What Is Quantum Computing?

For cybersecurity professionals, quantum computing represents a dual reality. Quantum computing promises breakthroughs in optimization, simulation, and artificial intelligence. However, it simultaneously poses an existential threat to the public key cryptography that underpins every digital certificate, TLS connection, and code-signing operation your organization relies on. The urgency is compounded by Harvest Now, Decrypt Later attacks, where adversaries capture encrypted data today with the intent to decrypt it once quantum computers mature. This article explains how quantum computing works, traces its evolution, examines the engineering challenges that remain, explores its applications, and, most critically, details the threat it poses to encryption and what security leaders must do to prepare.

How Quantum Computing Works 

Understanding the quantum threat to cryptography starts with understanding how quantum computers differ from classical computing infrastructure. 

How Quantum Computers Process Information 

In a classical computer, information is encoded in bits. Each bit exists in one of two definitive states: zero or one. Every operation a classical processor performs (e.g. every encryption routine, certificate validation, hash computation, etc.) reduces to manipulating streams of these binary values. 

Quantum computers encode information using particles that show quantum properties  (e.g. atoms, ions, or  photons). These properties are not strictly binary, but rather, they are probabilistic. A quantum bit, or qubit, is an idealistic model of these characteristics. It can represent a zero, a one, or both simultaneously. This property, called superposition, is the foundation of quantum computing’s power. 

The scaling implications are (potentially) dramatic.  A system with 100 classical bits can represent one of 2^100 possible states at any given moment. A system with 100 qubits can represent all 2^100 states simultaneously, which opens the possibility of exploring an enormous solution space in parallel. 

Superposition: The Third State 

Superposition is the quantum property that allows a qubit to exist in a probabilistic combination of states rather than a single definitive state. Consider a coin toss: while the coin spins through the air, there is an equal probability that heads or tails is facing up. Only when you catch the coin (or, in quantum lingo, when you measure it) does the outcome collapse to one result. A qubit in superposition behaves similarly, existing in a blend of possible states until measurement forces it into a definitive value. 

This property enables quantum computers to evaluate all possible inputs to a problem simultaneously rather than sequentially. Imagine navigating a maze. A traditional computer would run all possible paths one after the other, testing each route until it finds the exit. A quantum computer can test all possible paths at once, arriving at a solution in a single run. If the algorithm yields the correct path with high enough probability (once the state is measured), then it provides an improvement in running time with respect to its classical counterpart. 

This parallelism is not comparable to  multithreading. Classical parallel processors still evaluate one state per thread. A quantum system in superposition evaluates a superposition of all states within a single computational operation, a fundamentally different model of information processing. 

Entanglement: Quantum Correlation 

Entanglement is the second foundational property of quantum computing. When two or more qubits become entangled, manipulating one qubit simultaneously affects its entangled partners, regardless of physical distance. This correlation makes it possible to coordinate the behavior of multiple qubits, enabling complex calculations that require interdependencies between variables. 

Entanglement is also essential for quantum error detection and correction. Because quantum states are fragile (a challenge discussed in detail later) the ability to distribute information across entangled qubits and detect inconsistencies without directly measuring (and collapsing) the computation is critical to building reliable quantum systems. 

The Quantum Advantage Framework 

Quantum computers are not universally faster than classical machines. As mentioned before, they do not automatically find the right solution in one run. In practice, research has consistently shown that classical computers generally operate faster than quantum computers for many routine tasks but require more steps to complete certain complex operations. The advantage of quantum computing applies to specific categories of problems, particularly those involving large combinatorial search spaces, optimization, and (critically for cybersecurity) the problems associated with classical cryptography (such as factoring and discrete logarithm). 

The quantum advantage operates along two dimensions. First, quantum computers can solve problems that are effectively impossible for classical systems regardless of time or resources. Second, they can solve existing problems faster at comparable cost, a concept sometimes called “quantum economic advantage,” where the speed improvement justifies the investment in quantum infrastructure even when classical solutions technically exist. 

