Quantum key distribution and authentication: Separating facts from myths

Key exchange protocols and authentication mechanisms solve distinct problems and must be integrated in a secure communication system.

Quantum key distribution (QKD) is a technology that leverages the laws of quantum physics to securely share secret information between distant communicating parties. With QKD, quantum-mechanical properties ensure that if anyone tries to tamper with the secret-sharing process, the communicating parties will know. Keys established through QKD can then be used in traditional symmetric encryption or with other cryptographic technologies to secure communications.

“Record now, decrypt later" (RNDL) is a cybersecurity risk arising from advances in quantum computing. The term refers to the situation in which attackers record encrypted data today, even though they cannot decrypt it immediately. They store this data with the expectation that future quantum computers will be powerful enough to break the cryptographic algorithms currently securing it. Sensitive information such as financial records, healthcare data, or state secrets could be at risk, even years after it was transmitted.

Mitigating RNDL requires adopting quantum-resistant cryptographic methods, such as post-quantum cryptography (PQC) and/or quantum key distribution (QKD), to ensure confidentiality against future quantum advancements. AWS has invested in the migration to post-quantum cryptography to protect the confidentiality, integrity, and authenticity of customer data.

Quantum communication is important enough that in 2022, three of its pioneers won the Nobel Prize for physics. However, misconceptions about QKD’s role still persist. One of them is that QKD lacks practical value because it “doesn’t solve the authentication problem”. This view can obscure the broad benefits that QKD brings to secure communications when integrated properly into existing systems.

QKD should be viewed as a complement to — rather than a replacement for — existing cybersecurity frameworks. Functionally, QKD solves the same problem solved by other key establishment protocols, including the well-known Diffie-Hellman (DH) method and the module-lattice-based key encapsulation mechanism (ML-KEM), the standard recently ratified by the FIPS — but it does it in a fundamentally different way. Like those methods, QKD depends on strong authentication to defend against threats such as man-in-the-middle attacks, where an attacker poses as one of the communicating parties.

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In short, key exchange protocols and authentication mechanisms are different security primitives for solving distinct problems and must be integrated together in a secure communication system.

The challenge, then, is not to give QKD an authentication mechanism but to understand how it can be integrated with other established mechanisms to strengthen the overall security infrastructure. As quantum technologies continue to evolve, it’s important to shift the conversation from skepticism about authentication to consideration of how QKD can be thoughtfully and practically implemented to address today’s and tomorrow’s cybersecurity needs — such as the need to mitigating the “record now, decrypt later” (RNDL) attack (see sidebar).

Understanding the role of authentication in QKD

When discussing authentication in the context of QKD, we focus on the classical digital channel that the parties use to exchange information about their activities on the quantum channel. This isn’t about user authentication methods, such as logging in with passwords or biometrics, but rather about authenticating the communicating entities and the data exchanged. Entity authentication ensures that the parties are who they claim to be; data authentication guarantees that the information received is the same as what was sent by the claimed source. QKD protocols include a classical-communication component that uses both authentication methods to assure the overall security of the interaction.

Entity authentication

Entity authentication is the process by which one party (the "prover") asserts its identity, and another party (the "verifier") validates that assertion. This typically involves a registration step, in which the verifier obtains reliable identification information about the prover, as a prelude to any further authentication activity. The purpose of this step is to establish a “root of trust” or “trust anchor”, ensuring that the verifier has a trusted baseline for future authentications.

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Several entity authentication methods are in common use, each based on a different type of trust anchor:

  • Public-key-infrastructure (PKI) authentication: In this method, a prover’s certificate is issued by a trusted certificate authority (CA). The verifier relies on this CA, or the root CA in a certificate chain, to establish trust. The certificate acts as the trust anchor that links the prover’s identity to its public key.
  • PGP-/GPG-based (web of trust) authentication: Here, trust is decentralized. A prover’s public key is trusted if it has been vouched for by one or more trusted third parties, such as a mutual acquaintance or a public-key directory. These third parties serve as the trust anchors.
  • Pre-shared-key-based (PSK) authentication: In this case, both the prover and the verifier share a secret key that was exchanged via an offline or other secure out-of-band method. The trust anchor is the method of securely sharing this key a priori, such as a secure courier or another trusted channel.

These trust anchors form the technical backbones of all authentication systems. However, all entity authentication methods are based on a fundamental assumption: the prover is either the only party that holds the critical secret data (e.g., the prover’s private key in PKI or PGP) or the only other party that shares the secret with the verifier (PSK). If this assumption is broken — e.g., the prover's private key is stolen or compromised, or the PSK is leaked — the entire authentication process can fail.

