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arXiv:2108.11015

Quantum Algorithms for Variants of Average-Case Lattice Problems via Filtering

Yuehua Chen, Qipeng Liu, Mark Zhandry

Published Aug 25, 2021• 5 citations
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Abstract

We show polynomial-time quantum algorithms for the following problems: (*) Short integer solution (SIS) problem under the infinity norm, where the public matrix is very wide, the modulus is a polynomially large prime, and the bound of infinity norm is set to be half of the modulus minus a constant. (*) Learning with errors (LWE) problem given LWE-like quantum states with polynomially large moduli and certain error distributions, including bounded uniform distributions and Laplace distributions. (*) Extrapolated dihedral coset problem (EDCP) with certain parameters. The SIS, LWE, and EDCP problems in their standard forms are as hard as solving lattice problems in the worst case. However, the variants that we can solve are not in the parameter regimes known to be as hard as solving worst-case lattice problems. Still, no classical or quantum polynomial-time algorithms were known for the variants of SIS and LWE we consider. For EDCP, our quantum algorithm slightly extends the result of Ivanyos et al. (2018). Our algorithms for variants of SIS and EDCP use the existing quantum reductions from those problems to LWE, or more precisely, to the problem of solving LWE given LWE-like quantum states. Our main contribution is solving LWE given LWE-like quantum states with interesting parameters using a filtering technique.

