Post-quantum cryptography is the development of cryptographic primitives that run on conventional hardware but are mathematically structured to resist Shor's algorithm and other quantum attacks. Unlike quantum key distribution, which relies on physical properties, PQC replaces vulnerable RSA and Elliptic Curve Cryptography with hardness assumptions from lattice-based, code-based, and multivariate problems that remain intractable for quantum adversaries.
Glossary
Post-Quantum Cryptography

What is Post-Quantum Cryptography?
Post-quantum cryptography (PQC) refers to cryptographic algorithms designed to secure data against attacks from both classical and cryptographically relevant large-scale quantum computers.
The NIST Post-Quantum Cryptography Standardization process has selected algorithms like CRYSTALS-Kyber for key encapsulation and CRYSTALS-Dilithium for digital signatures. Migration requires integrating these quantum-resistant primitives into existing protocols like TLS and homomorphic encryption frameworks, ensuring cryptographic agility to swap algorithms before large-scale quantum decryption becomes feasible.
Key Characteristics of PQC
Post-Quantum Cryptography (PQC) refers to cryptographic algorithms designed to run on classical computers while resisting cryptanalytic attacks from both classical and large-scale quantum adversaries. These schemes are not merely theoretical; they are being standardized by NIST to replace RSA and ECC before a Cryptographically Relevant Quantum Computer (CRQC) arrives.
Frequently Asked Questions
Clear, technically precise answers to the most common questions about cryptographic algorithms designed to resist attacks from large-scale quantum computers.
Post-quantum cryptography (PQC) refers to cryptographic algorithms designed to be secure against cryptanalytic attacks by both classical and large-scale quantum computers. Unlike current public-key systems such as RSA and Elliptic Curve Cryptography (ECC)—which rely on the hardness of integer factorization and discrete logarithms that Shor's algorithm can efficiently solve—PQC schemes are built on mathematical problems believed to be intractable even for quantum adversaries. The primary families include lattice-based cryptography (relying on problems like Learning With Errors), code-based cryptography (using error-correcting codes), multivariate cryptography (solving systems of polynomial equations), hash-based signatures (leveraging the security of hash functions), and isogeny-based cryptography (operating on maps between elliptic curves). These schemes replace vulnerable primitives in existing protocols, ensuring long-term confidentiality and authentication.
PQC vs. Classical Cryptography
A comparative analysis of classical public-key cryptosystems against post-quantum alternatives across security, performance, and deployment dimensions.
| Feature | RSA/ECC (Classical) | Lattice-Based (PQC) | Hash-Based (PQC) |
|---|---|---|---|
Security Basis | Integer factorization / Discrete logarithm | Learning With Errors (LWE) / Shortest Vector Problem | Preimage resistance of cryptographic hash functions |
Quantum Vulnerability | |||
Public Key Size (Typical) | 256 bytes (ECC) – 512 bytes (RSA-3072) | 800 bytes (Kyber-768) – 1.5 KB (Dilithium) | 32 bytes (SPHINCS+ public key) |
Signature Size (Typical) | 64 bytes (Ed25519) – 384 bytes (RSA-3072) | 2.4 KB (Dilithium2) – 4.6 KB (Dilithium5) | 8 KB (SPHINCS+-128s) – 50 KB (SPHINCS+-256f) |
Encryption Speed (Relative) | Fast (hardware-accelerated) | Comparable to ECC (sub-millisecond ops) | Not applicable (signature-only scheme) |
Maturity and Standardization | Decades of deployment; NIST SP 800-56A | NIST FIPS 203/204 (2024); growing adoption | NIST FIPS 205 (2024); conservative security choice |
Side-Channel Resistance | Requires constant-time implementations | Requires masking and constant-time implementations | Inherently resistant to certain timing attacks |
State Management Required |
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Related Terms
Post-quantum cryptography does not exist in isolation. It is a critical component of a broader privacy-preserving computation stack, often hybridized with homomorphic encryption and multi-party computation to secure AI infrastructure against both classical and quantum adversaries.
Code-Based Cryptography
A post-quantum approach relying on the hardness of decoding random linear error-correcting codes. The McEliece cryptosystem, proposed in 1978, remains unbroken against both classical and quantum attacks, offering the longest track record of any PQC candidate.
- Classic McEliece selected by NIST as a conservative KEM alternative
- Public keys are large (hundreds of kilobytes), but ciphertexts are compact
- Security reduces to the syndrome decoding problem, proven NP-hard
Harvest-Now-Decrypt-Later Mitigation
The primary threat model driving urgent PQC migration. Adversaries passively record encrypted traffic today, storing ciphertexts until a cryptographically relevant quantum computer (CRQC) becomes available to retroactively break the captured key exchanges.
- Any data requiring confidentiality beyond a 10-15 year horizon is at immediate risk
- Sovereign AI infrastructure processing classified or proprietary model weights must implement quantum-safe hybrid key exchange now
- Circuit bootstrapping within TFHE pipelines must itself be parameterized with post-quantum secure LWE dimensions
Multivariate Cryptography
A post-quantum family based on the difficulty of solving systems of multivariate quadratic equations over finite fields—an NP-hard problem. Primarily suited for digital signatures rather than encryption or key encapsulation.
- Rainbow (third-round finalist) was broken during NIST evaluation, highlighting the active cryptanalysis landscape
- Remaining multivariate schemes offer extremely short signatures and fast verification
- Useful in resource-constrained embedded environments within sovereign hardware roots of trust

About the author
Prasad Kumkar
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
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