Data authentication

Data authentication, also known as message authentication, ensures both the integrity and authenticity of the transmitted data. This means the data received by the verifier is exactly what the sender sent, and it came from a trusted source. As with entity authentication, the foundation of data authentication is the secure management of secret information shared by the communicating parties.

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The most common approach to data authentication is symmetric cryptography, where both parties share a secret key. A keyed message authentication code (MAC), such as HMAC or GMAC, is used to compute a unique tag for the transmitted data. This tag allows the receiver to verify that the data hasn’t been altered during transit. The security of this method depends on the collision resistance of the chosen MAC algorithm — that is, the computational infeasibility of finding two or more plaintexts that could yield the same tag — and the confidentiality of the shared key. The authentication tag ensures data integrity, while the secret key guarantees the authenticity of the data origin.

An alternative method uses asymmetric cryptography with digital signatures. In this approach, the sender generates a signature using a private key and the data itself. The receiver, or anyone else, can verify the signature’s authenticity using the sender’s public key. This method provides data integrity through the signature algorithm, and it assures data origin authenticity as long as only the sender holds the private key. In this case, the public key serves as a verifiable link to the sender, ensuring that the signature is valid.

In both the symmetric and the asymmetric approaches, successful data authentication depends on effective entity authentication. Without knowing and trusting the identity of the sender, the verification of the data’s authenticity is compromised. Therefore, the strength of data authentication is closely tied to the integrity of the underlying entity authentication process.

Authentication in QKD

The first quantum cryptography protocol, known as BB84, was developed by Bennett and Brassard in 1984. It remains foundational to many modern QKD technologies, although notable advancements have been made since then.

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QKD protocols are unique because they rely on the fundamental principles of quantum physics, which allow for “information-theoretic security.” This is distinct from the security provided by computational complexity. In the quantum model, any attempt to eavesdrop on the key exchange is detectable, providing a layer of security that classical cryptography cannot offer.

QKD relies on an authenticated classical communication channel to ensure the integrity of the data exchanged between parties, but it does not depend on the confidentiality of that classical channel. (This is why RNDL is not an effective attack against QKD). Authentication just guarantees that the entities establishing keys are legitimate, protecting against man-in-the-middle attacks.

Currently, several commercial QKD products are available, many of which implement the original BB84 protocol and its variants. These solutions offer secure key distribution in real-world applications, and they all pair with strong authentication processes to ensure the communication remains secure from start to finish. By integrating both technologies, organizations can build communication infrastructures capable of withstanding both classical and quantum threats.

Authentication in QKD bootstrap: A manageable issue

During the initial bootstrap phase of a QKD system, the authentic classical channel is established using traditional authentication methods based on PKI or PSK. As discussed earlier, all of these methods ultimately rely on the establishment of a trust anchor.

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While confidentiality may need to be maintained for an extended period (sometimes decades), authentication is a real-time process. It verifies identity claims and checks data integrity in the moment. Compromising an authentication mechanism at some future point will not affect past verifications. Once an authentication process is successfully completed, the opportunity for an adversary to tamper with it has passed. That is, even if, in the future, a specific authentication mechanism used in QKD is broken by a new technology, QKD keys generated prior to that point are still safe to use, because no adversary can go back in time to compromise past QKD key generation.

This means that the reliance on traditional, non-QKD authentication methods presents an attack opportunity only during the bootstrap phase, which typically lasts just a few minutes. Given that this phase is so short compared to the overall life cycle of a QKD deployment, the potential risks posed by using authentication mechanisms are relatively minor.

Authentication after QKD bootstrap: Not a new issue

Once the bootstrap phase is complete, the QKD devices will have securely established shared keys. These keys can then be used for PSK-based authentication in future communications. In essence, QKD systems can maintain the authenticated classical communication channel by utilizing a small portion of the very keys they generate, ensuring continued secure communication beyond the initial setup phase.

It is important to note that if one of the QKD devices is compromised locally for whatever reason, the entire system’s security could be at risk. However, this is not a unique vulnerability introduced by QKD. Any cryptographic system faces similar challenges when the integrity of an endpoint is compromised. In this respect, QKD is no more susceptible to it than any other cryptographic system.

Overcoming key challenges to QKD’s role in cybersecurity

Up to now we have focused on clarifying the myths about authentication needs in QKD. Next we will discuss several other challenges in using QKD in practice.

