Application to Physical Systems

Hermann Haken

Abdullah Al Mamun, Akid Abrar, Mizanur Rahman +2 more

The rise of quantum computing threatens traditional cryptographic algorithms that secure Transportation Cyber-Physical Systems (TCPS). Shor's algorithm poses a significant threat to RSA and ECC, while Grover's algorithm reduces the security of symmetric encryption schemes, such as AES. The objective of this paper is to underscore the urgency of transitioning to post-quantum cryptography (PQC) to mitigate these risks in TCPS by analyzing the vulnerabilities of traditional cryptographic schemes and the applicability of standardized PQC schemes in TCPS. We analyzed vulnerabilities in traditional cryptography against quantum attacks and reviewed the applicability of NIST-standardized PQC schemes, including CRYSTALS-Kyber, CRYSTALS-Dilithium, and SPHINCS+, in TCPS. We conducted a case study to analyze the vulnerabilities of a TCPS application from the Architecture Reference for Cooperative and Intelligent Transportation (ARC-IT) service package, i.e., Electronic Toll Collection, leveraging the Microsoft Threat Modeling tool. This case study highlights the cryptographic vulnerabilities of a TCPS application and presents how PQC can effectively counter these threats. Additionally, we evaluated CRYSTALS-Kyber's performance across wired and wireless TCPS data communication scenarios. While CRYSTALS-Kyber proves effective in securing TCPS applications over high-bandwidth, low-latency Ethernet networks, our analysis highlights challenges in meeting the stringent latency requirements of safety-critical wireless applications within TCPS. Future research should focus on developing lightweight PQC solutions and hybrid schemes that integrate traditional and PQC algorithms, to enhance compatibility, scalability, and real-time performance, ensuring robust protection against emerging quantum threats in TCPS.

Hermann Haken

Fangjun Hu, Gerasimos Angelatos, Saeed A. Khan +6 more

The expressive capacity of physical systems employed for learning is limited by the unavoidable presence of noise in their extracted outputs.Though present in physical systems across both the classical and quantum regimes, the precise impact of noise on learning remains poorly understood.Focusing on supervised learning, we present a mathematical framework for evaluating the resolvable expressive capacity (REC) of general physical systems under finite sampling noise and provide a methodology for extracting its extrema, the eigentasks.Eigentasks are a native set of functions that a given physical system can approximate with minimal error.We show that the REC of a quantum system is limited by the fundamental theory of quantum measurement and obtain a tight upper bound for the REC of any finitely sampled physical system.We then provide empirical evidence that extracting low-noise eigentasks can lead to improved performance for machine learning tasks such as classification, displaying robustness to overfitting.We present analyses suggesting that correlations in the measured quantum system enhance learning capacity by reducing noise in eigentasks.The applicability of these results in practice is demonstrated with experiments on superconducting quantum processors.Our findings have broad implications for quantum machine learning and sensing applications.

Walter E Thirring

Ji Guan, Yuan Feng, Andrea Turrini +1 more

Model checking has been successfully applied to verification of computer hardware and software, communication systems and even biological systems. In this paper, we further push the boundary of its applications and show that it can be adapted for applications in quantum physics. More explicitly, we show how quantum statistical and many-body systems can be modeled as quantum Markov chains, and some of their properties that interest physicists can be specified in linear-time temporal logics. Then we present an efficient algorithm to check these properties. A few case studies are given to demonstrate the use of our algorithm to actual quantum physical problems.