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What makes quantum-computing approaches different?
A classical bit is represented as either 0 or 1. A qubit is encoded in a quantum system whose state can be prepared, manipulated, and measured. Quantum algorithms process information through controlled operations, including operations that entangle qubits. The algorithm is conceptually separate from the physical device: different hardware can implement programmable quantum circuits using different kinds of qubits and operations.
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Physical qubits are error-prone. To run useful fault-tolerant computations, researchers need to encode logical information across physical hardware and detect and correct errors during computation. That means a machine’s physical-qubit count alone does not tell you how much reliable computation it can perform.
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Most of the hardware approaches below aim at gate-model computing, in which a program applies a sequence of quantum operations. Quantum annealing instead uses an annealing process to seek low-energy solutions to an encoded optimization problem; it should not be treated as interchangeable with a universal gate-model processor.
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The main hardware approaches
| Approach | How it encodes or carries information | Key engineering distinction |
|---|---|---|
| Superconducting circuits | Engineered electrical circuits, commonly using Josephson junctions | Fast operations and chip-based fabrication, but requires very cold cryogenic operation and complex control and scaling infrastructure |
| Trapped ions | Individual ions held in electromagnetic traps | Gate-model platform; elementary operations are slower than those of several fast-clock architectures in a Google Quantum AI landscape paper |
| Neutral atoms | Individually controlled neutral atoms | Active platform for gate-model and logical-qubit research; compare array size, reconfiguration, operations, measurement, and connectivity on specific systems |
| Silicon spin qubits | Electron spin states in semiconductor structures | May draw on semiconductor fabrication; current demonstrations remain distinct from proof of commercial-scale fault tolerance |
| Photonic quantum computing | Photons carry quantum information | Identified as a fast-operation approach in the Google Quantum AI landscape paper; the sources cited here do not establish a fair comparison of architecture-specific performance |
| Quantum annealing | An optimization problem is encoded in an energy landscape and approached through annealing | Targets optimization-style formulations rather than general-purpose programmable quantum circuits |
Superconducting circuits
Superconducting qubits are engineered electrical circuits, often built around Josephson junctions, and operated in a very cold cryogenic environment. The approach combines chip-based fabrication with fast elementary operations, but the processor is only one part of the system: cooling, control wiring, noise management, connectivity, and scaling across modules all matter.
IBM describes its systems as superconducting. Its hardware page, accessed in 2026, lists Heron r1 with 133 programmable qubits, Heron r2 and r3 with 156 each, and Nighthawk with 120. Those are IBM’s model specifications, not a standardized comparison with devices using other technologies.
Trapped ions
In an ion-trap computer, individual ions are held in electromagnetic traps and controlled with lasers or related techniques. Trapped ions are an active gate-model approach. A Google Quantum AI white paper groups ion traps among platforms with elementary operations significantly slower than superconducting, photonic, and spin approaches. That is a broad architecture-level observation in that paper, not a ranking of complete machines or their usefulness for particular algorithms.
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For an actual device, compare operation time alongside gate quality, connectivity, system size, and progress toward error-corrected logical qubits. A slower elementary operation does not by itself establish worse overall performance.
Neutral atoms
Neutral-atom systems use individually controlled atoms as qubits. The FY2025 National Quantum Initiative report includes neutral atoms among the technologies involved in research on entanglement between logical qubits. The Google Quantum AI landscape paper groups neutral atoms with trapped ions as slower in elementary operations than the faster-clock architectures it discusses.
For a specific neutral-atom system, useful comparison questions include how large an atom array can be, how it can be reconfigured, how well operations and measurements perform, and how qubits connect. The sources cited here do not establish current cross-vendor numerical rankings on those measures.
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Silicon spin qubits
Spin qubits encode information in the spin state of electrons. In an article dated 23 July 2026, IBM described an HRL exchange-only design in which three electrons in three silicon-germanium quantum dots form one qubit, controlled with electrical pulses. IBM reported a device with 54 quantum dots arranged on three rails, supporting up to 18 qubits, and described demonstrations of one- and two-qubit gates and small-scale error-detecting codes.
