Quantum computing, once confined to laboratory experiments, has become a contested field where American technology giants, startups, and state-backed programs compete for primacy. IBM opened the door to wider access in 2016 when it launched the IBM Quantum Experience, a cloud-based service that let researchers and developers run experiments on quantum processors remotely.
That early bet gave the company an initial lead in making quantum hardware available beyond academic institutions. Since then, the landscape has grown crowded with competitors pursuing different technical paths.
Diverse Approaches and Growing Competition
Google’s Sycamore processor reached 53 qubits in 2019, a milestone the company used to claim quantum supremacy — the point at which a quantum machine performs a calculation impossible for a classical computer. Microsoft is developing topological qubits, a design that promises greater stability, and offers cloud access through its Azure Quantum platform.
IonQ, using trapped-ion technology, announced a 32-qubit system in 2021. Rigetti Computing builds superconducting processors and also provides cloud access. D-Wave Systems focuses on quantum annealing, a method suited for optimization problems.
Honeywell, another major industrial player, develops trapped-ion quantum computers. Each approach carries trade-offs.
Superconducting qubits, used by IBM and Google, must be kept near absolute zero and suffer from short coherence times, but they benefit from established semiconductor manufacturing techniques. Trapped-ion systems, pursued by IonQ and Honeywell, offer longer coherence but are harder to scale. Microsoft’s topological qubits remain largely theoretical but could prove more resistant to errors if realized. The industry is still years away from a fault-tolerant, general-purpose quantum computer, and the key question is which method will scale to useful size while managing error rates and manufacturability.
Government investments have intensified the competition. National laboratories such as Los Alamos are conducting research, and the United States, China, and Europe have all committed public funds to quantum development.
The race is no longer restricted to individual companies; state-backed programs are pouring significant resources into advancing the technology.
IBM’s Roadmap and the Cloud Model
IBM has not remained static amid the growing field. In 2021, the company unveiled its 127-qubit Eagle processor and announced plans for a 1,121-qubit Condor chip by 2023.
That roadmap gives the firm a clear narrative focused on scale, projecting a thousandfold increase in qubit count within a few years. For now, cloud access remains the primary way researchers and enterprises interact with quantum hardware, and IBM’s early start in that model, combined with its aggressive qubit targets, keeps it a central player. The next major milestone to watch is whether the Condor chip arrives on schedule and whether it performs as promised.
The company’s long history helps explain why it moved early into quantum hardware delivered through the cloud. IBM began in 1911 as a holding company for record-keeping equipment and adopted the International Business Machines name in 1924.
It grew into the dominant supplier of mainframe computers through the middle of the last century and shaped the personal computer market with its 1981 release, whose design became the template for most machines that followed. After selling its PC business, IBM shifted focus toward services, software, and frontier research. Its labs produced a long string of computing inventions and held a record for sustained patent output across several decades.
That research heritage, along with a background in large-scale systems and industrial science, gives the company credibility in a field still far from practical fault-tolerant machines.
Why This Race Matters
The outcome of the quantum computing race will affect areas far beyond the technology sector. Quantum computers could reshape encryption, making current cryptographic systems vulnerable. They could accelerate materials science by simulating molecular interactions that classical computers cannot handle.
In logistics, quantum algorithms might optimize supply chains and routing problems on a scale not possible today. IBM’s roadmap and its open-access cloud model will influence who gets to experiment with that future and on what terms.
As the industry awaits the Condor chip and the next round of results from competing approaches, the fundamental question remains: which technology will scale into a reliable, general-purpose tool that can deliver on those promises?
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