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This article mainly introduces "how to use qiskit package for quantum computer programming in Python". In daily operation, I believe many people have doubts about how to use qiskit package in Python for quantum computer programming. The editor consulted all kinds of materials and sorted out simple and easy-to-use operation methods. I hope it will be helpful for you to answer the doubt of "how to use qiskit package for quantum computer programming in Python". Next, please follow the editor to study!

What is a quantum computer?

First, let's talk about quantum computing and what you can expect from this technology. You will find that the computer chips inside a mobile phone, laptop or tablet are all made of silicon. One of the most common techniques is to use a special light-based lithography technology to etch microscopic patterns into silicon chips. These tiny patterns enable us to transmit electrons (currents) through silicon chips in a controlled manner. At present, we have reached the physical limit that these tiny patterns can reach. Now we can effectively create a large number of them with nanometer precision, and a typical architecture is about 10 nanometers, about 20 to 50 atoms.

There are technologies that can manipulate individual atoms more precisely, and we can now create architectures that are smaller than the architectures you usually find in computers or phones. But the manufacturing process is slower, so it is more difficult to mass-produce these much smaller, atomic-scale chips. The difficulty we encounter in constructing a computer chip with such a small feature is the quantum behavior of electrons and atoms. Electrons are wave-particle biphasic, which means that, in some cases, electrons behave like waves rather than particles, so if the etched pattern is too small, it will become more difficult to limit it to tiny etching patterns on silicon chips. Electron "diffusion" and electron leakage (quantum tunneling effect) occur, which causes part of the current to flow through the tiny channels in the silicon chip. This leakage means that the chip cannot achieve the expected performance, and the behavior of electrons as waves will be a problem. This is often referred to as the "death of Moore's Law", and it represents the stagnation of computer performance growth that we have expected over the past few decades. If you bet on the assumption that the market is growing, then this will be a big problem, because there are physical restrictions on the size and efficiency of computer chips.

Quantum computing aims to use this "problem" of quantum mechanics as a computational advantage, by using it to process information in a different way than the usual binary ones and zeros in our mobile phones and laptops. One useful way for quantum computers is as ASIC (application specific integrated circuit), which is not exact, but it is not a bad analogy. It is regarded as a special computer chip to perform specific types of calculations more efficiently than standard silicon chips. Let's take a look at the representation of the basic unit of calculation in QISKit.

Quantum States in QISKit

First, you need to download the free Anaconda so that you can use Jupyter Lab. After downloading Anaconda, open an instance of Jupyter Lab in the Anaconda navigation. To install QISKit, you only need to use pip in Jupyter notebook or Jupyter Lab.

Next, you need to import the qiskit and matplotlib libraries.

Now, if you want to draw the "Spin-Up" state on Bloch Sphere, you can type:

We can use the following command to plot the "Spin-Down" state of qubits.

In general, the qubit state is no longer limited to a simple 0 or 1 (classical binary bit), qubits can be in an infinite number of states. Each state can be represented as a point on the Bloch sphere. Spin-Up corresponds to state 0 and Spin-Down corresponds to state 1, but qubits can also be in a combination of these two states. Each point on the Bloch ball, like the coordinates on the earth's surface, represents a unique qubit state. This property of infinitely many different states of quantum ratio is related to the concept of "superposition state" in quantum mechanics. For example, the following state on the Bloch ball is a uniform mixture of state 0 and state 1, so it is in a superposition of the two states.

This "Spin-Right" position is represented as "superposition"

Or a "linear combination" of states 0 and 1. It doesn't matter if you don't understand linear algebraic expressions. Now, if we want to operate on a single qubit in a Spin-Up state (or state 0) and flip it to Spin-Down (or state 1), we can use a non-gate. Non-gates or "Pauli X gates" are quantum logic gates. Quantum logic gate is similar to classical logic gate in concept, but not exactly the same. Their operations on qubits are very similar to those that can be performed by classical logic gates, but they are always reversible and represented by unit matrix multiplication. We can create a quantum circuit in QISKit, as follows:

Now, if you want to use a non-gate to operate on a single quantum, you can use the following code in QISKit.

Then we can define a device to run the circuit.

Finally, we can output the state vector obtained by running the circuit.

This shows that we have flipped the qubit state to Spin-Down, which is the basic idea of quantum algorithms. In the following articles, we will study how to implement these quantum logic gates on the actual hardware. It can also be done with the software of IBM, and the microwave pulse of the quantum computer chip can be programmed.

At this point, the study on "how to use qiskit packages for quantum computer programming in Python" is over. I hope to be able to solve your doubts. The collocation of theory and practice can better help you learn, go and try it! If you want to continue to learn more related knowledge, please continue to follow the website, the editor will continue to work hard to bring you more practical articles!

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