How does a quantum computer run continuously for hours?

Quantum computers have long struggled to maintain stable operation for more than a few minutes due to the persistent problem of atom loss, which disrupts calculations and resets quantum states.

Environmental interference and instability at the atomic scale frequently lead to errors that have stymied progress in practical quantum computing.

Traditional silicon-based systems rely on predictable transistor functions. Still, quantum devices manipulate subatomic particles in a state of superposition and entanglement, making them highly sensitive to even minor environmental changes.

This fragility has kept operational durations short, limiting the technology’s commercial applications.

What limits quantum computer runtime?

Most quantum computers face instability as atoms serving as qubits are easily lost or decohered during computation. Cooling mechanisms, isolation chambers, and error-correcting codes help mitigate disruptions; yet, the fundamental challenge remains maintaining coherence among thousands of particles for extended periods.

Atom loss is especially problematic for large-scale systems, as a single lost or altered atom can cause cascade failures in computation.

Scientists have pursued new architectures, such as neutral atom arrays and advanced control systems, to extend processing time and reliability.

Did you know?
Quantum computers can theoretically solve problems in seconds that would take classical supercomputers longer than the age of the universe to complete.

How did Harvard achieve a two-hour operation?

Harvard’s breakthrough utilized a neutral atom quantum computing platform with 3,000 qubits, employing sophisticated laser trapping and error correction methods.

The team developed novel approaches to stabilize atoms in optical traps, significantly reducing losses and allowing calculations to run for more than two hours without interruption.

Senior author Mikhail Lukin emphasized the scalability of this method, noting that the system’s robustness may enable even larger quantum computers to achieve similarly long or longer operational times.

This accomplishment demonstrates that practical quantum computing is moving closer to mainstream adoption.

What commercial advances make quantum computing viable?

Recent months have seen quantum processors tackle real-world tasks previously considered impossible for classical computers.

IBM enabled HSBC to execute bond trading algorithms on quantum hardware, marking the first tangible application of quantum technology in financial services.

Meanwhile, Nvidia’s quantum acceleration toolkit achieved performance gains of up to 4,000 times in quantum simulations.

Google’s Willow quantum chip achieved significant error reductions as the number of qubits increased, with certain calculations now completed in minutes, rather than the septillions of years required by classical machines.

The sector expects explosive growth, with new quantum computing campuses, like Chicago’s recent project, beginning to anchor industry hubs.

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How does biological AI differ from classical computers?

In parallel with advances in quantum computing, biological computing is emerging as a radically efficient AI paradigm. Australian startup Cortical Labs unveiled CL1, a commercially viable computer powered by living human brain cells.

Neuron-based networks learn from minimal examples, adapt in real time, and consume only a fraction of the energy required by silicon-based AI systems.

Unlike classical systems, biological computers can maintain neural connections for several months, supporting advanced applications in drug discovery and disease modeling.

Swiss firm FinalSpark has enabled remote experimentation with brain organoids, making access to biological intelligence available for research and development.

What’s next for quantum and neuron-powered tech?

Both quantum and neuron-based computing promise to redefine the boundaries of technology across industries. Developers are exploring quantum hardware for life sciences, energy, and advanced AI, while biological computers pave the way for breakthroughs in personalized medicine and adaptive learning.

As researchers overcome stability and scaling challenges, the potential for these next-generation processors will extend into consumer, medical, and defense sectors.

The competition between quantum’s raw computational power and the adaptive intelligence of living cell-based systems signals a transition away from purely silicon-based solutions.

With market momentum building and experimental systems crossing new technical thresholds, quantum and brain-powered computers are together forging the future of computation, promising speed, adaptability, and energy efficiency that are not possible with legacy technologies.