The main limitation of quantum computing lies not in the technological race, but in the fundamental laws of physics. Qubits—quantum bits of information—are extremely vulnerable to any external influence, which calls into question their practical application for complex computations.

The Core of the Problem: Decoherence and Errors

A quantum computer will not replace a familiar laptop or run classical programs. This is not a matter of the current level of development—it is a fundamental limitation. A qubit in a superposition of two states (like a coin that has not yet landed) easily loses this fragile state under the influence of thermal noise, vibrations, or electromagnetic interference. This process, called decoherence, leads to an accumulation of errors as algorithms become more complex.

This is precisely why superconducting processors from giants such as Google and IBM operate at extremely low temperatures—around 10–20 millikelvins, significantly lower than the temperature of cosmic microwave background radiation (~2.72 K). Such cooling requires highly complex cryogenic systems and reduces, but does not completely eliminate, thermal noise.

Physical and Logical Qubits: The Price of Reliability

A physical qubit is a real quantum system: a superconducting circuit, an ion in a trap, or a photon. Due to imperfections in the environment, it "makes noise": for superconducting qubits, the error probability in two-qubit operations is 0.1–1%. Market leaders such as Quantinuum (99.921% accuracy on the Helios system), IQM (99.91%), and IBM (99.9%) have achieved impressive figures, but this is still not enough for long computations.

The solution is logical qubits, where quantum information is distributed across many physical qubits using quantum error correction (QEC). The most promising method is the surface code with a theoretical fault-tolerance threshold of about 1%. Recent breakthroughs are impressive:

  • Google Quantum AI's Willow processor demonstrated exponential suppression of logical errors when scaling the lattice—the error rate is more than halved at each step.
  • Quantinuum Helios (98 physical qubits on barium-137 ions) demonstrated 48 error-corrected logical qubits at a ratio of approximately 2:1.
  • Researchers from Harvard, MIT, and QuEra executed circuits with dozens of logical qubits on a processor of 448 neutral atoms, and in certain configurations—up to 96.

However, the price of reliability is high: it was previously believed that up to 1,000 physical qubits were needed per logical qubit. New qLDPC codes could potentially reduce these costs by an order of magnitude, but still to dozens, not single digits, of physical qubits.

What This Means in Practice

Massive fault-tolerant quantum computers remain only on roadmaps. IBM promises the Starling system with 200 logical qubits by 2029, but even if successful, it will be a highly specialized cloud coprocessor, not a replacement for a data center. The number of physical qubits in itself is not very informative: the quality of operations, error rates, and the system's ability to maintain logical qubits with a low probability of failure are critically important.

The advantages of quantum machines have been formally proven only for specialized tasks (boson sampling, Shor's algorithm), while in everyday scenarios—from text processing to gaming—classical processors remain unrivaled. Expectations are tied to pharmaceuticals, materials science, and chemistry, but optimization and finance have not yet confirmed practical benefits.

My view: The industry is moving from "quantum supremacy" to "quantum utility," but the path is thorny. While corporations and governments invest billions, real commercial applications remain beyond the horizon of 2030. The question is not whether a quantum computer can replace a classical one, but whether it will find its niche before investors' patience runs out.