Bridging the gap between QKD theory and implementation

While QKD protocols are theoretically secure, there remains a significant gap between theory and real-world implementations. Unlike traditional cryptographic methods, which rely on well-understood algorithms that can be thoroughly reviewed and certified, QKD systems depend on specialized hardware. This introduces complexity, as the process of reviewing and certifying QKD hardware is not yet mature.

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In conventional cryptography, risks like side-channel attacks — which use runtime clues such as memory access patterns or data retrieval times to deduce secrets — are well understood and mitigated through certification processes. QKD systems are following a similar path. The European Telecommunications Standards Institute (ETSI) has made a significant move by introducing the Common Criteria Protection Profile for QKD, the first international effort to create a standardized certification framework for these systems. ISO/IEC has also published standards on security requirements and test and evaluation methods for QKD. These represent crucial steps in building the same level of trust that traditional cryptography enjoys.

Once the certification process is fully established, confidence in QKD’s hardware implementations will continue to grow, enabling the cybersecurity community to embrace QKD as a reliable, cutting-edge solution for secure communication. Until then, the focus remains on advancing the review and certification processes to ensure that these systems meet the highest security standards.

QKD deployment considerations

One of the key challenges in the practical deployment of QKD is securely transporting the keys generated by QKD devices to their intended users. While it’s accepted that QKD is a robust mechanism for distributing keys to the QKD devices themselves, it does not cover the secure delivery of keys from the QKD device to the end user (or key consumer).

QKD diagram.png
A schematic representation of two endpoints — site A and site B — that want to communicate safely. The top line represents the user traffic being protected, and the bottom lines are the channels required to establish secure communication. An important practical consideration is how to transmit a key between a QKD device and an end user within an endpoint.

This issue arises whether the QKD system is deployed within a large intranet or a small local-area network. In both cases, the keys must be transported over a non-QKD system. The standard deployment requirement is that the key delivery from the QKD system to the key consumer occurs “within the same secure site”, and the definition of a “secure site” is up to the system operator.

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The best practice is to make the boundary of the secure site as small as is practical. One extreme option is to remove the need for transporting keys over classical networks entirely, by putting the QKD device and the key user’s computing hardware in the same physical unit. This eliminates the need for traditional network protocols for key transport and realizes the full security benefits of QKD without external dependency. In cases where the extreme option is infeasible or impractical, the secure site should cover only the local QKD system and the intended key consumers.

Conclusion

QKD-generated keys will remain secure even when quantum computers emerge, and communications using these keys are not vulnerable to RNDL attacks. For QKD to reach its full potential, however, the community must collaborate closely with the broader cybersecurity ecosystem, particularly in areas like cryptography and governance, risk, and compliance (GRC). By integrating the insights and frameworks established in these fields, QKD can overcome its current challenges in trust and implementation.

This collective effort is essential to ensure that QKD becomes a reliable and integral part of secure communication systems. As these collaborations deepen, QKD will be well-positioned to enhance existing security frameworks, paving the way for its adoption across industries and applications.