Both dimensions are relevant to the cryptographic threat. Breaking RSA-2048 encryption is not impossible for a classical computer in principle, however, it is impossible in practice because the time required exceeds the age of the universe. A sufficiently powerful quantum computer reduces that timeline from astronomical to practical. 

Where Quantum Computing Applies 

While the cryptographic implications are the primary concern for security leaders, quantum computing’s broader applications explain why investment is accelerating and why the technology will mature. Quantum computers excel at simulating quantum mechanical systems, making them powerful tools for chemistry, molecular modeling, drug discovery, and materials science. In genetics and biological research, quantum computing addresses limitations that classical systems cannot overcome when modeling complex multi-gene interactions and mutations. 

Quantum computing also has the potential to accelerate artificial intelligence training and inference, as the probabilistic nature of quantum computation aligns naturally with machine learning algorithms. Optimization problems – such as global supply chain routing, logistics, financial portfolio management, etc. – are a natural fit for quantum’s ability to explore massive solution spaces in parallel. And in precision measurement, NIST’s quantum research program has demonstrated practical value through improvements in atomic clock precision and laser cooling technology. 

The Evolution and Current State of Quantum Computing 

Quantum computing did not emerge overnight. Its theoretical foundations stretch back more than a century, and its practical development has accelerated substantially since the turn of the century. 

A Brief History of Quantum Computing 

The theoretical groundwork began in the early 1900s, when physicists including Max Planck, Albert Einstein, Niels Bohr, Werner Heisenberg, and John von Neumann established the principles of quantum mechanics that would eventually underpin quantum information science. 

The leap from quantum physics to quantum computing began in the 1980s. In 1981, Richard Feynman proposed that quantum systems could simulate physical phenomena far more efficiently than classical computers. In 1982, Paul Benioff described the first theoretical model of a quantum computer. By 1985, David Deutsch had articulated the concept of a universal quantum computer capable of simulating any physical system. 

The 1990s brought two algorithmic breakthroughs with direct implications for cryptography. In 1994, Peter Shor published his algorithm demonstrating that a quantum computer could factor large integers exponentially faster than any known classical method. These are the mathematical problems whose hardness protects RSA, ECC, and other widely deployed encryption schemes. In 1996, Lov Grover published a search algorithm offering quadratic speedup for unstructured search problems, with implications for symmetric key cryptography. 

The 2010s saw the transition from theory to physical hardware. D-Wave released the first commercially available quantum computing system in 2011, using quantum annealers. IBM made quantum processors available through open-source cloud access in 2016. In 2019, Google claimed having achieved “quantum supremacy” by demonstrating a quantum processor completing a specific computation in 200 seconds that would have taken a classical supercomputer  much longer, with estimates ranging from 2.5 days to 10,000 years.. By 2020, the field entered the Noisy Intermediate-Scale Quantum (NISQ) era, with tens, hundreds or even thousands of physical qubits.  

Starting in 2020, quantum computing shifted towards solving one of the most important challenges in scalability: quantum error correction. Major advances from Google Quantum AI IBM Quantum and others focus on improving qubit fidelity (reducing the probability of errors) and coherence times (increasing the time that the particles show quantum properties), resulting in better logical qubit construction. For more details, see Subsection Quantum Error Correction below.  

The field has reached an inflection point. Universities have established dedicated quantum engineering degree programs, signaling workforce maturation. National security frameworks addressing quantum threats have been formalized at the federal level. NIST has published its first post-quantum cryptography standards, establishing the algorithms that will protect digital infrastructure in the quantum era. 

Multiple Technology Approaches 

The quantum computing field is pursuing several distinct hardware approaches simultaneously: superconducting qubits, trapped ions, neutral atoms, and photonic systems, among others. Each has different strengths, trade-offs, and engineering challenges. 

