
The advancements in Quantum Computing technology have been increasing rapidly over the past couple of years. When I first joined the field in 2018, not many people were aware of the practical side of the field or sought it out as much, perhaps because only a few knew what quantum computers were or because we did not have quantum computers at the time. All that has changed since then.
More and more people have become interested in the field, from academia to industry and even some of the public. So, when I was sitting to think about my first article of 2025, the current status of quantum computing seemed like a good idea.
Not just because of how fast the quantum field has come but also because this year, the 100th year since the start of the development of Quantum Mechanics, the United Nations has announced 2025 as the international year of quantum science and technology.
So, what better time to discuss the field for those who are curious about where we are now as a field?
What are quantum computers? A very short intro
But first, for anyone new to the field, let us briefly address what quantum computing is and why anyone should care about it.
The computers we use today (we’ll call them classical computers) work in binary, as in 0 or 1. These represent whether an electric current passes through a circuit; we refer to them as bits. Fundamentally, we use transistors to control whether a current passes through the circuit inside the computer. To simplify things, think of the transistor as a switch; if the switch is closed, a current passes, or we have 1. If it is not, no current passes, and we have 0.
Computers today have millions of tiny transistors (switches), and manipulating the state of those transistors allows us to perform all kinds of computations and solve problems.
Quantum computers, on the other hand, work differently. In quantum computers, we utilize the phenomena of quantum mechanics to solve some problems better. But before I get ahead of myself, let me take a step back and talk about the equivalent of a bit in quantum computers.
Qubits, or quantum bits, are the fundamental building blocks of quantum computers. They are a bit more complex than classical bits, mainly because we can use them to encode more information. You may ask, how are they different?
Classical bits represent whether a current passes in a circuit or not. Conversely, a qubit is a small system (or quantum system), like a photon or a small superconducting circuit. Because of that, qubits give us more freedom to encode information while presenting a different set of challenges (which we will discuss shortly).
Because qubits are quantum systems, we can then manipulate their status (like superposition and entanglement) to perform computations and solve some problems better than classical computers.

The keyword here is "some" problems. This brings me to my next point: Why should we care about quantum computers? What kind of problems will these computers solve better than our current computers?
Problems quantum computers will be good at solving
Quantum computers will be pretty good at solving "Multivariable problems." These problems involve multiple variables that interact with each other and influence the outcome. They are common across various fields, such as mathematics, science, engineering, economics, and machine learning.
Examples of such problems include:
- Finding the maximum or minimum of a function.
- Allocating resources to maximize efficiency or minimize costs.
- Scheduling tasks to minimize total completion time.
- Training models where inputs (features) are multi-dimensional vectors.
- Modeling supply and demand based on price, income, and production cost.
- Designing systems with constraints on multiple variables, such as speed, efficiency, and safety.
What is unique about these kinds of problems is how the variables interact in a complex way. Solving these problems involves understanding their interdependencies and how they collectively influence the system and the final solution. So, we can summarize the challenges in solving these problems as:
- Complex Interactions between the variables.
- As the number of variables increases, the complexity grows.
- Variables may relate in non-linear ways.
- Real-world problems often have uncertain or noisy data.
We use different methods today to attempt to solve these problems to the best of our ability using current Technology. We use techniques like Substitution and Elimination, Gradient Descent, Lagrange Multipliers, Monte Carlo simulations, Neural networks, regression models, or clustering algorithms. Though these techniques work fine for most problems, they may take a very long time (years) to solve something like finding a large number of factors. That is where quantum computers shine. The quantum systems’ ability to exist in superposition and be entangled allows us to solve multivariable problems much faster than we can today.
Now that we know what quantum computers are and what problems they can solve let’s discuss where we are currently in the field. I will discuss the two sides of quantum computing: the hardware side and the software side.
Quantum Hardware
Currently, there are different ways to construct qubits, and researchers are working on improving these methods and developing new ones. But since we are talking about the current state of quantum hardware, we will address the 5 leading methods to construct qubits.
Superconducting Qubits (used by IBM and Google): These tiny superconducting circuits cooled to near absolute zero to create and manipulate quantum states.
Trapped Ions (used by IonQ and Honeywell): This method uses individual ions trapped by electromagnetic fields, with quantum states controlled via lasers. Trapped ion qubits are known for their long coherence times and high precision.
Photonics-Based Systems: Rely on photons (light particles) as qubits for their potential to achieve long-distance communication.
Neutral Atoms: These systems use individual atoms held in place by laser fields, with quantum states manipulated using light. Due to the natural uniformity of atoms, they promise scalability.
Topological Qubits are a theoretical approach based on exotic particles that encode information in their braided paths to produce more robust systems.
Though each of these systems has their own advantages and disadvantages, the challenges facing quantum computing overall are:
Scalability Issues
Scaling quantum systems is quite a complex challenge on different levels, including:
Physical Limitations:
- Each qubit requires precise control and isolation from noise. As the number of qubits grows, maintaining control becomes exponentially more complicated.
- Space and infrastructure requirements (like using cryogenic cooling for superconducting qubits) increase with system size.
Qubit Connectivity:
Quantum algorithms often require qubits to interact with each other.
Control Systems:
Scaling up requires complex control systems to manage each qubit’s operations without introducing additional noise or complexity.
Fabrication:
Producing uniform, high-quality qubits at scale is a challenge for superconducting circuits or trapped ions because variability in manufacturing can lead to inconsistent performance.
Error Rates and Decoherence
Unfortunately, quantum systems are susceptible to errors and loss of information due to their sensitivity to external factors. And that can happen in one of two ways:
Error Rates
- Gate Errors: These are introduced during quantum gate operations due to imperfections in control pulses or environmental factors.
- Readout Errors: Errors that occur when measuring qubits’ states. These can be due to imperfections in the measurement process or interference from neighboring qubits.
- Cross-Talk: Interaction between not intentionally entangled qubits can introduce errors during operations.
Decoherence
Decoherence is the loss of a qubit’s quantum state due to interactions with its environment, such as thermal fluctuations, electromagnetic interference, or the quantum system’s imperfect isolation.
Scientists and companies are working on different approaches to tackle these problems and finding solutions to build larger quantum computers using approaches like Quantum Error Correction.
Software and Algorithmic Development

The hardware aspect of quantum computing is not the only aspect still under development. For scientists and engineers to utilize the current hardware and help advance it, we need software that is on the same level (if not better) to implement algorithms that use the different strengths of quantum systems.
Today, scientists and anyone interested in quantum technology can start using quantum computers using open-source solutions like Qiskit, Cirq, and TKET to develop and implement algorithms for optimization problems (e.g., logistics, financial modeling), quantum chemistry and material science, and cryptography and its implications for cybersecurity.
Though these tools are good for the time being, the software side of quantum is not moving as rapidly as the hardware side. We still need to develop tools along the stack to be ready to utilize the current and future hardware fully.
We need higher-level approaches to implementing algorithms, debugging and testing tools and strategies, and the ability to implement algorithms independently from the target hardware.
Final Thoughts
Though quantum computers have come a long way from the research labs into practice, they still face many challenges. However, the field’s promise and the possible applications it can solve make it an excellent tool to improve our current technology.
So, if you’re wondering if it is too late to get into quantum, I am here to tell you that it is not. In fact, 2025 is a great time to get into quantum and be part of this exciting field with many options.
There are so many opportunities within the field that anyone can contribute to without the need to know quantum physics, mechanics, or even math.
My goal in writing this article is to shed light on the field of quantum computing with a very brief summary of its current state.