A guided history of quantum computing — from Max Planck's first quantum of energy in 1900 to today's error-corrected processors. Read it top to bottom as a course, or jump to any era below.
Quantum computing did not begin as computing at all. It began as a crisis in physics — classical theory could not explain how matter absorbed and radiated energy, and the fixes that scientists proposed opened up an entirely new picture of nature.
To explain the glow given off by heated objects, Max Planck proposed that energy is exchanged only in discrete packets, or "quanta," rather than as a smooth continuous flow. This single assumption cracked open classical physics and gave quantum theory its name.[1]
Over the next two decades, Niels Bohr's atomic model, Werner Heisenberg's matrix mechanics, and Erwin Schrödinger's wave equation each supplied a different mathematical language for describing quantum behavior — languages that would later become the working vocabulary of quantum computing.[1][2]
Einstein, Podolsky, and Rosen challenged the idea that two particles could remain correlated no matter the distance between them — what Einstein reportedly dismissed as "spooky." The debate they started was only resolved decades later, and entanglement is now a core resource that quantum computers exploit.[1]
For roughly fifty years, quantum mechanics stayed a tool for describing nature — not for computing with it. That changed once physicists noticed that simulating quantum systems on ordinary computers was itself an impossibly hard problem.
Physicist Paul Benioff, at Argonne National Laboratory, showed that a Turing machine — the abstract model underlying all classical computers — could in principle be built from purely quantum mechanical components, giving quantum computing its first theoretical footing.[3]
At a conference at MIT, Richard Feynman argued that simulating a quantum system on a classical computer requires resources that explode exponentially with the number of particles involved — and that only a computer built from quantum components could keep up. Mathematician Yuri Manin raised a closely related point independently around the same time.[4][5]
David Deutsch, at Oxford, described a theoretical machine that could simulate any other quantum system — a quantum counterpart to the universal Turing machine. He also introduced "quantum parallelism," the idea that a quantum computer can explore many possible computations in a single run.[6]
Having a theoretical machine is one thing; giving it something worth doing is another. This decade produced the algorithms that convinced governments, universities, and eventually companies that quantum computing was worth serious investment.
David Deutsch and Richard Jozsa devised a toy problem where a quantum computer clearly beat any classical approach. Daniel Simon's 1994 algorithm went further and directly inspired the technique behind the field's breakthrough result the same year.[7]
Charles Bennett and collaborators outlined a protocol for transferring an unknown quantum state between two distant particles using entanglement — not a way to teleport matter, but a way to move quantum information, later verified experimentally in 1997.[8]
Peter Shor, at Bell Labs, showed that a sufficiently powerful quantum computer could factor large numbers in polynomial time — a task believed to be intractable classically, and one that underpins the security of RSA encryption. This single result is widely credited with turning quantum computing from a curiosity into a funding priority.[9][10]
Shor and, independently, Andrew Steane, showed that quantum information — thought to be too fragile to preserve — could actually be protected against noise using clever encoding schemes, answering one of the strongest objections to building real quantum computers.[11]
Lov Grover, also at Bell Labs, devised a quantum algorithm offering a quadratic speed-up for searching unsorted data — today the second most-cited quantum algorithm after Shor's. Alexei Kitaev's 1997 toric code then introduced a way to protect quantum information topologically, an idea that underlies the "surface codes" used in the field's most recent hardware.[12][9]
Algorithms on paper needed to be run on something real. Early experiments were tiny and fragile, but they proved the underlying physics actually worked outside a blackboard.
Researchers at IBM and Stanford ran a scaled-down version of Shor's algorithm on a seven-qubit nuclear magnetic resonance system, successfully factoring the number 15 — a modest number, but proof that a quantum algorithm could run on physical hardware.[13]
Canadian company D-Wave released a quantum annealer — a specialized machine designed for optimization problems rather than general-purpose computing — becoming the first company to sell a quantum computing system commercially.[14]
Researchers at Bristol and Queensland used light particles to factor the number 21 on a photonic processor. That same year, physicist John Preskill coined the term "quantum supremacy" to describe the future moment a quantum machine would outperform any classical computer at some task, however impractical.[13][9]
Through the 2010s, competing labs pushed qubit counts up while fighting the same enemy: noise. The decade closed with the first widely publicized claim that a quantum processor had done something no classical machine ever could.
Preskill coined a second influential term, "Noisy Intermediate-Scale Quantum" (NISQ), to describe the current generation of quantum devices: powerful enough to be interesting, but too error-prone yet to be reliably useful.[9]
Google reported that its 53-qubit Sycamore processor completed a random circuit-sampling task in about 200 seconds — a calculation the team estimated would take the era's fastest supercomputer roughly ten thousand years. IBM and others publicly disputed how large that classical gap really was, a debate that has continued with each new result since.[15][16]
The current phase of the field is less about single dramatic demonstrations and more about the unglamorous, essential work of making qubits reliable enough to trust — while hardware companies race to scale up.
IBM progressed through processors such as Eagle and the 1,121-qubit Condor, while other companies pursued different physical approaches — trapped ions, photonics, and neutral atoms among them — each betting on a different path to reliable qubits.[17]
Anticipating that a future large-scale quantum computer could eventually break widely used encryption via Shor's algorithm, the U.S. National Institute of Standards and Technology finalized its first standardized "post-quantum" cryptographic algorithms, designed to resist quantum attack.[18]
Google unveiled Willow, a 105-qubit superconducting processor, and reported a long-sought milestone: as the number of physical qubits grouped into a single protected "logical" qubit increased, the error rate fell instead of rising — the first clear demonstration that quantum error correction can work below a critical noise threshold.[19][20]
Google reported a further "verifiable" quantum advantage result on Willow in late 2025, Microsoft advanced a different, topological approach with its Majorana chip, and Caltech researchers assembled a neutral-atom array exceeding 6,000 qubits. Most major roadmaps still target a genuinely useful, error-corrected quantum computer only by the end of this decade — the work of making quantum computing practical, rather than merely possible, is very much ongoing.[20][17]
Sources consulted while researching this page. Content above has been written in original language and independently organized — it is not copied or closely paraphrased from any single source. Consult these directly for authoritative detail.