Quantum Computing: Unleashing the Power of a New Era

Quantum computing represents a groundbreaking frontier in information processing, harnessing the principles of quantum mechanics to revolutionize our approach to complex problem-solving. By leveraging the unique properties of quantum bits, or qubits, quantum computers possess unprecedented computational potential beyond the capabilities of classical computers. In this article, we will explore the fundamentals, advancements, and transformative applications of quantum computing, unveiling a new era in information processing.

However, before we delve into the intrinsics of quantum computing, let us quickly revisit how traditional computers interpret data. As we know, for human beings, “A” represents the letter “A”; however, for traditional computers, it's a series of 0s and 1s. So, “A” will be “01000001”. This includes everything we do with computers, be it watching a video, image, audio, or just text.

As you might have guessed, this works well, but it is definitely some kind of limiting of our computers and so when we have to deal with problems which are above calculations, finding information, then we have to look beyond the traditional computers. One example is to discover new medicine to help people. That’s where quantum computing comes into the picture.

What Is Quantum Mechanics?

Quantum Mechanics is a branch of Physics, and just like every branch of science is a study of something, quantum mechanics studies things that are very small or isolated. In other words, it’s a study of very small particles, such as atoms, and subatomic particles, like electrons. It describes how these tiny particles can exist in multiple states at the same time and how they can be connected or influenced by each other, even when they are far apart.

Superposition

One of the key ideas in quantum mechanics is called superposition. Imagine flipping a coin: in classical physics, the coin would either be heads or tails. But in quantum mechanics, the coin can be in a superposition of both heads and tails at the same time. It's like the coin is in a blurry state where it is partly heads and partly tails until we measure it and force it to be one or the other.

Entanglement

Let's say we have two particles that are entangled. If we change the state of one particle, the other particle instantly changes its state, no matter how far apart they are. It's as if they are connected in a mysterious way, even though we don't fully understand the mechanism behind it. Let’s go back to our example of a coin. We have two coins that are entangled by some means. If we spin both of them and then when they stop, we can infer the outcome of one coin based on what we have on another one.  So, if coin 1 has heads, then coin 2 will have heads, too, and vice-versa. Isn’t this crazy?

The way entanglement works is using interference, where two or more particles are communicating using waves. 

These properties of superposition and entanglement allow quantum computers to perform calculations in a fundamentally different way than classical computers. Instead of using bits that can only be 0 or 1, quantum computers use qubits, which can exist in a superposition of both 0 and 1 simultaneously. This enables quantum computers to perform many calculations at once, potentially solving complex problems much faster than classical computers.

In essence, quantum computing surpasses traditional binary computing by offering more than just the options of 0s and 1s. This expanded range of possibilities is where the true power of quantum computing lies. To illustrate this, consider two individuals with different mathematical abilities. One person only knows addition and subtraction, while the other person is knowledgeable in addition, subtraction, multiplication, and division. If both are given a problem to count the total number of balls in five buckets, with each bucket containing six balls, the first person would compute it as 6+6+6+6+6, resulting in 30. However, the second person, representing the quantum computer, would compute it as 6 multiplied by 5, also resulting in 30. This example showcases how the broader range of operations available to the quantum computer allows for more efficient computations and highlights the advantage it holds over traditional computing methods.

Principles of Quantum Computing

Qubits: Qubits stands for Quantum Bits. In classical computing, a bit can represent either a 0 or a 1. However, qubits can exist in a superposition, meaning they can be in a combination of 0 and 1 at the same time. This superposition enables qubits to hold and process much more information than classical bits. In quantum computing, state 0 is represented as |0> and state 1 as |1>.

Quantum Gates: Quantum gates are fundamental building blocks used in quantum computing to manipulate qubits. Similar to classical logic gates in traditional computing, quantum gates perform specific operations on qubits to enable computation and information processing. The basic gates includes:

1. *Pauli-X Gate: This is similar to NOT gate which converts 0 to 1 and vice-versa. So, |0> with Pauli-X will result in |1>.
2. Pauli-Y Gate: it combines both a bit flip (changing 0 to 1 and vice versa) and a phase flip (introducing a phase change)
3. Pauli-Z Gate: It introduces a phase flip, leaving the basis states unchanged but adding a negative phase to the state |1>.
4. SWAP Gate: It swaps the states of two qubits, exchanging their quantum information.

