Quantum computers use qubits and quantum effects such as superposition and entanglement to process information in ways classical computers cannot. That does not make them magic machines that try every answer and reveal the right one: a useful quantum algorithm must carefully manipulate the system so measurement is likely to reveal information about a particular problem. Today’s machines are experimental, error-prone systems; their most promising uses are specialized, and broadly useful fault-tolerant computers remain a substantial engineering challenge.
What is quantum computing?
A classical computer represents information as bits, each with a value of 0 or 1. A quantum computer uses quantum bits, or qubits. A qubit can be prepared in a superposition of states, and qubits can become entangled, meaning their possible measurement results are correlated in ways that have no direct classical equivalent.
A quantum program prepares qubits, applies operations to change their state, and then measures them. Measurement produces an ordinary classical result, such as a sequence of bits. The quantum state is not a hidden list of answers that can all be read out: measurement returns only limited information from the system.
How does a quantum computer work?
Superposition gives an algorithm more to work with—not every answer at once
Superposition lets a qubit represent a combination of possible states before measurement. With multiple qubits, a system can represent a much richer state than a collection of classical bits. But that alone does not solve a problem. A computation must use its operations to shape the possible outcomes so that useful information is more likely to appear when measured.
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Quantum algorithms do this in part through interference: operations can reinforce some possibilities and cancel others. The algorithm’s design determines whether that effect helps with a particular task. As Stephen Jordan, a Google quantum-computing researcher and former NIST staff member, puts it in NIST’s quantum-computing explainer: “But contrary to popular belief, this doesn’t allow quantum computers to do an efficient ‘brute force’ search over all the potential solutions.”
Entanglement links qubits
Entangled qubits have linked measurement outcomes, so a quantum program may need to manage them as a system rather than as independent units. Entanglement is a resource used by quantum algorithms, not a shortcut that automatically makes a calculation faster. The advantage, when one exists, depends on the problem and on whether a suitable algorithm can exploit the system’s quantum behavior.
Measurement returns a classical result
At the end of a computation, measurement turns the quantum state into a classical outcome. Because one measurement does not expose every component of a superposition, algorithms often need to arrange the computation so that useful results are more likely, and may use repeated runs to estimate an answer. Quantum computers are therefore not general-purpose answer-revealing machines; they are specialized processors whose output must be interpreted in the context of the algorithm.
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What might quantum computers be useful for?
Simulating molecules and materials
Molecules and materials obey quantum rules, so representing their behavior can be difficult for classical computers. Quantum computers may eventually help simulate some of these systems more naturally. Researchers have demonstrated calculations involving small-molecule energies and interacting-atom magnetic properties, but NIST notes that these demonstrations have not yet established truly useful applications.
Selected optimization problems
Researchers are investigating quantum approaches to some optimization tasks. That does not mean a quantum computer will improve every scheduling, logistics, or business problem. An advantage depends on a particular problem, an effective quantum algorithm, and a hardware system capable of running it accurately enough to produce a useful result.
Cryptography and factoring
Shor’s factoring algorithm is central to security discussions because a sufficiently capable quantum computer could threaten some public-key cryptography. That is a future risk, not evidence that today’s devices can decrypt ordinary internet traffic. NIST says a machine able to run Shor’s code-breaking algorithm may require millions of very low-error qubits—far beyond current systems. Work to adopt post-quantum cryptography is a response to that future threat, not proof that current quantum computers can break deployed encryption.
What “quantum advantage” means
A quantum advantage is not established merely because a quantum device completes a specially chosen task or produces a result that is difficult to obtain in a particular classical way. The result also needs to matter for a real scientific or practical goal, and comparisons should account for what classical methods can achieve. NIST notes that classical approaches have matched or exceeded some claimed advantages, while many proposed quantum applications remain years or potentially decades away.
Why useful quantum computers are hard to build
Qubits are sensitive to disturbances
Qubits can lose the delicate states needed for computation when affected by disturbances such as electric or magnetic fields and temperature changes. Such errors can damage superposition or entanglement before an algorithm finishes. NIST’s explainer, updated May 28, 2026, summarizes the field’s best quantum computers as having hundreds of interconnected qubits and making an error roughly once in every thousand operations. That is NIST’s broad summary, not a uniform benchmark for every device or hardware platform.
Error correction requires logical qubits
Fault-tolerant computing aims to protect computation from physical errors using error correction. A logical qubit is an error-corrected unit built from multiple physical components; it is not simply another name for one physical qubit. Building systems with enough reliable logical qubits requires progress across hardware, controls, error decoding, software, system architecture, and algorithms. A large physical-qubit count by itself does not show that a machine can perform a useful fault-tolerant computation.
Hardware approaches make different trade-offs
There is no settled hardware winner. NIST describes two approaches with contrasting strengths and limitations:
| Approach | Strength described by NIST | Trade-off described by NIST |
|---|---|---|
| Trapped ions | Can maintain superpositions for comparatively long periods | Operations are relatively slow |
| Superconducting circuits | Can operate quickly and use chip-fabrication techniques | Quantum states are more fragile and shorter-lived |
Neutral atoms, photons, silicon devices, and other approaches are also being developed. A useful comparison considers how long states remain coherent, how often operations fail, how quickly operations run, how qubits connect, and how well a system can scale with error correction—not just its physical-qubit total.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What quantum hardware exists today?
Commercial and research systems are not all directly comparable: a processor’s published physical-qubit count does not indicate how many reliable logical qubits it can provide. For example, IBM’s current hardware page lists its own processor specifications as follows:
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| IBM-reported processor | Programmable qubits |
|---|---|
| Heron | 133 or 156, depending on the processor |
| Nighthawk | 120 |
These are vendor-reported specifications on IBM’s hardware page; they are physical programmable-qubit counts, not logical-qubit counts or proof of fault-tolerant capacity. IBM also describes Quantum System Two installations at IBM sites and partner centers, and gives 2029 as a target for its future Starling system. That target is a company roadmap goal and may change, not a delivered capability.
What current government programs are trying to achieve
The U.S. Department of Energy says it announced Quantum Genesis in June 2026, with the aim of developing a fault-tolerant, scientifically relevant quantum-computing capability for research and development by 2028. DOE’s September 2026 Q Competition describes up to $215 million in initial planned funding and invites proposals for systems with at least 100 logical qubits and hundreds of millions of fault-tolerant operations. The agency also lists a supporting testbed-lab call with $45 million in planned funding and an October 19, 2026, deadline. These are program aims, planned funding, and proposal requirements—not evidence that the target machines have been delivered. Details are on the DOE / National Quantum Initiative page.
How to start learning about quantum computing
For an accessible book
Chris Bernhardt’s Quantum Computing for Everyone is a paperback introduction intended for readers comfortable with high-school mathematics. MIT Press says it covers qubits, entanglement, quantum teleportation, and quantum algorithms. It is an optional learning resource, not equipment needed to use quantum computers. See the MIT Press book page.
For a free digital course series
IBM describes a free four-course series, “Understanding quantum information and computation,” through IBM Quantum Learning. Its subjects include quantum information and computation, algorithms, general quantum information, and error correction. It is an educational resource from a quantum-computing vendor; check IBM’s page for current course and platform access details: IBM’s learning-series announcement.
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