Quantum Computing: What it is and how qubits work

Quantum computing is a computing paradigm that uses qubits — capable of taking 0, 1, or a superposition of both — to process information based on quantum mechanics, solving problems like integer factorization and molecular simulation that are infeasible for classical computers.

• What is quantum computing?
• What is the difference between classical and quantum computing?
• Superposition, entanglement, and interference: the three pillars
• Models of quantum computers: logic gates, annealers, and simulators
• What is quantum computing for in practice?
• Does quantum computing break current cryptography?
• Current challenges: decoherence, errors, and the NISQ era

What is quantum computing?

Quantum computing is the field that designs and programs computing systems based on quantum mechanics phenomena — superposition, entanglement, and interference — instead of the boolean logic of transistors. Its fundamental unit is the qubit, which can exist in multiple states simultaneously until measured, allowing the exploration of an exponential number of possibilities in parallel.

The revolutionary character of quantum computing lies in the class of problems it tackles. Simulating a complex molecule to discover a new drug, for example, requires representing quantum interactions that grow exponentially with the system's size — something that quickly exhausts any classical supercomputer, but that is natural for a processor already operating under the same physical laws.

A classic example is the Traveling Salesman Problem: finding the shortest route among several points. With few points, a classical computer solves it; as the number grows, the total number of permutations explodes and the calculation becomes infeasible. A quantum computer, by exploring multiple paths simultaneously through superposition and entanglement, can evaluate a much larger solution space per operation, making certain optimization problems tractable.

Important: quantum computing is not a "faster classical computer." It is a different model, probabilistic, that accelerates some classes of problems — optimization, quantum-system simulation, factorization, and search — and offers no advantage for most everyday tasks. Here at CodeCrush, we treat the topic as what it is: a research frontier with real impact, but with honest timelines.

What is the difference between classical and quantum computing?

The central difference lies in the unit of information: classical computing uses bits with definite states (0 or 1) and deterministic logic, while quantum computing uses qubits that exploit superposition and entanglement, operating probabilistically over a state space that doubles with each added qubit.

The classical computer operates with boolean logic: each bit carries a single binary value, and the hardware tests possibilities sequentially or with limited parallelism. This architecture is unbeatable for text processing, browsing, databases, and conventional numeric calculation — just as GPUs redefined classical parallelism for AI without changing the binary paradigm.

The quantum computer, in turn, manipulates qubits whose states combine: a register of n qubits in superposition represents 2^n states at the same time. The most cited example is RSA cryptography (Rivest–Shamir–Adleman), whose security depends on the classical difficulty of factoring large numbers — a difficulty that Shor's quantum algorithm would eliminate, as detailed below.

Superposition, entanglement, and interference: the three pillars

Quantum computing draws its power from three phenomena: superposition lets a qubit represent 0 and 1 simultaneously, entanglement correlates qubits inseparably, and interference amplifies the correct answers while canceling the incorrect ones. Quantum algorithms are, in essence, choreographies of these three effects.

1. Superposition is the ability of a qubit to exist as a linear combination of 0 and 1 until measured — when it "collapses" to a classical state with a certain probability. It's what gives rise to quantum parallelism: n qubits in superposition encode 2^n simultaneous states.

2. Entanglement is the phenomenon where two or more qubits become intrinsically linked: the state of one cannot be described without the others, even when physically separated, and measuring one instantly influences the result of the others. Entanglement creates the correlations that quantum algorithms and error correction exploit.

3. Interference is the mechanism by which the probability amplitudes of different computational paths reinforce (constructive interference) or cancel (destructive interference). A good quantum algorithm directs interference so that the states corresponding to the right answer end up with a high probability of being measured.

Grover's algorithm, for searching unsorted databases, illustrates the three pillars together: it uses superposition to query every item at once, entanglement to correlate the states, and interference to amplify the sought item — delivering a quadratic speedup over classical search.

Models of quantum computers: logic gates, annealers, and simulators

Today's quantum computers follow three main models: gate-based machines (universal and programmable), quantum annealers (specialized in optimization), and analog quantum simulators (dedicated to mimicking physical systems). Each model trades generality for construction feasibility.

Gate-based computers are the most versatile model, analogous to classical AND/OR/NOT gates: sequences of quantum gates manipulate the qubits to run algorithms like Shor's and Grover's. Physical implementations vary — superconducting qubits (IBM, Google), trapped ions (IonQ, Quantinuum), and topological qubits (Microsoft). Google's Sycamore processor marked the field in 2019 by running, in about 200 seconds with 53 qubits, a sampling that the company estimated would take 10 thousand years on a classical supercomputer, a result published in Nature. In December 2024, the successor Willow, with 105 qubits, demonstrated "below-threshold" error correction — errors fall exponentially as qubits are added — and completed in under 5 minutes a benchmark that Google estimates at 10 sextillion years for today's fastest supercomputer, according to the official Google announcement. In the same race, IBM introduced the Condor chip in December 2023, with 1,121 superconducting qubits, and maintains a public quantum hardware roadmap with goals into the 2030s.

