arXiv subjects

M. Isabel Franco Garrido

Publications and source records attributed to M. Isabel Franco Garrido.

3 recordsLinked to original sources

arXiv2026

Gibbs state preparation through quantum decoding

We propose a Gibbs state preparation algorithm that exploits algebraic structure in the Hamiltonian. Our starting point is Hamiltonian Decoded Quantum Interferometry (HDQI), a recent algorithm that reduces Gibbs state preparation for Pauli Hamiltonians to decoding error-correcting codes. We introduce quantum decoding HDQI (QD-HDQI), replacing HDQI's coherent classical decoder by a quantum decoder that can exploit interference between different errors. For commuting Pauli Hamiltonians, our reduction is efficient and the associated quantum decoding problem corresponds to transmitting the associated symplectic code over i.i.d. pure-state classical-quantum channels. For uniformly random Hamiltonian signs, the vanishing-error threshold of an efficient decoder directly determines the lowest temperature that our algorithm can reach. Even using only classical decoders, this improves upon HDQI's achievable inverse temperature from linear to square-root in the correctable error fraction; quantum decoders can improve further by tolerating still stronger noise. A similar picture extends to the noncommuting regime previously handled by HDQI, where now the noise is correlated within small, independent blocks. We study two such quantum decoders in our QD-HDQI Gibbs sampling framework. The first, based on unambiguous state discrimination, works efficiently for arbitrary binary linear codes. However, we "dequantize" this strategy for commuting Hamiltonians, contributing a classical rejection sampler that matches its temperature threshold. The second is belief propagation with quantum messages (BPQM), a quantum generalization of classical belief propagation. We show that for certain dilute spin glasses, BPQM's decoding threshold exceeds the Shannon limit for classical decoding, reaching temperatures inaccessible to any classical decoder within our framework.

quant-ph↗

arXiv2026

Regev's reduction as a candidate quantum algorithm for the discrete logarithm problem in finite abelian groups

In this article, we investigate whether combining Regev's reduction with the Cheng-Wan reduction can yield an alternative quantum algorithm for the discrete logarithm problem (DLOG) in finite abelian groups. Cheng and Wan reduce DLOG over finite fields to bounded-distance decoding of low-rate Reed-Solomon codes at a large decoding radius. Regev's reduction, in turn, exploits a decoder for the high-rate dual code with a lower noise level to obtain a quantum algorithm for decoding the original low-rate code at a larger radius. The Cheng-Wan instances therefore provide a natural setting in which to explore the power of Regev's framework. We first extend the Cheng-Wan reduction to the discrete logarithm problem in finite abelian groups, assuming access to embeddings of their cyclic factors. We also establish NP-hardness of Reed-Solomon bounded-distance decoding at asymptotically vanishing rate, reaching the rate regime relevant to Cheng-Wan, while the radius at which we establish hardness remains beyond the Cheng-Wan radius. We then instantiate Regev's reduction at the Cheng-Wan parameters and, under a conjecture on the performance of the induced BDD algorithm on the structured Cheng-Wan instances, characterize the noise threshold for the quantum decoding problem(QDP) sufficient to solve DLOG. We evaluate known decoders in this regime and show that a quantum decoder based on unambiguous state discrimination(USD) enables Regev's reduction to reach a strictly stronger decoding regime than the standard classical Reed-Solomon decoders we consider. Nevertheless, the QDP noise threshold required by the Cheng-Wan reduction is asymptotically 4/3 times that achieved by USD. Going beyond the USD framework, we show that every Cheng-Wan instance can be solved through Regev's reduction using a suitable Pretty Good Measurement and its inverse, although no efficient implementation of it is known.

quant-ph↗

arXiv2025

Quantum and classical algorithms for SOCP based on the multiplicative weights update method

We give classical and quantum algorithms for approximately solving second-order cone programs (SOCPs) based on the multiplicative weights (MW) update method. Our approach follows the MW framework previously applied to semidefinite programs (SDPs), of which SOCP is a special case. We show that the additional structure of SOCPs can be exploited to give better runtime with SOCP-specific algorithms. For an SOCP with $m$ linear constraints over $n$ variables partitioned into $r \leq n$ second-order cones, our quantum algorithm requires $\widetilde{O}(\sqrt{r}γ^5 + \sqrt{m}γ^4)$ (coherent) queries to the underlying data defining the instance, where $γ$ is a scale-invariant parameter proportional to the inverse precision. This nearly matches the complexity of solving linear programs (LPs), which are a less expressive subset of SOCP. It also outperforms (especially if $n \gg r$) the naive approach that applies existing SDP algorithms onto SOCPs, which has complexity $\widetilde{O}(γ^{4}(n + γ\sqrt{n} + \sqrt{m}))$. Our classical algorithm for SOCP has complexity $\widetilde{O}(nγ^4 + m γ^6)$ in the sample-and-query model.

quant-ph↗