Quantum computing explained simply: it is a different way of computing, built on the physics of the very small, that makes a narrow class of problems dramatically faster to solve. Not every problem, and not soon for most of them, but the narrow class includes one that matters to anyone responsible for protecting data: breaking the public-key encryption the modern internet runs on.
That single implication is why quantum computing stopped being only a physics story and became a security planning story. Governments have passed migration laws, standards bodies have published replacement cryptography, and adversaries are widely assessed to be collecting encrypted data today in anticipation of decrypting it later.
This article explains how quantum computing actually works, where the technology genuinely stands, what it does and does not threaten, and what the U.S. government has already done about it. No hype in either direction.
Quantum computing is a model of computation that uses quantum-mechanical behavior, chiefly superposition and entanglement, to process information. Instead of transistors representing definite 0s and 1s, quantum processors manipulate qubits, physical systems such as superconducting circuits, trapped ions, or photons whose quantum states can encode and process information in ways classical hardware cannot.
The result is not a faster version of your laptop. For most workloads, classical computers remain better and always will be. Quantum computers matter because for a small set of mathematically special problems, simulating molecules, certain optimizations, and factoring the large numbers behind public-key encryption, quantum algorithms scale in fundamentally better ways.
A bit is either 0 or 1. A qubit, until measured, exists in a superposition, a precise combination of both possibilities at once. Add entanglement, the phenomenon where the states of multiple qubits become correlated so that measuring one constrains the others, and a register of qubits can represent and manipulate an enormous space of possibilities simultaneously.
Two clarifications keep this honest. First, entanglement does not transmit information faster than light; measurement outcomes are correlated, but no usable signal travels, a result known as the no-communication theorem. Second, quantum speedups come from clever algorithm design that choreographs interference between possibilities, not from naively “trying every answer at once.” Both points are commonly garbled in popular coverage, and both matter for judging vendor claims.
Qubits are also fragile. Interaction with the environment causes decoherence, which corrupts computation, so quantum machines require extreme isolation and, at useful scale, quantum error correction that consumes many physical qubits to produce one reliable logical qubit. That overhead is the central engineering obstacle between today’s demonstrations and tomorrow’s cryptographically relevant machines.
The idea took shape in the early 1980s, when Paul Benioff described a quantum mechanical model of computation and Richard Feynman argued in 1981 that simulating quantum systems would require a computer that was itself quantum. David Deutsch formalized the universal quantum computer in 1985.
The security world started paying attention in 1994, when Peter Shor showed that a quantum computer could factor large integers and compute discrete logarithms efficiently, the two hard problems underlying RSA and elliptic-curve cryptography. Lov Grover’s 1996 search algorithm added a quadratic speedup against symmetric ciphers, a far milder result with very different consequences, covered below.
Since then the field has moved from theory to noisy hardware: small processors of increasing qubit counts, headline demonstrations of quantum advantage on contrived tasks, and steady progress in error correction. What has not happened, anywhere, is a machine capable of running Shor’s algorithm against real-world key sizes.
Quantum computing’s security impact is asymmetric, and the asymmetry is the single most useful thing to understand.
Public-key cryptography breaks. RSA and elliptic-curve algorithms secure key exchange, digital signatures, and most of the internet’s trust infrastructure. Shor’s algorithm defeats the math they rely on, so a cryptographically relevant quantum computer would retroactively expose anything protected only by them.
Well-implemented symmetric encryption largely holds. Against AES, the best known quantum attack is Grover’s algorithm, which offers only a quadratic speedup, effectively halving key strength. AES-256 retains roughly 128 bits of effective security against a quantum adversary, which remains computationally out of reach. This is why data-at-rest standards for classified information, which layer AES-256 hardware and software encryption as described in why classified data at rest requires a CSfC architecture, are built on symmetric cryptography with strong key management rather than on the public-key algorithms quantum computing threatens.
The threat does not wait for the computer. In a harvest-now-decrypt-later strategy, an adversary intercepts and stores encrypted data today, betting that future capability will unlock it. For data whose sensitivity expires in months, that bet is irrelevant. For data that must stay protected for decades, intelligence, defense, health, and industrial data among it, the exposure window opened the day collection started. The concept, examined further in what quantum and AI mean for data at rest, is the reason government timelines treat migration as urgent despite the hardware’s immaturity.
Three concrete milestones replaced speculation with policy.
The Quantum Computing Cybersecurity Preparedness Act (December 2022) directed federal agencies to inventory cryptographic systems and prepare migration to post-quantum cryptography, putting harvest-now-decrypt-later into statute as a planning assumption.
NIST finalized the first post-quantum standards on August 13, 2024, concluding a public competition that began in 2016 and evaluated submissions from cryptographers worldwide. The three standards, announced through the National Quantum Coordination Office, are FIPS 203 (ML-KEM, derived from CRYSTALS-Kyber) for general encryption and key establishment, FIPS 204 (ML-DSA, from CRYSTALS-Dilithium) for digital signatures, and FIPS 205 (SLH-DSA, from SPHINCS+) as a signature backup built on different mathematics, with a fourth standard based on Falcon to follow, per coverage of the release. Agencies and vendors were encouraged to begin integration immediately.