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Do you want to build science solutions that shape how millions of customers experience AWS products? Do you want to influence AWS investment in Cloud and AI technology, and see your recommendations inform AWS leadership decisions and drive the growth of the AWS business? Do you want to push the boundaries of data science (e.g., statistical modeling, causal inference, econometrics, product growth analytics, and forecasting models) and help democratize how AWS senior leaders access analytics insights using an agentic analytics system? The AWS Analytics Engineering team is at the forefront of leveraging AI/ML technology and infrastructure to redefine how AWS product leaders and teams interact with and derive insights from their product and customer data. Our vision is to use data science methods to enable AWS product teams and business leaders to drive product and revenue growth and create personalized, optimized, and simplified product experiences to delight our customers. We are looking for a customer-focused Senior Data Scientist to develop and deliver science solutions for AWS services. In this role, you will build ML-driven product analytics for AWS Compute (EC2), GenAI & Agents, Database & Analytics, and Storage (S3). You will partner with product managers, business leaders, and senior stakeholders to translate complex business problems into scientific solutions that influence AWS's top line and bottom line. You will analyze product growth insights, understand product growth drivers, and identify business risks that need to be surfaced to leadership. As a Senior Data Scientist, you will analyze business trends and customer insights hands-on, own analyses end to end from problem definition through recommendation, and help raise the bar for scientific rigor on the team. You will mentor junior data scientists through code and methodology reviews and share best practices across the team. You will operate effectively in ambiguous environments, use sound business judgment, show high ownership and a deep understanding of the AWS business, and apply new data science methods at AWS scale. Key job responsibilities Set the technical direction for ML-driven product analytics across AWS Compute (EC2), GenAI & Agents, Database & Analytics, and Storage (S3) Deliver at scale ML-Driven, proactive product analytics for ALL AWS services - initiating and driving GM/VP level conversations Define, own and deliver Knowledge organization best practices for agentic data analytics across AWS Services Build agentic knowledge coverage mechanisms to ensure any service GM/VP/Finance/PM leaders in AWS are able to derive accurate and well covered agentic insights Drive the delivery of cross-service AWS insights, and influence GMs and VPs on product and process changes based on insights delivered. Partner with other science organizations like AWS Central Econ and science (ACES) and IDEAS Science & Economics to drive AWS wide agentic analytics boundaries across organizations and governance mechanisms Work across the AAE Science, Engineering, Core data engineering and service analytics organization to scale science and analytics to all AWS services
US, NY, New York
We are seeking an Applied Scientist to develop and optimize Visual Inertial Odometry (VIO) and sensor fusion systems for our intelligent robots. In this role, you will design, implement, and deploy state estimation and tracking algorithms that enable robots to understand their position and motion in real time, even in challenging and dynamic environments. You will own the full pipeline from algorithm development through embedded deployment, ensuring that perception systems run efficiently on resource-constrained robotic hardware. You will also leverage modern machine learning approaches to push the boundaries of classical perception methods, combining learned representations with geometric techniques to achieve robust, real-time performance. This is a deeply hands-on role. You will work directly with sensors, hardware, and real-world data, while prototyping, testing, and iterating in physical environments. The ideal candidate has strong foundations in VIO and sensor fusion, practical experience optimizing algorithms for embedded platforms, and familiarity with how modern deep learning is transforming perception. Key job responsibilities - Design and implement Visual Inertial Odometry algorithms for robust real-time state estimation on robotic platforms like Sprout - Develop multi-sensor fusion pipelines integrating cameras, IMUs, and other sensing modalities for accurate pose tracking - Optimize perception and tracking algorithms for deployment on embedded hardware (e.g., ARM, GPU-accelerated edge devices) under strict latency and power constraints - Apply modern ML-based perception techniques (learned features, depth estimation, neural odometry) to complement and improve classical geometric approaches - Build and maintain calibration, evaluation, and benchmarking infrastructure for perception systems - Collaborate with hardware, controls, and navigation teams to integrate perception outputs into the robot’s autonomy stack - Lead technical projects from research prototyping through production deployment
IN, KA, Bengaluru
We are looking for a talented Research Scientist to join our team. In this role, you will design, develop, and deploy machine learning and computer vision models that solve real-world problems at scale in the Amazon grocery domain. You will work closely with engineering, product, and business teams to turn complex technical challenges into production-ready solutions, and own the model development lifecycle from experimentation through deployment. You will bring scientific rigor to every stage — from data analysis and model design to evaluation and iteration. This is a high-impact role where your models will directly improve the shopping experience for millions of customers in Amazon grocery stores. Key job responsibilities Design, train, and evaluate computer vision and machine learning models for complex grocery-domain problems including product identification, shelf perception, and in-store scene understanding — iterating rapidly from prototype to production-quality solutions Conduct rigorous exploratory data analysis to characterize domain-specific challenges (image variability, catalog gaps, label noise) and translate findings into actionable modeling decisions Design and execute offline and online evaluation frameworks — defining metrics that capture both model performance and downstream business impact, and diagnosing failure modes to prioritize improvements Build and improve data pipelines and annotation workflows that feed model training, including active learning strategies to maximize label efficiency Communicate technical results, trade-offs, and recommendations clearly to engineering, product, and business stakeholders — connecting model behavior to customer experience outcomes Stay current with state-of-the-art research in computer vision, multimodal learning, and representation learning — evaluating and adapting promising techniques to team-specific