As Andrew Wilson of NIST has noted, from a physics perspective, there is no fundamental reason why all of these approaches cannot work. The question is which will prove most practical for specific applications, and the answer may well be that different technologies serve different use cases, much as CPUs, GPUs, and specialized processors coexist in classical computing. 

From Theory to Practice 

Small quantum computers have been built and used for research, but none has demonstrated the ability to run Shor’s algorithm on significant input sizes. The qubits required to break production encryption number in the thousands to millions, depending on error correction overhead. This threshold demands resolving the engineering challenges described below. 

This reality should not inspire complacency. As Wilson has observed, quantum computing is “now looking like a serious proposition rather than some sort of fantastical thing.” Quantum systems have demonstrated practical value in mathematical problems, physics simulations – domains that validate the underlying technology even as cryptographic applications remain on the horizon. 

There remains a gap between theoretical algorithms and laboratory implementation that the field is actively working to close. Progress has been rapid, driven by significant public and private investment. 

Engineering Challenges and Technical Barriers 

The timeline from current quantum systems to cryptographically relevant quantum computers is governed by several formidable engineering challenges. Understanding these barriers provides important context for assessing the timeline on which quantum computers will threaten existing encryption. 

Decoherence: Environmental Fragility 

Qubits are extraordinarily sensitive to their environment. Vibrations, temperature fluctuations, and electromagnetic interference can cause qubits to collapse from their superposition state, a phenomenon called decoherence. Once a qubit decoheres, the information it carried is lost, and the computation fails. 

Stabilizing qubits (that is, maintaining their coherence) requires extreme isolation from the external environment. This can be done via supercooled refrigerators, electromagnetic shielding, or vacuum chambers that substantially reduce external interference. The applicability of each technique depends on the hardware architecture. For example, superconducting qubits typically operate in supercooled refrigerators, while ion traps and neutral atom systems can operate at room temperature. These requirements impose significant constraints on where quantum computers can be built and operated, and they directly limit how long a quantum computation can run before accumulated decoherence corrupts the result. 

Quantum Error Correction 

Errors in quantum computation are fundamentally different from errors in classical systems. Because of entanglement, errors in one qubit propagate and amplify through the circuit. More precisely, an error in a single qubit can cascade through entangled partners, corrupting an entire computation. 

The strategy for managing quantum errors involves adding redundancy with additional qubits. The information is distributed across entangled groups, and measuring error syndromes without disturbing the underlying computation. The result is a “logical qubit”, which is an error-protected assembly of many physical qubits that behaves like a single idealized, reliable qubit. It is worth noting that different quantum computing architectures are better suited for different error correcting codes. Different approaches provide trade-offs between physical-to-logical qubit ratio, computational parallelism, clock speeds, etc. 

Recent progress in logical qubit construction has been significant, but the overhead is still substantial.  Current approaches require dozens to hundreds of physical qubits per logical qubit, which means a quantum computer that needs thousands of logical qubits to break encryption may require from tens of thousands to millions of physical qubits. Google’s Willow demonstrated in 2025 that it’s possible to decrease errors exponentially with the number of physical qubits on superconduting quantum computers. Other approaches have a better physical-to-logical ratio, however, their capacity to scale remains unclear.  

Traditional computing also requires error correction. Nevertheless, traditional systems have become so good at adjusting for mistakes that engineers rarely think about it. Quantum error correction has not yet reached that level of maturity. 

Extreme Refrigeration Requirements 

Most leading quantum computing approaches, particularly those based on superconducting qubits, require operating temperatures near absolute zero, that is, approximately -273.15 degrees Celsius. Achieving and maintaining these temperatures demands specialized dilution refrigerators that are expensive, energy-intensive, and difficult to scale. 

Different quantum approaches have varying environmental requirements. Photonic and neutral atom systems operate at significantly higher temperatures, which may give them long-term advantages in practical deployment. The diversity of approaches is partly driven by the desire to find architectures that reduce or eliminate extreme cooling requirements. 

