The decades-long pursuit to build a practical quantum computer—a technology promising breakthroughs in materials science, AI, and beyond—may finally be nearing a critical milestone. According to a detailed report from the Financial Times, recent technical advances have propelled leading tech giants like IBM and Google closer to turning quantum computing from experimental labs into industrial-scale systems.
In June, IBM unveiled a new blueprint for a quantum computer that addresses key missing components in its earlier designs. Jay Gambetta, IBM’s head of quantum initiatives, confidently stated, “It doesn’t feel like a dream anymore. I really do feel like we’ve cracked the code and we’ll be able to build this machine by the end of the decade.”
Google is similarly optimistic, having cleared one of the toughest technical hurdles late last year. Julian Kelly, head of hardware at Google Quantum AI, said, “All the [remaining] engineering and scientific challenges are surmountable,” with both companies aiming for a full-scale quantum computer by 2030.
Yet, despite these breakthroughs, experts warn that significant engineering challenges remain. Scaling up from current experimental systems, which operate with fewer than 200 qubits—the fundamental units of quantum information—to machines with millions of qubits is a monumental task. A major obstacle is the instability of qubits, which maintain quantum states for only fractions of a second, leading to errors and interference as systems grow larger.
IBM’s recent Condor chip, containing 433 qubits, demonstrated the problem of “crosstalk,” or interference between components, illustrating the complexity of expanding quantum systems. IBM has since developed a new coupling technology to reduce these effects.
Error correction techniques, which create redundancy across qubits to counteract imperfections, are crucial for scaling. Google remains the only company to have demonstrated scalable error correction, while IBM pursues a different method that requires fewer qubits but introduces other engineering complexities.
Mark Horvath, analyst at Gartner, noted that while IBM’s latest design could produce a workable large-scale quantum machine, it remains theoretical until manufacturing challenges are overcome.
Beyond qubit technology, companies face challenges in wiring, cooling, and integrating vast numbers of components into coherent systems. Quantum computers typically require ultra-cold temperatures near absolute zero, adding complexity and cost.
While superconducting qubits—used by IBM and Google—have led the charge, rival approaches involving trapped ions, neutral atoms, or photons promise greater stability but face their own scalability issues.
Government investment and strategic decisions are expected to play a major role in identifying which technologies and companies will ultimately succeed. The Pentagon’s research arm, DARPA, has initiated studies to pinpoint leaders who could deliver practical quantum machines first.
Despite the hurdles, optimism runs high. “Just because it’s hard, doesn’t mean it can’t be done,” said Horvath, capturing the spirit driving the quantum computing race.
