# The Dawn of Quantum Computing: Implications for Software Development and QA

Quantum computing is often championed as the next big thing in tech, nothing short of a revolution that will, well, revolutionize everything. But despite the hype, there is still a ton about QC that the average user doesn’t understand and then some that they misunderstand.

Given the recent attention and breakthroughs this new paradigm of computing has been enjoying, it’s fitting that we take the time to understand and appreciate what QC is and how it works. In this post, we’ll try to figure out how quantum computing works as well as its implications for software development.

Understanding what makes quantum computers so great comes down to knowing the difference between quantum computers and classical computers. But before we get anywhere near the quantum stuff, it might be worth asking: how do computers compute anyway?

## How do things Compute?

The basic principles of computing have remained unchanged since the inception of the computer. Everything we do on our screen, every interaction, input, response, or calculation, gets translated into what is called ‘machine language’ or ‘machine code.’ In other words, 0s and 1s. This is necessary because computers do not understand English, and all computation is essentially just the manipulation of binary bits. So the first step in computing is where compilers and interpreters transform human language into machine language.

On the hardware level, computers store and operate on bits using transistors. Connections of transistors form gates that perform various operations on the data available. This is a gross oversimplification, but it’ll do to highlight the difference between classical computers and their quantum counterparts.

## Classical vs Quantum Computers

While classical computers may store bits using transistors, a quantum computer uses qubits. A qubit is characterized by having two essential qualities:

- Like a classical bit, a qubit must have two primary states, each of which can come to resemble a ‘0’ state and a ‘1’ state.
- Unlike a classical bit, a qubit can be in superposition. This means that until a qubit is measured, its state is given by a linear combination of the |0> state and |1> state.

Fancy terminology aside, what does this really mean? This means that much like particles in nature that exhibit quantum behavior, a qubit is neither in the 0 state nor the 1 state but rather just in a fuzzy state where it has a certain probability of being in either of the states.

The fun part is that a qubit can really be anything that can quantum mechanically move between two states. Photons can act as qubits based on their plane of polarization; atoms can act as qubits-being able to move between different energy states, particles that change their spin when fed with photons, and even ‘macroscopic’ superconducting circuits with tiny currents in them. The point is that there are many ways of making a qubit, and while it isn’t easy to build, sustain and scale a large number of qubits, scientists have been at it for a few decades and have made solid progress.

## Why Use Qubits?

All this technical talk is good, but what’s the inherent advantage of using qubits in the first place?

Well, qubits have some quantum properties that can be exploited for computing, which gives them a decisive advantage over classical computers. This makes quantum computers good at certain types of calculations, especially those that involve multiple possibilities or parallel computing.

A classical system made of two bits can either be in one of four possibilities: (0,0), (0,1), (1,0), (1,1). A quantum system can be in a superposition of all of these states at once.

This does not mean that the system can ‘store’ all these values. An incredibly popular misconception is that n qubits can store 2n bits. When in superposition, n qubits can calculate for 2n possibilities, but once you make a measurement, the probabilities collapse, and you get a single output state.

The majority of quantum computing involves designing software that can effectively make use of the unique behaviors of quantum physics. This is done to increase the chances of qubits – the fundamental units of quantum information – settling into a state that provides the correct solution to a problem.

We’ll delve deeper into these concepts shortly. For the time being, it’s enough to understand that qubits and quantum phenomena, like superposition, are unconventional effects that don’t align with traditional, familiar scientific explanations.

*Yea, so why use qubits?*

**Because they can leverage quantum mechanical effects like superposition and interference to calculate across a vast number of possibilities in a way that classical bits can’t. **

To put it in numbers, a quantum algorithm like Glover’s algorithm can significantly cut short calculations like finding a specific item in a long list of items. If classical algorithms take X amount of time to find the desired answer, quantum computers take the square root of X to find the same.

Here are a few examples:

- Large Database Search. Quantum computers can exploit quantum superposition to search through large databases exponentially faster than classical computers.
- Cryptography. Another significant advantage of quantum computing is in the field of cryptography. Shor’s algorithm, a quantum algorithm, can factor large numbers much more efficiently than the best known classical algorithms, potentially rendering current encryption techniques obsolete.
- Quantum Simulation. Quantum computers excel at simulating quantum systems, which is a task classical computers struggle with.
- Optimization Problems. Problems that involve finding the optimal solution among many possibilities, such as routing problems in logistics or configuration problems in machine learning, can also benefit from quantum computing.

But If quantum computers are so much smarter and faster than regular computers, why aren’t they everywhere?

That’s because quantum computing has some serious limitations, both on the hardware and software levels. Let’s discuss these a bit.

## Challenges of Building Quantum Computers

We can discuss the challenges of building quantum computers on both the hardware and software levels.

### Hardware Challenges in Building Quantum Computers: Why Making Qubits Isn’t Easy

Qubits are incredibly fragile. Creating and maintaining a state of superposition is difficult in and of itself, and if qubits aren’t isolated enough, even the tiniest of photons can alter their state and cause decoherence.

Some of the most powerful quantum computers today use superconductors that need to be at extremely low temperatures to be operational; we are talking about a fraction of a degree above absolute zero! Clearly, that implies strict restrictions in terms of scalability, cost, and maintenance. These systems require sophisticated hardware to run that simply isn’t practical on the retail, consumer level.