Scalability 

Scaling quantum computers beyond current qubit counts introduces challenges that compound non-linearly. Linking multiple quantum chips requires maintaining coherence and entanglement across physical connections, and the control complexity grows exponentially with qubit count. 

Energy consumption is an emerging concern. While there was early hope that quantum computers would require less energy than massive classical data centers, the reality is more nuanced: if it takes more energy to power smaller quantum computers than classical alternatives, that energy-efficiency benefit does not exist. The field is actively working to address this, but scalable, energy-efficient quantum computing remains an unsolved engineering problem. 

The Quantum Threat to Cryptography 

For security leaders, quantum computing represents, above all, a risk that demands careful preparation. The same computational properties that make quantum computers powerful for optimization and simulation are equally powerful against the mathematical foundations of classical encryption. 

How Classical Encryption Works, and Where It Breaks 

Most currently deployed public key cryptography relies on mathematical problems that classical computers cannot efficiently solve. RSA encryption depends on the difficulty of factoring large prime numbers. Elliptic curve cryptography relies on the discrete logarithm problem. These operations are easy to perform in one direction but computationally impractical to reverse with classical hardware, a property that is often called computational asymmetry. 

Shor’s algorithm, running on a sufficiently powerful quantum computer, reduces the time required to factor large integers from computationally impractical to feasible, directly undermining the computational asymmetry that RSA and ECC depend on for their security. Every digital certificate, TLS handshake, code signature, and encrypted communication that relies on these algorithms becomes vulnerable. 

As Chris Hickman has stated, the day will come when quantum computers are powerful enough to render traditional encryption obsolete. The question is when cryptographically capable quantum computers will arrive, and whether your organization will be prepared. 

The “Harvest Now, Decrypt Later” Attack 

The quantum threat extends beyond future decryption capabilities, because adversaries are harvesting encrypted data with the intent to decrypt it once quantum systems mature. 

In a “harvest now, decrypt later” attack, adversaries capture encrypted data today, intercepting network traffic, exfiltrating encrypted databases, or copying stored communications. They do so with the intent to decrypt it once quantum computers are available. This attack does not require breaking into your systems in a conventional sense. It requires only the ability to capture encrypted traffic as it traverses networks. 

Hickman has been direct about this reality: we know data is being stolen now for decryption later. For data with long-term value – intellectual property, state secrets, personally identifiable information, health records, financial data – the threat window is already open. Any encrypted data captured today with a long sensitivity shelf life is already at risk. 

This is why acting on quantum readiness cannot wait for quantum computers to actually break encryption. The data you are protecting today must withstand threats that emerge years from now. 

Post-Quantum Cryptography Standards 

The global response to the quantum threat centers on post-quantum cryptography (PQC) – a new generation of cryptographic algorithms designed to resist both classical and quantum attacks. NIST has published its first PQC standards, including FIPS 203, 204, and 205, alongside SP 800-208, and more to be finalized in upcoming years. This establishes the algorithms that will replace current encryption schemes. 

PQC algorithms are mathematically different from RSA and ECC. They rely on lattice-based, hash-based, code-based and other mathematical problems that are believed to be resistant to quantum attack. The transition from current cryptography to PQC is complex and will take years, requiring changes to protocols, infrastructure, applications, and certificate management systems. 

Cryptographic Agility as a Strategic Imperative 

The PQC transition underscores a broader requirement: crypto agility, the ability to rapidly update cryptographic algorithms, protocols, and configurations across your infrastructure without rebuilding systems from scratch. Organizations that build cryptographic agility into their architectures now will be positioned to adopt PQC standards efficiently, and to adapt again when standards inevitably evolve. 

The PQC migration is not just a threat response. It is an opportunity to build a fundamentally more adaptable security architecture, one that can respond to future cryptographic challenges with agility rather than panic. 

