arXiv subjects

Pol Julià Farré

Publications and source records attributed to Pol Julià Farré.

4 recordsLinked to original sources

arXiv2026

Secure authentication via Quantum Physical Unclonable Functions: a review

Quantum Physical Unclonable Functions (QPUFs) offer a physically grounded approach to secure authentication, extending the capabilities of classical PUFs. This review covers their theoretical foundations and key implementation challenges - such as quantum memories and Haar-randomness -, and distinguishes QPUFs from Quantum Readout PUFs (QR-PUFs), more experimentally accessible yet less robust against quantum-capable adversaries. A co-citation-based selection method is employed to trace the evolution of QPUF architectures, from early QR-PUFs to more recent Hybrid PUFs (HPUFs). This method further supports a discussion on the role of information-theoretic analysis in mitigating inconsistencies in QPUF responses, underscoring the deep connection between secret-key generation and authentication. Despite notable advances, achieving practical and robust QPUF-based authentication remains an open challenge.

quant-ph↗

arXiv2025

Entanglement-assisted authenticated BB84 protocol

In this work, we present a novel authenticated Quantum Key Distribution (QKD) protocol employing maximally entangled qubit pairs. In the absence of noise, we securely authenticate the well-known BB84 QKD scheme under two assumptions: first, adversaries cannot simultaneously access preshared and non-pre-shared secret classical information, and second, adversaries cannot simultaneously access pre-shared secret classical information and quantum memories held by legitimate parties. The main strength of this noiseless result is that access to all secretly pre-shared classical information is insufficient for breaching our scheme. Additionally, our protocol desirably allows for pre-shared secrecy reusage, leading to secret key growing. In order to address noise, we simulate a photonic implementation of our scheme, together with a storage model that aims to replicate the performance of cavity-enhanced Atomic- Frequency Comb (AFC) memories. Two methods are used to distinguish authentic entities from forgery attempts: on the one hand, a statistical approach is used after calibration of its defining parameter $μ$. Alternatively, a Deep Neural Network (DNN) is designed and trained to learn the underlying different structure of that input data coming from adversaries in comparison to that one coming from legitimate parties. Both methods achieve a correct classification rate larger than 0.80 for memory storage time of 150 $μ$s and a 1 km distance between parties.

quant-ph↗

arXiv2025

Secure Hybrid Key Growing via Coherence Witnessing and Bipartite Encoding

We propose a novel Hybrid Key Growing (HKG) protocol based on quantum principles and a classical physical-layer assumption. We simultaneously exploit the quantum photon-number and photon-time-bin Degrees of Freedom (DoFs), effectively doubling the bit-per-pulse rate compared to conventional Quantum Key Growing (QKG) schemes. Our protocol integrates entity authentication, and is designed for practical implementation by avoiding reliance on single-photon sources or detectors. By incorporating prior knowledge about the quantum channel, the scheme actively mitigates noise effects, making it suitable for real-world conditions. Under certain assumptions on experimental efficiencies, our approach also promises an increased key generation rate in bits per second. Our simulation results display, first, expected outcomes to gain assurance about the correctness of our implementation and, second, relevant dependencies that showcase desirable properties of our scheme in regimes of low photon loss and dephasing. In particular, within such regimes, our encoding scheme reduces the Quantum Bit Error Rate (QBER) while preserving the ability to detect eavesdropping and identity-forgery attempts.

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arXiv2024

Optimal depth and a novel approach to variational quantum process tomography

In this work, we present two new methods for Variational Quantum Circuit (VQC) Process Tomography onto $n$ qubits systems: PT_VQC and U-VQSVD. Compared to the state of the art, PT_VQC halves in each run the required amount of qubits for process tomography and decreases the required state initializations from $4^{n}$ to just $2^{n}$, all while ensuring high-fidelity reconstruction of the targeted unitary channel $U$. It is worth noting that, for a fixed reconstruction accuracy, PT_VQC achieves faster convergence per iteration step compared to Quantum Deep Neural Network (QDNN) and tensor network schemes. The novel U-VQSVD algorithm utilizes variational singular value decomposition to extract eigenvectors (up to a global phase) and their associated eigenvalues from an unknown unitary representing a general channel. We assess the performance of U-VQSVD by executing an attack on a non-unitary channel Quantum Physical Unclonable Function (QPUF). U-VQSVD outperforms an uninformed impersonation attack (using randomly generated input states) by a factor of 2 to 5, depending on the qubit dimension. For the two presented methods, we propose a new approach to calculate the complexity of the displayed VQC, based on what we denote as optimal depth.

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