problems Contribute to a culture of scientific rigor through reproducible experimentation, thorough documentation, peer code and design reviews, and raising the quality bar for the team A day in the life As a Research Scientist on the GRAISE team, you'll spend your days analyzing model performance from overnight experiments, and prototyping new approaches using multimodal learning with store video and sensor data. You'll present findings to product and business stakeholders, translating technical results into actionable recommendations. Throughout the day, you'll balance rigorous scientific thinking with practical engineering constraints, knowing your work directly improves the shopping experience for millions of customers in Amazon grocery stores. About the team The GRAISE team (Grocery, Retail & In-Store Experience) within World Wide Grocery Store Tech (WWGST) builds foundational AI and machine learning systems that power Amazon's in-store grocery technologies. We develop domain-specific models that solve uniquely complex challenges in grocery — from smart shopping carts and inventory intelligence to personalization and store operations. Our mission is to create technology which makes grocery shopping more convenient, economical, personalized, and enjoyable for customers while empowering retailers with operational efficiency.
US, NY, New York
The Ads Measurement Science team in the Measurement, Ad Tech, and Data Science (MADS) team of Amazon Ads serves a centralized role developing solutions for a multitude of performance measurement products. We create solutions which measure the comprehensive impact of advertiser's ad spend, including sales impacts both online and offline and across timescales, and provide actionable insights that enable our advertisers to optimize their media portfolios. We also own the science solutions for AI tools that unlock new insights and automate high-effort customer workflows, such as custom query and report generation based on natural language user requests. We leverage a host of scientific technologies to accomplish this mission, including Generative AI, classical ML, Causal Inference, Natural Language Processing, and Computer Vision. As an Applied Scientist on the team, you will lead measurement solutions end-to-end from inception to production. You will propose, design, analyze, and productionize models to provide novel measurement insights to our customers. Key job responsibilities Leverage deep expertise in one or more scientific disciplines to invent solutions to ambiguous ads measurement problems Disambiguate problems to propose clear evaluation frameworks and success criteria Work autonomously and write high quality technical documents Implement a significant portion of critical-path code, and partner with engineers to directly carry solutions into production Partner closely with other scientists to deliver large, multi-faceted technical projects Share and publish works with the broader scientific community through meetings and conferences Communicate clearly to both technical and non-technical audiences Contribute new ideas that shape the direction of the team's work Mentor more junior scientists and participate in the hiring process About the team We are a team of scientists across Applied, Research, Data Science and Economist disciplines. You will work with colleagues with deep expertise in ML, NLP, CV, Gen AI, and Causal Inference with a diverse range of backgrounds. We partner closely with top-notch engineers, product managers, sales leaders, and other scientists with expertise in the ads industry and on building scalable modeling and software solutions.
US, CA, Sunnyvale
Robots are about to step out of the movies and into our homes. If you're excited about this robotics revolution, come join Amazon's Fauna Robotics. As an Applied Scientist in this org, you'll push the state-of-the-art techniques in Robotics. You'll drive technical excellence in areas such as perception, manipulation, sim2real transfer, reinforcement learning and multi-task learning, designing novel algorithms that bridge the gap between research and real-world deployment. In this role, you will integrate hands-on technical expertise with scientific leadership, ensuring your team delivers robust solutions for dynamic real-world environments. While staying updated with the latest advancements in robotics research, you will leverage your experience and expertise to guide the team towards the most promising direction. Collaborating with a talented group of software engineers and scientists, you will bring your ideas to fruition. Deploying robots in home environments requires you to anticipate challenges not encountered in structured settings like warehouses. Additionally, consumer-grade sensors and actuators necessitate working around their limitations, requiring out-of-the-box innovative ideas. As such, you are someone who truly enjoys thinking about problems using first principles at a holistic and systems level to address the aforementioned challenges. Key job responsibilities - Lead technical initiatives in areas such as robotics foundation models, reinforcement learning and manipulation - - Design experiments to identify the limitations of current state-of-the-art models and developing new models or techniques that can surpass these models - Design and implement novel deep learning architectures that push the boundaries of what robots can understand and accomplish - Mentor fellow scientists while maintaining strong individual technical contributions - Collaborate with engineering teams to optimize and scale models for real-world applications - Influence technical decisions and implementation strategies within your area of focus - Experienced in communicating complex technical work to a non-technical audience - The ability to work with minimal guidance, be proactive and to handle ambiguity and the challenge of quickly evolving goals About the team Fauna Robotics, an Amazon company, is building capable, safe, and genuinely delightful robots for everyday life. Our goal is simple: make robots people actually want to live and interact with in everyday human spaces. We believe that future won’t arrive until building for robotics becomes far more accessible. Today, too much effort is spent reinventing the fundamentals. We’re changing that by developing tightly integrated hardware and software systems that make it faster, safer, and more intuitive to create real-world robotic products. Our work spans the full stack: mechanical design, control systems, dynamic modeling, and intelligent software. The focus is not just functionality, but experience. We’re building robots that feel responsive, expressive, and genuinely useful. At Fauna, you’ll work at the frontier of this space, helping define how robots move, manipulate, and interact with people in natural environments. It’s an opportunity to solve hard problems across hardware and software with a team focused on making robotics accessible and joyful to build. If you care about making robotics real for everyone and building systems that are as delightful as they are capable, we’re interested in hearing from you.