But if you think building the right hardware for quantum computers is difficult, wait till you hear the software side of the story.

### Software Challenges of Quantum Computing: Why simulating Qubits Isn’t Easy Either

Because Quantum Computers are hard to build, most programmers won’t have access to them anytime soon. Although, a quantum computer can be simulated on a good enough classical computer to run at least some basic programs.

What’s challenging about that? Classical programs don’t translate well into quantum programs. A lot of practical systems today are, in reality, hybrid systems, utilizing both quantum and classical systems to address different parts of the same problem. On top of that, quantum programs use entirely different syntactic structures, logic, and hardware, making it difficult for developers to choose which parts of the problem demand a quantum solution and which don’t.

Quantum computers don’t have the hardware to go mainstream yet, nor do they support easy-to-use, plug-and-play software solutions. But hopefully, one day, they will.

And if so, it might be worth asking what this implies for software development in the future.

## Implications of Quantum Computing for Software Development

If you could only have a single takeaway from this post, be it this: Quantum Computers aren’t just faster, more efficient classical computers. They are entirely different types of computers with their unique strengths and weaknesses. This makes them very good at solving certain types of problems, while also making them terrible at other types of problems.

Let’s take a moment to understand what quantum computers are good at to better understand the type of software they could support:

### Factorizing and Encryption

The most popular and controversial use case for quantum computers is in the realm of encryption. Estimates suggest that a quantum computer made out of a few thousand qubits can pose a threat to most of our encryption techniques. Although we aren’t nearly close to that many qubits in a single operational computer yet, we’ll certainly get there one day. And once we do, all of our encryption will have to be redesigned.

That’s because public key cryptography is based on algorithms that allow for decrypting keys using factorization of prime numbers. While this is still possible on classical computers, it simply isn’t practical. However, running Shor’s algorithm on a quantum computer could make it possible for a future quantum computer to crack even the highest level of current encryption in a matter of hours.

New quantum-resistant encryption techniques are under development as quantum computers scale up to a level that threatens existing systems. Therefore, one major application of quantum computing in the world of software will be in decrypting classical encryption as well as developing quantum encryption.

### Scanning Large Databases

Another promising application of quantum computing comes in the form of database search and scanning. Using the previously mentioned Glover’s algorithm, quantum computers can scan unstructured databases significantly faster than classical computers. This advantage scales exponentially the larger the database gets. Essentially quantum computers can take into account multiple entries at once using their quantum properties instead of going through each entry at a time like their classical counterparts.

### Substance and Quantum Modeling

In the hands of the right physicists and engineers, quantum computers can work magic in areas like medicine, manufacturing, and material science. That’s because classical computers have a hard time understanding and modeling how molecules, atoms, and substances, in general, react with each other in a natural environment. Partly because these problems can be too complex, demanding too many interacting variables to be taken into account. But also because all matter exhibits quantum behavior on the microscopic scale, which is extremely difficult to simulate.

Quantum computers can easily simulate these systems, which will significantly speed up research in areas like material sciences and drug discovery.

## Top Things Software Developers Need to Build Quantum Algorithms

Most of the major applications of quantum computers come down to building the right quantum algorithms. Which begs the question: How does one write quantum code?

Well, the process isn’t much different than writing traditional code, but it also isn’t as simple as opening a new file in a Python development environment. Here is a list of what developers need to know and implement in order to write quantum algorithms:

### Strong Conceptual Understanding

As mentioned, quantum mechanics relies on weird quantum effects like superposition, interference, entanglement, and more. To build algorithms that can exploit these, developers need to have a strong background in quantum mechanics as well as understand how these effects can interact with each other in a computational environment.

### Quantum Languages

There are several new languages and libraries designed specifically for building quantum algorithms. The most notable ones are:

- QISKIT;
- Q#;
- CIRQ;
- And python tools and packages like Ocean and QuTip.

Developers must learn these new languages and tools to build effective quantum algorithms.

### Noise and Error Management

As mentioned, quantum systems are extremely fragile, making them highly vulnerable to noise. Even a tiny photon or a marginal error in a quantum code can screw up the computer’s accuracy big time. Also, because quantum computers work with probabilities, it is often necessary to run the same calculations multiple times to ensure we’ve arrived at the right answer. Developers are required to understand these nuances and account for possible errors and noise within their algorithms.

### QA and Testing

Testing and debugging quantum algorithms can be yet another challenge because quantum states are probabilistic. When qubits are in superposition, we don’t really know about their states, making it difficult to identify and resolve any potential problems within the code since one cannot pinpoint the problem down to a single unit. Developers will have to come up with new strategies to ensure a smooth testing, debugging, and QA process for quantum algorithms.

All things considered, the learning curve for building quantum software is steep. Devs will need to master new languages, learn quantum mechanics, and devise novel ways of error management and debugging. The result, however, will be a paradigm shift in computing that can truly change the world.

## Conclusion

Quantum computing is no longer a concept confined to science fiction; it’s becoming a reality. Major companies, such as IBM and Google, are making significant strides in the development of quantum computers, sparking excitement within the broader community as we inch closer to this futuristic vision.

This emerging era of quantum computing is unveiling a wide array of possibilities that can drastically reshape many facets of our lives in ways we might not fully comprehend yet. However, to effectively leverage this upcoming technology, it’s crucial to partner with a future-focused firm like QArea. Reach out to us for all your needs in web design, development, testing, and IT consulting.

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