DOI
10.48550/arxiv.2108.11015

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We show polynomial-time quantum algorithms for the following problems: () Short integer solution (SIS) problem under the infinity norm, where the public matrix is very wide, the modulus is a polynomially large prime, and the bound of infinity norm is set to be half of the modulus minus a constant. () Learning with errors (LWE) problem given LWE-like quantum states with polynomially large moduli and certain error distributions, including bounded uniform distributions and Laplace distributions. (*) Extrapolated dihedral coset problem (EDCP) with certain parameters. The SIS, LWE, and EDCP problems in their standard forms are as hard as solving lattice problems in the worst case. However, the variants that we can solve are not in the parameter regimes known to be as hard as solving worst-case lattice problems. Still, no classical or quantum polynomial-time algorithms were known for the variants of SIS and LWE we consider. For EDCP, our quantum algorithm slightly extends the result of Ivanyos et al. (2018). Our algorithms for variants of SIS and EDCP use the existing quantum reductions from those problems to LWE, or more precisely, to the problem of solving LWE given LWE-like quantum states. Our main contribution is solving LWE given LWE-like quantum states with interesting parameters using a filtering technique.
We show polynomial-time quantum algorithms for the following problems: (*) Short integer solution (SIS) problem under the infinity norm, where the public matrix is very wide, the modulus is a polynomially large prime, and the bound of infinity norm is set to be half of the modulus minus a constant. (*) Extrapolated dihedral coset problem (EDCP) with certain parameters. (*) Learning with errors (LWE) problem given LWE-like quantum states with polynomially large moduli and certain error distributions, including bounded uniform distributions and Laplace distributions. The SIS, EDCP, and LWE problems in their standard forms are as hard as solving lattice problems in the worst case. However, the variants that we can solve are not in the parameter regimes known to be as hard as solving worst-case lattice problems. Still, no classical or quantum polynomial-time algorithms were known for those variants. Our algorithms for variants of SIS and EDCP use the existing quantum reductions from those problems to LWE, or more precisely, to the problem of solving LWE given LWE-like quantum states. Our main contributions are introducing a filtering technique and solving LWE given LWE-like quantum states with interesting parameters.
The Learning With Errors (LWE) problem is a fundamental assumption in post-quantum cryptography. Regev established a quantum reduction from LWE to the Dihedral Coset Problem (DCP). Later, Brakerski et al. introduced the Extrapolated Dihedral Coset Problem (EDCP), proving its equivalence to LWE. However, unlike DCP, EDCP no longer admits a coset structure. This limits the direct application of techniques for hidden subgroup problems. In this work, we introduce the Cyclotomic Coset Problem (CCP), a cyclotomic generalization of DCP that preserves an exact hidden-subgroup structure. Let ζpζ_pζp​ be a primitive ppp-th root of unity, let π=ζp−1π=ζ_p-1π=ζp​−1, and write q=ptq=p^tq=pt and L=t(p−1)L=t(p-1)L=t(p−1). We work over Rq=Zq[ζp]≅Z[ζp]/(πL)R_q=\mathbb Z_q[ζ_p] \cong \mathbb Z[ζ_p]/(π^L)Rq​=Zq​[ζp​]≅Z[ζp​]/(πL), where the isomorphism follows from the total ramification identity (p)=(π)p−1(p)=(π)^{p-1}(p)=(π)p−1. We exploit the resulting πππ-adic ideal chain to construct a quantum sieve that successively reduces phase states modulo πL,πL−1,…,ππ^{L},π^{L-1},\ldots,ππL,πL−1,…,π. For every fixed prime ppp and modulus q=ptq=p^tq=pt, our algorithm solves the CCP in time and sample complexity 2Op(log⁡nlog⁡q)2^{O_p(\log n\log q)}2Op​(lognlogq), using polynomial quantum space. The sieve also applies to uniform EDCP and Gaussian S|LWE>, yielding quasi-polynomial time algorithms for all the above problems when q=poly(n)q=\text{poly}(n)q=poly(n). This extends the power-of-two EDCP sieve of Bai et al. (CRYPTO 2025) to a cyclotomic setting. However, we emphasize that our result does not, by itself, yield a quasi-polynomial-time algorithm for standard LWE, because the currently known reduction produces only a limited number of approximate CCP states.
Our main result is a quantum public-key encryption scheme based on the Extrapolated Dihedral Coset problem (EDCP) which is equivalent, under quantum polynomial-time reductions, to the Learning With Errors (LWE) problem. For limited number of public keys (roughly linear in the security parameter), the proposed scheme is information-theoretically secure. For polynomial number of public keys, breaking the scheme is as hard as solving the LWE problem. The public keys in our scheme are quantum states of size O~(n)\tilde{O}(n)O~(n) qubits. The key generation and decryption algorithms require O~(n)\tilde{O}(n)O~(n) qubit operations while the encryption algorithm takes O(1)O(1)O(1) qubit operations.
This thesis lies within the framework of post-quantum cryptography, whose goal is to conceive cryptosystems that resist classical and quantum attacks. Our focus here is on the Decoding Problem (DP) and the Short Codeword Problem (SCP) in code-based cryptography. We study here how quantum algorithms can exploit the structure of these problems. In a first part, we generalized the information-theoretic achievability threshold for the Quantum Decoding Problem (QDP) to all memoryless noise models. By combining this result with Regev's reduction, we obtain a quantum algorithm that samples dual codewords whose weight approaches the minimum at the achievability threshold. In a second part, we formulate a general framework for adapting to coding theory the lattice reduction from Learning With Errors (LWE) to the Extrapolated Dihedral Coset Problem (EDCP). We study this framework in the binary Hamming case and show that the resulting parameter regime can already be decoded classically in polynomial time.
In the paper, we study lattice-based cryptographic problems, in particular Learning With Errors (LWE) and Short Integer Solution (SIS) lattice problems, which are considered to be known cryptographic primitives that are supposed to be secure against both classical and quantum attacks. We formulated the LWE and SIS problems as Mixed-Integer Programming (MIP) model and then converted them to Quadratic Unconstrained Binary Optimization (QUBO) problem, which can be solved by using a quantum annealer. Quantum annealing searches for the global minimum of an input objective function subjected to the given constraints to optimize the given model. We have estimated the q-bits required for the Quantum Processing Unit (QPU). Our results show that this approach can solve certain instances of the LWE and SIS problems efficiently.
Chen, Liu, and Zhandry [CLZ22] introduced the problems S|LWE⟩ and C|LWE⟩ as quantum analogues of the Learning with Errors problem, designed to construct quantum algorithms for the Inhomogeneous Short Integer Solution (ISIS) problem. Several later works have used this framework for constructing new quantum algorithms in specific cases. However, the general relation between all these problems is still unknown. In this paper, we investigate the equivalence between S|LWE⟩ and ISIS. We present the first fully generic reduction from ISIS to S|LWE⟩, valid even in the presence of errors in the underlying algorithms. We then explore the reverse direction, introducing an inhomogeneous variant of C|LWE⟩, denoted IC|LWE⟩, and show that IC|LWE⟩ reduces to S|LWE⟩. Finally, we prove that, under certain recoverability conditions, an algorithm for ISIS can be transformed into one for S|LWE⟩. We instantiate this reverse reduction by tweaking a known algorithm for (I)SIS∞ in order to construct quantum algorithm for S|LWE⟩ when the alphabet size q is a small power of 2, recovering some results of Bai et al. [BJK + 25]. Our results thus clarify the landscape of reductions between S|LWE⟩ and ISIS, and we show both their strong connection as well as the remaining barriers for showing full equivalence.