Quantum annealers, pioneered by D-Wave Systems, are not universal: they seek the minimum-energy state of a system, which corresponds to the optimal solution of an optimization or sampling problem. They are easier to scale but restricted to that class of problems.

Analog quantum simulators use controllable systems (cold atoms, photons) to mimic other quantum systems that are hard to model classically — valuable in materials physics and chemistry, but without general-purpose programmability.

What is quantum computing for in practice?

Quantum computing serves, today, mainly for research in chemistry, materials science, optimization, and cryptography — areas where simulating or exploring exponential spaces of possibilities is the bottleneck. Commercial applications at scale still depend on fault-tolerant machines, expected in the coming years.

The most promising use cases include:

• Drug and materials discovery: simulating molecules and chemical reactions with native quantum accuracy, accelerating the design of medicines, batteries, and catalysts.
• Logistics and financial optimization: delivery routes, portfolio allocation, and risk pricing are combinatorial problems that annealers and variational algorithms already explore in proofs of concept.
• Machine learning: research in quantum machine learning investigates gains in sampling and model optimization — a complement, not a replacement, to the classical machine learning fundamentals that underpin today's AI (Artificial Intelligence).
• Cryptography and security: both as a threat (Shor's algorithm) and as a defense (quantum key distribution and post-quantum standards).

Practical access is already a reality via cloud computing: IBM Quantum Platform, Amazon Braket, and Azure Quantum expose real processors via API, in the same consumption model as traditional cloud services like AWS, Azure, and GCP. For the developer, that means writing circuits in Python with SDKs like Qiskit and running them remotely without owning cryogenic hardware.

Does quantum computing break current cryptography?

In theory, yes: Shor's algorithm, published by Peter Shor in 1994, factors integers exponentially faster than any known classical method, which would break RSA and other public-key ciphers. In practice, no quantum computer with enough stable qubits to attack real keys exists yet — but the defensive migration has already begun.

The industry's response is post-quantum cryptography (PQC): classical algorithms designed to resist quantum attacks. In August 2024, the NIST (National Institute of Standards and Technology) finalized its first three standards — FIPS 203 (ML-KEM, key encapsulation), FIPS 204 (ML-DSA, digital signatures), and FIPS 205 (SLH-DSA, hash-based signatures) — concluding a standardization process started in 2016, per the official NIST PQC project.

The relevant risk today is "harvest now, decrypt later": adversaries can intercept and store encrypted traffic now to decrypt it once capable machines exist. That's why information security teams at banks, governments, and cloud providers have already begun migrating to the new standards, even with the quantum attack still distant.

Current challenges: decoherence, errors, and the NISQ era

The biggest obstacle in quantum computing is the fragility of qubits: any interaction with the environment — heat, vibration, radiation — destroys superposition, a phenomenon called decoherence. That's why processors operate near absolute zero, and even so accumulate errors that require complex correction schemes, consuming tens or hundreds of physical qubits to form a single reliable logical qubit.

Physicist John Preskill (Caltech) coined the term that defines the current stage in 2018: NISQ (Noisy Intermediate-Scale Quantum). In the article Quantum Computing in the NISQ era and beyond, he wrote: "Noisy Intermediate-Scale Quantum (NISQ) technology will be available in the near future", warning that these machines would be useful for research, but insufficient to transform the world on their own.

The central challenges can be summarized as:

• Decoherence: quantum states last microseconds; on Google's latest chips, T1 coherence times approach 100 microseconds.
• Error correction: Willow's "below-threshold" result in 2024 was the first experimental sign that adding qubits can reduce errors instead of multiplying them.
• Scalability: going from hundreds to the millions of physical qubits needed for fault-tolerant applications requires advances in fabrication, control, and refrigeration.
• Software and talent: quantum algorithms, compilers, and professionals are still scarce, which opens room for those who enter the field early.

Conclusion

Quantum computing is to the 2030s what classical computing was to the 1950s: expensive, fragile hardware restricted to labs, but with solid foundations and measurable progress — Sycamore proved an advantage in 2019, Willow proved scalable error correction in 2024, and the NIST already hardened cryptography before the threat materialized. For the developer, the pragmatic stance is clear: don't abandon your stack because of hype, but learn the fundamentals (linear algebra, probability, Qiskit) and follow the post-quantum migration, because it will reach your backend before the first useful quantum computer reaches the market.