*It is named after physicist Wolfgang Pauli, who made significant contributions to quantum mechanics.

Quantum Circuits: A quantum circuit is a graphical representation of a sequence of quantum gates applied to qubits in quantum computing. It serves as a visual depiction of the quantum operations and transformations performed during a quantum computation. 

Here are the quantum circuits notations for respective quantum gates:

1. Pauli-X: 
2. Pauli-Y:
3. Pauli-Z:
4. SWAP:

Quantum Algorithms:

A quantum algorithm is a set of computational steps or procedures designed to solve specific problems using a quantum computer. Unlike classical algorithms that operate on classical bits, quantum algorithms take advantage of the unique properties of qubits and quantum gates to perform computations in a fundamentally different manner.

Quantum Computers:

At present, there are few approaches on how to manufacture quantum computers. 

1. Superconducting: 

A highly favored variant of quantum computers is the superconducting qubit quantum computer, which commonly employs superconducting materials. These quantum computing systems utilize miniature electrical circuits to generate and manipulate qubits. By employing superconducting qubits, gate operations can be executed with notable speed.

2. Photonic:

Photon-based quantum computers utilize photons, which are particles of light, to transport and process quantum information.

3. Neutral Atoms:

Neutral atom quantum computing relies on the suspension of atoms in an ultrahigh vacuum environment using arrays of tightly focused laser beams known as optical tweezers. It is worth noting that while optical tweezers are commonly used for this purpose, not all neutral atom companies employ them. One significant advantage of neutral atom quantum computers is their reduced susceptibility to stray electric fields, making them an attractive choice for quantum processors. 

4. Trapped Ions:

The trapped ion quantum computer employs atoms or molecules with a net electrical charge, commonly referred to as "ions," which are captured and manipulated using electric and magnetic fields to store and process quantum information. The isolation of trapped ions from their surroundings allows for precise measurements and facilitates applications that demand exceptional levels of stability and control. Additionally, the qubits in trapped ion systems can retain their superposition state for extended periods before experiencing decoherence.

• Quantum Dots:

In a quantum dot quantum computer, silicon qubits composed of pairs of quantum dots are utilized. The concept involves employing these "coupled" quantum dots as durable quantum bits, known as qubits, in the context of quantum computing.

Quantum Development Framework

There are several quantum development frameworks available that provide tools, libraries, and resources for programming and simulating quantum algorithms. Some of the popular quantum development frameworks include:

• Qiskit: Developed by IBM, Qiskit is an open-source framework that allows users to design and run quantum circuits, simulate quantum algorithms, and interface with quantum hardware.
• Microsoft Quantum Development Kit: This framework by Microsoft provides a comprehensive set of tools, including the Q# programming language, for developing quantum applications, simulating quantum algorithms, and interfacing with quantum simulators and hardware. 
• Forest: Forest is a quantum development platform developed by Rigetti Computing. It includes the Forest SDK and pyQuil library, which enable users to design and execute quantum circuits, access quantum simulators, and connect to Rigetti's quantum processors. 
• Cirq: Cirq is an open-source framework developed by Google that focuses on providing tools for creating and running quantum circuits. It offers high-level abstractions for constructing quantum circuits and supports simulation and hardware access through the Quantum Engine.
• Xanadu PennyLane: PennyLane is an open-source framework developed by Xanadu that focuses on quantum machine learning and hybrid quantum-classical computing. It provides a unified interface for working with various quantum computing platforms and integrates with popular machine learning libraries.

These frameworks provide different features, programming languages, and support for various quantum hardware or simulators. Developers can choose the framework that best suits their requirements and preferences.

Setup Qiskit

We are going to write and run our first quantum program on IBM’s Quiskit framework over the cloud. You can do the same on your local machine regardless of its operating system; however, in the interest of time, we are leveraging the cloud setup. 

The purpose of this program would be to demonstrate the usage of the Pauli-X Gate.

Let’s get started by visiting Qiskit and clicking the “Get started” button:

From the Getting Started page, click on the “Start on the cloud” tab and under that “IBM Quantum Lab” link:

You can log in using Google, GitHub, or LinkedIn account or create a new IBMid account:

Once sign-in is complete, the cloud development framework dashboard will be presented. From here, click on the “Launch Lab” button to go to the cloud IDE. From the IDE, select the first option, “Python 3,” under the Notebook section:

As our setup is complete, now let’s get started on real coding.

