantum effects to find the global minimum of a complex energy landscape, making them suitable for specific types of problems but not for general-purpose computing.

Quantum algorithms

Several quantum algorithms have been developed that demonstrate quantum computers' potential advantages:

• Shor's algorithm - for integer factorization
• Grover's algorithm - for searching unsorted databases
• Quantum Fourier Transform - for signal processing
• Variational Quantum Eigensolver (VQE) - for quantum chemistry

Current limitations

Despite their theoretical advantages, current quantum computers face several significant challenges:

• Quantum decoherence - quantum states are fragile and easily disrupted by environmental noise
• Error rates - current quantum computers have high error rates that limit their usefulness
• Limited scalability - current systems have relatively few qubits compared to classical bits
• Specialized applications - quantum advantage is limited to specific problem domains

Applications

Quantum computing has potential applications in several fields:

• Cryptography - breaking current encryption and developing quantum-resistant security
• Drug discovery - simulating molecular interactions for pharmaceutical research
• Financial modeling - portfolio optimization and risk analysis
• Machine learning - quantum-enhanced AI algorithms
• Materials science - discovering new materials with desired properties