Quantum Computing 2025: What It Is, How It Works, and Why It Is Strategic for Software and AI

What is Quantum Computing?

Quantum computing is a technology that uses qubits to process information leveraging the laws of quantum mechanics. Unlike classical bits, qubits can represent multiple states simultaneously, enabling extremely complex calculations.

In 2025, quantum computing is no longer just academic research: it is entering the real world thanks to the efforts of companies like IBM, Google, Microsoft, and Amazon, as well as numerous highly specialized startups. For developers, CTOs, and R&D teams, understanding how quantum computing works and its impact on software development represents a strategic opportunity.

Quantum Computing Simplified

Picture a classical computer as a huge notepad, where each page can contain only one 0 or 1 at a time. Now, imagine a qubit as a page that can be 0, 1, or both simultaneously (yes, like Schrödinger's cat). This is thanks to two typical effects of quantum mechanics: superposition (a qubit can be in multiple states simultaneously) and entanglement (qubits that "communicate" with each other even at a distance).

The advantage? Quantum computers can explore many possibilities in parallel computing, depending on the algorithm's design and measurement, making them ideal for super-complex problems like simulating molecules, optimizing logistics networks, or developing new materials.

Useful Tools to Start: Quantum Simulators

• IBM Quantum Composer → Create quantum circuits with a drag&drop interface.
• Quantum Playground (Google) → Simulates up to 22 qubits in the browser.
• Microsoft Quantum Simulator → Integrated with Visual Studio, perfect for Q#.

Differences with Classical Computing

Quantum computing stands apart from classical computing in several ways:

1. Unit of Information: As we've seen, qubits can represent both 0 and 1 simultaneously.
2. Processing Capability: Thanks to superposition, quantum computers can perform massive parallel calculations.
3. Algorithms and Speed: Quantum algorithms such as Shor's for factoring and Grover's for unstructured search offer significant advantages over classical algorithms.
4. Computational Approach: While classical computing is deterministic, quantum computing is inherently probabilistic, requiring multiple measurements for reliable results.

The Opportunities of Quantum Computing for Software

Cryptography and Information Security

With the advent of quantum computing, some encryption algorithms considered secure today might no longer be safe. In particular, algorithms like Shor's pose a threat to public-key cryptography, such as RSA and ECC, as they allow solving mathematical problems that classical computers take years to solve.

To counter this threat, the so-called post-quantum cryptography (PQC) is being developed: a set of new algorithms designed to withstand attacks from quantum computers. The NIST published the first official standards in 2024, marking an important step towards adopting more secure systems.

It is worth noting that not all cryptography is at risk: symmetric algorithms, like AES, and hash functions, like SHA-2, are still considered secure, although they may require longer keys in the future.

Complex Optimization and Simulations

Quantum computers are particularly effective at optimization problems, where the goal is to find the best solution among millions of possibilities. This is the case in logistics, supply chain management, finance, and the energy sector.

Algorithms like the QAOA (Quantum Approximate Optimization Algorithm) allow tackling these problems more efficiently than traditional approaches. In supply chain management, for example, quantum computing can help improve planning, reduce costs, increase visibility along the supply chain, and manage risks more responsively.

It is an evolving field, but the potential is concrete, especially in high-complexity contexts.

Quantum Machine Learning (QML)

QML represents the intersection of artificial intelligence and quantum computing, promising to revolutionize the training and use of AI models. Some hybrid algorithms like the Variational Quantum Eigensolver (VQE) and QAOA are also explored in the machine learning context, leveraging cooperation between classical and quantum machines. The potential of QML extends to fields such as finance, medicine, industrial process optimization, and cybersecurity.

Chemistry and Materials Science

One of the most promising applications of quantum computing involves the simulation of molecules and materials. Quantum computers are inherently suited to simulating quantum systems such as atoms and molecules, allowing much more accurate simulations than classical computers. These simulations can speed up the discovery of new drugs and advanced materials.

The Challenges of Software Development for Quantum Computing

Programming Languages

Writing quantum code requires languages specifically designed for this paradigm. Among the main ones:

• Q# (Microsoft), part of the Quantum Development Kit.
• Qiskit (IBM), open-source and Python-based.
• Cirq (Google), for developing quantum circuits.

These languages handle complex concepts like superposition, entanglement, and interference, and comply with typical quantum mechanical constraints like the no-cloning theorem.

Quantum Errors and Correction

One of the main obstacles is the fragility of qubits, which can easily lose their properties due to environmental interference (decoherence).
To prevent errors from compromising calculations, correction techniques like the surface code are being developed, which aggregate more physical qubits to represent one logical qubit.

Technical Note for Devs and CTOs: adopting QEC involves a significant trade-off between stability and scalability, as increasing error tolerance requires significantly more complex hardware.

Hardware and Scalability

Quantum computing is still in an intermediate phase called NISQ (Noisy Intermediate-Scale Quantum). Current machines have a limited number of qubits and are subject to noise.