As we are coding in traditional computers, we need a quantum device to run our quantum code. The Qiskit environment provides you access to some of the available devices, and you can set the device in your code to specify where the code should be executed. So, the first step is to get a list of your available devices. In the new file, write the below code to get the list:

from qiskit import IBMQ

# Load your IBM Quantum account
provider = IBMQ.load_account()

# Get the list of available backends
backends = provider.backends()

# Print the names of the available backends
for backend in backends:
    print(backend.name())

This code is available at GetBackends.ipynb

Click on the Run button, and it will generate a list of available devices. Choose anyone from the list and make a note of it. Let’s say it’s ibm_perth, for example.

Next is to create a new file and write code as below:

from qiskit import QuantumCircuit, execute, IBMQ

# Load your IBM Quantum account
provider = IBMQ.load_account()

# Create a quantum circuit with one qubit
circuit = QuantumCircuit(1, 1)

# Apply the Pauli-X gate to the qubit
circuit.x(0)

# Measure the qubit
circuit.measure(0, 0)

# Choose an IBM Quantum device to run the circuit on
backend = provider.get_backend('ibmq_qasm_simulator')

# Execute the circuit on the chosen backend
job = execute(circuit, backend, shots=1024)

# Get the result of the execution
result = job.result()

# Get the counts of the different measurement outcomes
counts = result.get_counts(circuit)

# Print the measurement outcomes
print(counts)

This code is available at Pauli-X.ipynb

If we run the code, then we will get the following output:

Explanation of the Code Line-By-Line

Note: The line beginning with “#” indicates comments.

1. Importing necessary modules:

from qiskit import QuantumCircuit, execute, IBMQ #

This line imports the required modules from Qiskit. QuantumCircuit is used to create a quantum circuit, execute is used to run the circuit, and IBMQ is used to interact with the IBM Quantum cloud.

2. Loading your IBM Quantum account:

provider = IBMQ.load_account()

This line loads your IBM Quantum account using the credentials you've set up. It allows you to access and use the IBM Quantum cloud services.

3. Creating a quantum circuit:

circuit = QuantumCircuit(1, 1)

This line creates a quantum circuit with one quantum bit (qubit) and one classical bit (for measurement outcomes). We specify the number of qubits and classical bits in the parentheses.

4. Applying the Pauli-X gate:

circuit.x(0)

This line applies the Pauli-X gate to the qubit at index 0. The Pauli-X gate is a single-qubit gate that flips the state of the qubit from 0 to 1 or vice versa. As in our case, we have just initialized the qubit, so the default value is 0, and it should get converted to 1 after applying the gate.

5. Measuring the qubit:

circuit.measure(0, 0)

This line adds a measurement operation to the circuit. It measures the qubit at index 0 and stores the measurement result in the classical bit at index 0.

6. Choosing an IBM Quantum backend:

backend = provider.get_backend('ibm_perth')

This line selects the backend (quantum device or simulator) you want to execute the circuit on. 

7. Executing the circuit on the chosen backend:

job = execute(circuit, backend, shots=1024)

This line executes the circuit on the chosen backend. It runs the circuit shots a number of times, which is set to 1024 in this example. The execute function returns a job object representing the execution of the circuit.

8. Getting the result of the execution:

result = job.result()

This line retrieves the result of the execution by calling the result() method on the job object.

9. Getting the counts of measurement outcomes:

counts = result.get_counts(circuit)

This line extracts the counts of the different measurement outcomes from the result. It returns a dictionary where the keys are the measurement outcomes, and the values are the corresponding counts.

10. Printing the measurement outcomes:

print(counts)

This line prints the counts of the measurement outcomes, which represent the probabilities of obtaining each state (either 0 or 1) after applying the Pauli-X gate to the qubit. We ran our shots 1024 times, so it will display the value and the number of times it was executed, i.e., 1, 1024.

Conclusion

Although quantum computing is still in its early stage, in the past couple of years, we have seen tremendous growth and industry investment. New advancements, breakthroughs, and discoveries are being made regularly, which can shape the future trajectory and capabilities of quantum computing.