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Jia-Cheng Fan

Publications and source records attributed to Jia-Cheng Fan.

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Offline dedicated quantum attacks on block cipher constructions based on two parallel permutation-based pseudorandom functions

Quantum cryptanalysis is essential for evaluating the security of cryptographic systems against the threat of quantum computing. Recently, Shi {\it et al.} introduced a dedicated quantum attack on block cipher constructions based on XOR-type functions, which greatly reduces the required resources (including circuit depth, width, and the number of gates) compared to the parallel Grover-meets-Simon algorithm. Here, our contribution is in two aspects. On the one hand, we discover new cryptographic structures amenable to this attack: PolyMAC and constructions based on two parallel permutation-based pseudorandom functions (TPP-PRFs), including XopEM, SoEM22, SUMPIP, and DS-SoEM, thereby answering Shi {\it et al.}' s open question. On the other hand, for constructions based on TPP-PRFs, we break the obstacle that this attack relies on online query by constructing decoupled XOR-type functions, then propose an offline quantum attack on them. Compared to previous results, our offline attack exhibits significantly reduced query complexity. Specifically, the number of queries to the encryption oracle is reduced from $O(2^{(n+t)/2}\cdot (n-t))$ to $O(2^{t}\cdot (n-t))$ in the quantum query model, where $0<t<n$, $t$ is a truncation parameter, and $n$ is the input length of constructions. Further, we enable its implementation in the classical query model, optimizing both the classical query complexity and time complexity from $\tilde O(2^{2n/3})$ to $\tilde O(2^{(2n-t)/3})$ for $0<t \leq n/2$.

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Quantum-Assisted Recursive Algorithm for Solving the Exact Cover Problem

The exact cover problem is an NP-complete problem with broad applications. Studies show that although applying the Quantum Approximate Optimization Algorithm (QAOA) to this problem can yield improved solution quality with deeper circuit depth, it can limit the algorithm's applicability on noisy intermediate-scale quantum devices. To improve solution quality at shallow depth, we propose a Quantum-Assisted Recursive Algorithm (QARA) for solving the exact cover problem. QARA addresses the problem by alternately applying classical and quantum pruning. Classical pruning is a repeatable pre-processing step to simplify the problem. When the classical pruning cannot promote the problem simplification, quantum pruning is invoked. During quantum pruning, QARA extracts information from the QAOA's output state to identify the subset with the strongest selection bias. This subset is then used to prune the problem based on our problem-tailored reduction rules. Furthermore, QARA incorporates a local verification and rollback mechanism to assistively judge the effectiveness of the quantum simplification. After quantum pruning, classical pruning is applied again to the reduced problem if the remaining subsets and element set are not null. This alternating process repeats until the original problem is fully resolved. In our numerical simulations, we evaluate the performance of QARA at one-layer depth on 140 instances with subset sizes ranging from 8 to 20. Numerical results show that the probability of QARA in finding an exact solution is approximately 60\% higher than that of both QAOA and Recursive QAOA, highlighting its efficiency.

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Iterative Partition Search Variational Quantum Algorithm for Solving Shortest Vector Problem

The Partition Search Algorithm (PSA) and Iterative Quantum Optimization with an Adaptive Problem (IQOAP) are leading variational quantum algorithms for solving Shortest Vector Problem (SVP). However, each has limitations that restrict its practical impact. IQOAP suffers from ineffective iterations that fail to update the lattice basis, whereas PSA's static partitioning leads to oversized search spaces. In this work, we propose the Iterative Partition Search Algorithm (IPSA), which systematically addresses these drawbacks by integrating a "1-tailed search spaces" with a dynamic, stack-managed iterative process. Specifically, the "1-tailed" strategy ensures that every successful execution yields an effective lattice basis update, thereby eliminating the ineffective iterations associated with IQOAP. Concurrently, the dynamic iterative process reduces the required qubit count, thereby avoiding the limitation of an oversized search space inherent to PSA. We validate IPSA on the Baihua superconducting quantum processor via the Quafu platform. Small-scale real hardware experiments demonstrate that, compared to PSA, IPSA achieves a 14-fold increase in success rate at a cost of less than double the total circuit depth. Conversely, compared to IQOAP, IPSA reduces the total circuit depth by 82.7% while achieving approximately 2.5 times its success rate. Furthermore, we also conduct numerical simulations whose results are in good agreement with the experimental findings and extend our analysis.

quant-ph