Computação Quântica - Fundamentos e Aplicações - Aula 06

Summary

The video discusses the current state of quantum computing, specifically the Noisy Intermediate-Scale Quantum (NISQ) era, characterized by challenges like noise and limited qubit counts (100-400). Although full-scale quantum computers aren't yet feasible, algorithms like Variational Quantum Eigensolver (VQE) and Quantum Approximate Optimization Algorithm (QAOA) are still functional within this stage. Adiabatic quantum computing, another model, also provides solutions for optimization problems. The programming of quantum computers mainly uses Python with libraries like Qiskit and Cirq, alongside specialized languages like OpenQASM. Despite challenges, quantum computing continues to evolve and holds potential for future applications.

Takeaways

• 😀 The current phase of quantum computing is known as Noisy Intermediate-Scale Quantum (NISQ), characterized by quantum computers with 100 to 400 qubits.
• 😀 NISQ-era quantum computers still face challenges due to environmental interference and noise, making it difficult to achieve precise operations.
• 😀 For effective quantum computing, more qubits (tens of thousands) are needed to run advanced algorithms and deliver practical solutions.
• 😀 Quantum algorithms like the Variational Quantum Eigensolver (VQE) are already being applied to molecular modeling and optimization problems.
• 😀 The Quantum Approximate Optimization Algorithm (QAOA) is another notable algorithm used for optimization, even with current hardware limitations.
• 😀 Adiabatic quantum computing focuses on finding the lowest energy state of a system, but it is not a fully programmable model and is limited to specific problem types.
• 😀 In quantum computing programming, Python is the most commonly used language, with popular libraries such as Qiskit, Cirq, and Q#.
• 😀 Markup languages like OpenCASMA are also used in quantum programming to define quantum circuit commands and operations.
• 😀 The NISQ era represents a transitional phase in quantum computing, where algorithms can still deliver some results despite hardware limitations.
• 😀 The practical application of quantum computing today includes solving real-world optimization problems and simulating molecular interactions, paving the way for future advancements.

Q & A

What is the current era of quantum computing called?

The current era of quantum computing is called the Noise Intermediate-Scale Quantum (NISQ) era. It refers to quantum computers that have between 100 to 400 qubits and are affected by environmental noise, making them less precise than ideal quantum systems.

Why is the NISQ era important in the development of quantum computing?

The NISQ era is important because it represents the current stage of quantum computing development, where quantum computers are available but are still limited in their capabilities due to the small number of qubits and the high level of noise. This era is essential for testing and refining quantum algorithms despite these challenges.

What are the main challenges faced by quantum computers in the NISQ era?

The main challenges in the NISQ era include the limited number of qubits (100-400) and the interference from environmental noise. These factors hinder the scalability and precision required for quantum algorithms to achieve optimal performance.

What is the goal for quantum computers in the future beyond the NISQ era?

The goal for quantum computers beyond the NISQ era is to achieve a much larger number of qubits (tens of thousands to hundreds of thousands) and reduce the noise level significantly. This would enable the development of more advanced quantum algorithms capable of solving complex problems efficiently.

What are some examples of quantum algorithms that work in the NISQ era?

Examples of quantum algorithms suitable for the NISQ era include the Variational Quantum Eigensolver (VQE) and the Quantum Approximate Optimization Algorithm (QAOA). These algorithms can deliver results despite the limitations in qubit numbers and noise.

How does the Variational Quantum Eigensolver (VQE) algorithm work?

The VQE algorithm is used for modeling molecules by finding the lowest energy state of a system. It uses quantum computing to solve problems related to atomic interactions, which are modeled on the quantum scale, and is especially useful for quantum chemistry simulations.

What is the Quantum Approximate Optimization Algorithm (QAOA), and what is its use?

The QAOA is a general-purpose optimization algorithm that works well in the NISQ era. It is used for solving optimization problems, including finding the best solution for complex systems where classical methods might be inefficient.

What is adiabatic quantum computing, and how does it differ from other quantum computing models?

Adiabatic quantum computing focuses on finding an optimal energy state by slowly evolving the energy of a system. Unlike other models, it does not require a programmable quantum computer. Instead, it uses predefined structures to model and solve quadratic optimization problems.

How is quantum computing programmed today?

Quantum computing is primarily programmed using Python, with popular libraries such as Qiskit, Cirq, Q#, and PennyLane. There are also specialized languages like OpenQASM that are used to define quantum circuits and control quantum systems.

What is OpenQASM, and how does it relate to quantum computing?

OpenQASM is a specialized language for quantum computing that allows users to define commands for quantum circuits. It is an open-source framework that provides a standardized way of programming quantum systems, enabling precise control over quantum operations.