National security systems have their own migration track, with NSA guidance setting expectations for transitioning NSS cryptography to quantum-resistant algorithms on timelines running into the 2030s. For programs governed by those requirements, the authoritative sources are the primary documents themselves, cited by name and date, not secondhand summaries.
“Quantum computers will replace regular computers.” No. They are accelerators for specific problem classes and depend on classical computers to operate.
“Entanglement enables faster-than-light communication.” No. Correlation is not communication; no quantum protocol sends usable information faster than light.
“Quantum computers will break all encryption.” No. Public-key algorithms fall to Shor’s algorithm; AES-256 and similar symmetric ciphers retain strong security margins against known quantum attacks.
“Quantum advantage means codebreaking is near.” No. Advantage demonstrations involve contrived tasks. Running Shor’s algorithm against real key sizes requires fault-tolerant machines orders of magnitude beyond anything demonstrated.
“There is nothing to do until the hardware arrives.” No. The standards are published, migration is law for federal systems, and long-horizon data is already exposed to harvest-now-decrypt-later collection.
Quantum computing is real physics, real engineering, and a real long-term security issue wrapped in a great deal of noise. The accurate summary fits in three sentences. A future quantum computer breaks today’s public-key cryptography, which is why NIST has already published replacements and federal law already mandates migration planning. Well-implemented AES-256 symmetric encryption is not meaningfully threatened by known quantum algorithms, which is why layered symmetric architectures remain the standard for protecting stored classified data. And because harvested data can wait for decryption, the planning horizon for anything with long-lived sensitivity has already begun.
For the fundamentals that govern stored data today, see how data-at-rest protection and software full drive encryption implement the layered architecture the current standards require.
Quantum computing uses the physics of very small systems, superposition and entanglement, to process information in ways classical computers cannot. For a narrow set of problems, including the math behind today’s public-key encryption, quantum algorithms scale fundamentally better than any known classical approach.
A qubit is the quantum version of a bit. Instead of being strictly 0 or 1, a qubit can exist in a superposition of both until measured, and multiple qubits can be entangled so their states are correlated. Qubits are physically built from systems like superconducting circuits, trapped ions, or photons.
No. Quantum computers accelerate specific problem classes such as quantum simulation and factoring, and they rely on classical computers for control and everything else. For ordinary workloads, from databases to video, classical hardware remains superior and will stay that way.
No. No existing machine approaches the scale and error correction needed to run Shor’s algorithm against real key sizes. The concern is forward-looking: encrypted data captured today could be decrypted once such a machine exists, which is why migration planning has already begun.
Public-key cryptography, RSA and elliptic-curve algorithms, is the primary casualty because Shor’s algorithm efficiently solves the math it depends on. Symmetric encryption such as AES-256 faces only Grover’s quadratic speedup, leaving it with strong effective security against known quantum attacks.
Against known quantum algorithms, AES-256 holds up well. Grover’s algorithm effectively halves key strength, leaving roughly 128 bits of security, which remains computationally infeasible to break. This is one reason stored-data protection standards are built on layered AES-256 rather than public-key algorithms.
It is an adversary strategy of capturing and storing encrypted data today in anticipation of decrypting it once quantum capability exists. It matters most for data with long sensitivity horizons, because the exposure begins at collection time, years before any quantum computer is built.
On August 13, 2024, NIST finalized FIPS 203 (ML-KEM) for general encryption and key establishment, FIPS 204 (ML-DSA) for digital signatures, and FIPS 205 (SLH-DSA) as an alternative signature scheme, the first outputs of a public competition that began in 2016. A fourth standard is planned.
A U.S. law enacted in December 2022 directing federal agencies to inventory their cryptographic systems and plan migration to post-quantum cryptography. It codified the assumption that waiting for quantum computers to arrive before acting would leave long-lived data exposed to earlier collection.
Nobody knows, and credible estimates span from roughly a decade to considerably longer. The engineering gap, error-corrected logical qubits at scale, is enormous. Planning bodies stopped treating the date as the trigger because harvested data makes the exposure retroactive to today.
Conner Crisafulli is a solutions engineer and cybersecurity professional at Cigent with a unique background bridging elite military operations and advanced data security. Before joining Cigent, Conner served six years as a U.S. Air Force Combat Controller, where he specialized in high-stakes mission planning, communication systems, and joint operations coordination/execution. Drawing from his experience in complex, contested environments, Conner now helps federal agencies and enterprise clients strengthen their data-at-rest protection strategies. At Cigent, he focuses on practical applications of self-encrypting drives (SEDs), pre-boot authentication (PBA), and various CSfC (commercial solutions for classified) technologies to safeguard sensitive data against evolving cyber threats.