IBM says spin devices share semiconductor-fabrication foundations with superconducting chips and may be manufacturable at scale. The company also gave an illustrative operating-temperature comparison of 1 K for the spin devices and 0.015 K for the superconducting architectures in its comparison. These are IBM’s stated prospects and comparison, not evidence that spin qubits have reached commercial-scale fault tolerance.
Photonic quantum computing
Photonic approaches use photons to carry quantum information. The Google Quantum AI white paper identifies PsiQuantum and Xanadu as developers of photonic quantum computers and groups photonics with architectures that have fast elementary operations. The sources cited here do not establish enough detail to compare particular photonic designs, their handling of photon loss, or present-day system performance, so a general performance ranking would be unwarranted.
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Quantum annealing
Quantum annealing encodes an optimization problem in a physical energy landscape and uses an annealing process to search for low-energy solutions. IBM’s overview identifies annealing as an alternative technology in quantum-computing software contexts. The sources cited here do not provide a dedicated technical account or current machine benchmarks, so they do not support claims that annealing is categorically faster or better than classical optimization—or that an annealer is equivalent to a universal gate-model computer.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to compare quantum computers fairly
Compare specific machines on a defined task, rather than treating a hardware label or qubit count as a performance verdict. IBM’s cross-modality comparison framework emphasizes scale, quality, and speed; its suggested practical questions concern programmable qubits, reliable execution of demanding operations, and circuit throughput. Those are IBM’s framing, not a universal field-wide benchmark.
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minute- Count useful qubits. Distinguish qubits users can program from all physical elements on a chip. Some physical resources support computation without being directly programmable.
- Measure operation quality. Ask how many demanding operations, particularly entangling two-qubit operations on current noisy machines, can be executed reliably before errors overwhelm the task.
- Account for connectivity and compilation. If the hardware cannot directly connect the qubits an algorithm needs, mapping its circuit can require extra operations. Native gate sets and compilation affect the work a machine must perform.
- Separate operation time from throughput. The duration of one operation is not the same as the number of circuits a system can run per second. IBM’s framework uses circuit throughput as a speed measure.
- Look for logical-qubit and error-correction results. Physical-qubit count is not logical-qubit capacity. The FY2025 National Quantum Initiative report describes error correction as identifying and correcting noise during computation and documents logical-qubit research across superconducting, neutral-atom, and trapped-ion platforms.
- Match the architecture to the workload. Annealers target optimization formulations, while gate-model systems are designed for programmable circuits. Even among gate-model devices, results depend on circuit structure and how well the hardware and compiler fit the task.
For fault-tolerant machines, IBM says T-gate count can serve as a useful proxy for computationally demanding operations. It is a measure to consider in that context, not a substitute for specifying the algorithm and the machine’s error-correction performance.
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What current examples do—and do not—show
| Example | Reported figure or status | How to interpret it |
|---|---|---|
| IBM Heron r1 | 133 programmable qubits, per IBM’s hardware page accessed in 2026 | IBM model specification |
| IBM Heron r2 and r3 | 156 programmable qubits each, per IBM’s hardware page accessed in 2026 | IBM model specifications |
| IBM Nighthawk | 120 programmable qubits, per IBM’s hardware page accessed in 2026 | IBM model specification |
| IBM Starling | Planned target of 200 logical qubits and 100 million gates by 2029, per IBM’s roadmap page accessed in 2026 | A company roadmap statement, not a delivered or independently verified future result |
| HRL silicon-spin device described by IBM | 54 quantum dots supporting up to 18 qubits, reported in IBM’s 23 July 2026 article | A specific reported demonstration, not an industry-wide spin-qubit capacity |
These figures use different generations and definitions of capacity, and they describe different kinds of hardware. They are not a cross-platform leaderboard. The FY2025 National Quantum Initiative report’s coverage of logical-qubit work across superconducting, neutral-atom, and trapped-ion technologies likewise shows that logical-qubit research is not confined to one physical modality; it does not establish a winning architecture.
What to take away
The physical qubit is an engineering choice, not the quantum algorithm itself. The major gate-model approaches have distinct trade-offs in fabrication, operating conditions, operation speed, connectivity, and scaling. Annealing addresses a narrower optimization setting. Since reliable operations, circuit throughput, workload fit, and error correction matter alongside scale, the most useful comparison is between defined machines running a defined task—not between modality names or raw qubit totals.
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