The secure transmission of information relies on robust hashing algorithms, and researchers are continually seeking methods that offer increased security and efficiency. Mohana Priya Thinesh Kumar and Pranavishvar Hariprakash, from the Department of Physics at the Indian Institute of Technology (Indian School of Mines), Dhanbad, now present a new approach to quantum hashing that leverages the unique properties of graphs and quantum dynamics. Their work introduces a novel spectral hashing algorithm, which generates highly distinctive fingerprints from input messages by mapping them to graphs and analysing their spectral characteristics using quantum phase estimation. This method distinguishes between even subtly different inputs, offering a structurally rich and sensitive hash, and represents a significant step towards more secure and reliable data transmission in a quantum future.
Message-Induced Graph Hashing via Phase Estimation
Scientists developed a novel spectral hashing algorithm, termed Graph Hash (QGH-256), which generates high-entropy fingerprints from message-induced graphs. The work centers on mapping each input message to a weighted graph constructed on a 4×4 toroidal grid, where the direction of a discrete random walk is dictated by the message itself. This process involves converting the message into a UTF-8 binary representation and dividing it into two-bit blocks, which then determine the walker’s movement at each step on the grid, ensuring a unique weighted map is assigned to every input message. The algorithm utilizes Phase Estimation (QPE) to extract the phase spectrum of the graph Laplacian, a crucial step in generating the spectral fingerprint.
Notably, QPE was performed with respect to a uniform superposition state across all node basis states, ensuring that all eigencomponents contribute to the resulting spectrum and distinguish even co-spectral graphs. The resulting spectral fingerprint is then converted into a compact 256-bit digest, providing a structurally rich representation of the input message, and tests demonstrate that this hash exhibits strong sensitivity to input perturbations. The team achieved this implementation within the Qiskit framework, utilizing a seeded statevector simulator to obtain stable and noise-free results, delivering a foundation for post-quantum hashing and addressing vulnerabilities in existing cryptographic standards threatened by quantum computing advancements.
Quantum Hashing via Spectral Fingerprinting and QPE
This work introduces a novel quantum hashing framework, termed QGH-256, which combines a classical discrete walker with quantum spectral fingerprinting to generate compact, high-entropy hashes. The method begins by mapping input messages to weighted graphs via a classical random walk, then utilizes Quantum Phase Estimation (QPE) on the graph Laplacian to extract spectral features. A key innovation lies in performing QPE with a superposition state, ensuring that contributions from all eigenvectors are included in the resulting spectrum, thereby distinguishing even structurally similar graphs and minimizing collisions. The researchers demonstrated the feasibility of QGH-256 using a seeded statevector simulator, achieving deterministic and reproducible results, and confirming the potential of this method for secure communication and blockchain systems, satisfying objectives outlined by NIST Post-Quantum Cryptography standards. Future work will focus on optimizing the classical walker component to reduce preprocessing time and evaluating performance under realistic conditions with more advanced quantum hardware, establishing a promising foundation for quantum-resistant hashing techniques and contributing to the ongoing development of post-quantum cryptography.