Why Act Now: The Timeline 

Expert consensus indicates that cryptographically relevant quantum computers (i.e. systems capable of running Shor’s algorithm on production-grade encryption) have not yet been demonstrated, with estimates of when this threshold will be crossed varying significantly across research communities. Not only quantum computing capabilities keep increasing with developments in engineering, but also the requirements for solving cryptographic problems have been consistently decreasing.  

Full-scale quantum computer deployment has been limited to specialized government and research entities. But the threat model is not limited to who has a quantum computer, but rather, it includes who is harvesting data today for future decryption. 

Cryptographic migration is not a switch that can be flipped overnight. It requires years of planning, inventory, testing, validation, and phased deployment. Organizations that begin preparation now will have a manageable transition. Those that wait risk a compressed, high-risk migration under pressure. 

Organizational Readiness 

Preparing for the quantum era is an operational imperative that security leaders who own PKI, certificate management, and encryption infrastructure must drive. 

Inventory, Discovery, and Risk Prioritization 

The first step in quantum readiness is visibility. You cannot protect what you cannot see, and most organizations lack a comprehensive inventory of their cryptographic assets, the certificates, keys, algorithms, and protocols deployed across their infrastructure. 

Not all cryptographic assets face equal quantum risk. Data with long shelf life, systems with extended deployment timelines, and assets subject to regulatory requirements should be prioritized. A risk-based approach allows security leaders to focus finite resources on the highest-impact transitions first. 

Building a cryptographic asset inventory across cloud, CI/CD, and device environments is the essential foundation for every subsequent readiness action. 

Next Steps for Security Leaders 

Quantum readiness is a multi-year program, not a single project. The following actions establish the foundation for a successful transition: 

• Conduct comprehensive cryptographic asset discovery. 
  Identify every certificate, key, algorithm, and protocol across your infrastructure – including cloud workloads, CI/CD pipelines, IoT devices, and code-signing operations. 

• Assess your posture against quantum-resistant standards. 
  Evaluate which assets rely on algorithms vulnerable to quantum attack and which are already aligned with PQC recommendations. 

• Develop a prioritized PQC migration roadmap. 
  Sequence transitions based on data sensitivity, regulatory exposure, and operational risk. Long-lived data and externally facing systems warrant early attention. 

• Implement cryptographic lifecycle management. 
  Automate the discovery, issuance, renewal, and revocation of certificates and keys to reduce manual overhead and ensure continuous compliance. 

• Explore PQC through hands-on experimentation. 
  Test quantum-resistant algorithms in non-production environments to build organizational familiarity and identify integration challenges early. 

• Establish crypto agility as an architectural principle. 
  Design systems that can swap cryptographic algorithms without re-engineering applications or infrastructure. 

To benchmark your current position, assess your organization’s cryptographic agility readiness as a starting point for building your migration roadmap. 

How Keyfactor Supports Quantum-Safe Readiness 

Keyfactor’s platform gives security leaders the capabilities needed to prepare for the post-quantum era, from cryptographic discovery through PQC migration. 

Keyfactor AgileSec 

Keyfactor AgileSec is a cryptographic inventory platform that deploys sensors across code repositories, servers, endpoints, cloud environments, and network infrastructure to discover every certificate, key, and algorithm in use. Automated risk scoring identifies deprecated algorithms, insecure key sizes, and assets requiring PQC migration. AgileSec integrates with certificate lifecycle management, GRC, ITSM, and CMDB systems to operationalize cryptographic visibility across your security stack. 

Keyfactor Command 

Keyfactor Command provides certificate lifecycle management at scale, inventorying certificate authorities from multiple sources and enabling continuous detection, cataloging, and management of certificates across hybrid environments. Command gives security leaders the operational foundation to execute cryptographic transitions systematically. 

PQC Lab 

Keyfactor’s PQC Lab offers open-source toolkits, free trial environments, and educational resources that enable security teams to experiment with quantum-resistant algorithms in a hands-on setting. PQC Lab is designed to help organizations move from awareness to action, building practical readiness for the quantum transition.