The main technologies in development include:

• Superconducting qubits (IBM, Google): mature but require very low temperatures.
• Trapped ions (IonQ, Quantinuum): stable, but slower to scale.
• Neutral atoms (QuEra, PASQAL): offer greater connectivity.
• Photonic qubits: operate at room temperature, ideal for the cloud.
  The number of effectively usable logical qubits is the most important parameter to monitor, even more than the total number of declared qubits.

Adapting Classical Algorithms

A common mistake is to think that it is enough to "translate" a classical algorithm into a quantum one. In reality, quantum computing requires a paradigm shift.
There are two approaches:

• Quantum-native algorithms, like Shor's or Grover's, designed from the ground up for the quantum environment.
• Hybrid algorithms, like VQE and QAOA, that combine classical and quantum computation.

In 2025, the most concrete path for R&D teams is to start with these hybrid approaches, testing them in cloud environments and evaluating the real computational advantage case by case.

Who is Shaping the Quantum Future

Big Tech

Major tech companies are leading the development of quantum computing, investing in hardware, cloud platforms, and programming tools:

• IBM aims to surpass 1,000 qubits with their Condor processor and continues to expand the Qiskit ecosystem in the cloud.
• Google, after achieving “quantum supremacy,” is now focusing on practical utility with the new Sycamore 2 chip.
• Microsoft offers Azure Quantum, a platform that integrates several back-ends (Honeywell, IonQ, Quantinuum) and supports the Q# language.
• Amazon, with Braket, provides a multi-backend environment to experiment with different hardware technologies in the cloud.

Startups Changing the Rules

2025 is the year when several quantum startups begin to emerge with concrete solutions and innovative technologies:

• QuEra Computing (USA) works on systems based on neutral atoms, with high scalability potential.
• Classiq (Israel) automates the design of optimized quantum circuits.
• SeeQC (Italy-USA) develops modular chips with integrated electronics to reduce latency.
• IQM (Finland) creates bespoke quantum infrastructures for data centers, focusing on vertical integration.

Many of these entities offer APIs, SDKs, and cloud-native environments designed for developers, data scientists, and research teams eager to start experimenting immediately.

Conclusion: Why You Should Start Getting Interested Now

You don’t need to be a theoretical physicist to start working with quantum. In 2025, there are open-source tools, simulators, and cloud environments to test and learn. For R&D teams, CTOs, and the most tech-savvy developers, understanding what quantum computing can (and cannot) do is already a competitive advantage.

The future is hybrid: classical and quantum systems will collaborate. And those who start getting familiar now with concepts like entanglement, no-cloning, and quantum circuit design will be ready for when the practical utility of quantum computers reaches mainstream adoption.

FAQs - Frequently Asked Questions

What is a qubit and how does it work?

A qubit is the basic unit of quantum computing. Unlike a classical bit, it can represent both 0 and 1 simultaneously due to quantum superposition, allowing for much more powerful parallel calculations.

What problems does quantum computing solve?

Quantum computing is particularly effective in complex optimization problems, molecular simulation, advanced cryptography, and large-scale machine learning, where classical computers struggle to provide competitive performance.

Will quantum computing replace classical computers?

No, quantum computing will not replace classical computers. The two systems will work together in a complementary fashion, with quantum used only for specific problems where they offer a real advantage.

What are the programming languages for quantum computing?

The most used languages in quantum computing are Q# (Microsoft), Qiskit (IBM), and Cirq (Google). They are designed to create and simulate quantum circuits and integrate cloud tools to test algorithms even without physical hardware.

Where can you try a quantum computer today?

It is possible to test a quantum computer through cloud platforms like IBM Quantum Experience, Amazon Braket, and Microsoft Azure Quantum, which offer free or freemium access to simulators and real hardware.

How powerful is a quantum computer today?

Today's quantum computers operate in the NISQ (Noisy Intermediate-Scale Quantum) phase, with dozens or hundreds of physical qubits. However, the effectively usable logical qubits are still few due to noise and errors.

What is the difference between a classical algorithm and a quantum algorithm?

A quantum algorithm uses superposition and entanglement to explore many solutions simultaneously. Unlike classical algorithms, it can solve certain problems exponentially more efficiently.

How to start programming in quantum computing?

To start developing in quantum computing, open-source tools like Qiskit, Cirq, or Q# can be used. There are simulators, cloud environments, and free tutorials available to experiment without needing quantum hardware.

What is post-quantum cryptography (PQC)?

Post-quantum cryptography (PQC) is a set of algorithms designed to withstand attacks from quantum computers. The NIST released the first official standards in 2024, initiating widespread adoption.

Can quantum computing be used in machine learning?

Yes, with Quantum Machine Learning (QML), it is possible to combine quantum and classical algorithms to enhance model training. It is useful in finance, medicine, security, and industrial optimization.