These Physicists Believe Quantum Computers Will Never Work

Quantum computers may never deliver the promised computational advantage because the physics needed to scale them up—especially sustained entanglement and coherence—could fail under realistic noise and possible new foundations for quantum mechanics. The skepticism is a minority view, but it matters because it targets the core bottleneck: even if small prototypes behave “quantum enough,” there’s no direct experimental evidence that large-scale quantum behavior will persist long enough to run practical algorithms.

Entanglement sits at the center of why quantum machines are expected to outperform conventional computers on certain tasks. Quantum bits (qubits) can exploit correlations that standard computers cannot reproduce, enabling operations that are impossible in classical computation. The business case follows: once devices become large enough, difficult calculations could be performed far faster, turning theoretical speedups into products and services. Yet the criticism begins with an uncomfortable gap—large entanglement has never been measured at the scale required for useful quantum computing, and quantum effects are known to fade as systems grow. The mechanism for that fading remains poorly understood, leaving open the possibility that scaling will break the very assumptions behind quantum advantage.

One line of attack focuses on noise. Mathematician and computer scientist Jill Kalai argues that quantum computers face inevitable noise that prevents them from ever reaching a true advantage over classical machines. Physics professor Robert Aliki similarly contends that when noise is modeled realistically, error correction becomes impossible. Leonid Levven adds a coherence-specific concern: maintaining coherence at sufficiently high precision may be thwarted by tiny, unavoidable disturbances such as those induced by neutrinos or gravitational waves. Notably, these critics are not dismissed as random outsiders; they hold relevant expertise. Still, the skepticism hasn’t become mainstream partly because the arguments lack quantitative predictions that map cleanly onto engineering targets.

A second category of skepticism questions whether quantum mechanics itself is fundamental. Steven Woodfr suggests the world is fundamentally discrete, implying quantum computers won’t outperform classical ones in his framework. Gerard ’s cellular automaton theory also treats quantum physics as step-by-step discrete dynamics, predicting that factoring numbers with millions of digits into prime factors won’t be feasible. Tim Palmer goes further with a quantitative ceiling: if quantum physics is ultimately discrete, quantum computation can’t exceed roughly 500 to 1,000 logical qubits (or “logical cubits” in the transcript). Since many commercial estimates place useful applications around 100 to 150 logical qubits, that would leave only a narrow window.

Finally, modified quantum mechanics models—such as spontaneous localization and Penrose’s collapse model—could impose physical limits on coherence. Spontaneous localization estimates suggest that a device with about a million superconducting qubits would have a decoherence time around a millisecond, potentially spoiling practical algorithms. For Penrose’s collapse model, an estimate cited in the transcript suggests gravitationally induced collapse wouldn’t show up until around 10^18 superconducting qubits or more.

Overall, the skepticism remains a minority position, but it’s framed as worth knowing because fringe ideas in science—like tectonic plate drift and jump theory in earlier eras—can later prove correct. The takeaway is less “quantum computers will fail” than “the path to scaling is unproven,” and multiple theoretical mechanisms could, in principle, close the gap between prototype behavior and real-world advantage.