When the Learning With Errors Problem Meets the Coherent Ising Machine: A Penalty-Free Algorithm-Hardware Co-Design
- URL: http://arxiv.org/abs/2606.22843v1
- Date: Mon, 22 Jun 2026 04:39:32 GMT
- Title: When the Learning With Errors Problem Meets the Coherent Ising Machine: A Penalty-Free Algorithm-Hardware Co-Design
- Abstract summary: The Learning With Errors (LWE) problem constitutes the mathematical foundation of Post-Quantum Cryptography (PQC)<n>We propose CIM-BDD, a hybrid Bounded-Distance-Decoding solver that reduces LWE to a Quadratic Unconstrained Binary Optimization (QUBO) problem through a strictly emphpenalty-free mapping.<n>We validate the framework on the TU Darmstadt LWE Challenge, giving an end-to-end demonstration for both Search- and Decision-LWE of a $40$-dimensional instance on the Coherent Ising Machine CPQC-550.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The Learning With Errors (LWE) problem constitutes the mathematical foundation of modern Post-Quantum Cryptography (PQC). Cryptanalysis of LWE ranges from classical lattice reduction to machine learning and quantum-classical hybrids. We propose CIM-BDD, a hybrid Bounded-Distance-Decoding solver that reduces LWE to a Quadratic Unconstrained Binary Optimization (QUBO) problem through a strictly \emph{penalty-free} mapping. An algebraic elimination of the secret embeds LWE into a $q$-ary lattice, absorbing the modular arithmetic and recasting the problem as a Closest Vector Problem (CVP). The squared error norm is then used \emph{directly} as the QUBO energy, so the cryptographic noise is the objective to be minimized rather than a penalized constraint. To realize this general model on current Noisy Intermediate-Scale Quantum (NISQ) devices, we design a special encoding method: a Continuous Relaxed Babai's Nearest Plane (CR-BNP) projection drives an adaptive mixed-radix encoder that greatly reduces both the qubit count and the QUBO coefficient range, so that a single batched hardware submission suffices. We further derive a statistically bounded early-stopping threshold ($T_{\text{early}}$) that acts as a one-sided certificate and doubles as a Decision-LWE distinguisher. We validate the framework on the TU Darmstadt LWE Challenge, giving an end-to-end demonstration for both Search- and Decision-LWE of a $40$-dimensional instance on the Coherent Ising Machine CPQC-550. This work establishes a new algorithm-hardware co-design paradigm for quantum-classical hybrid cryptanalysis.
Related papers
- Explainable quantum regression algorithm with encoded data structure [0.0]
We construct the first interpretable quantum regression algorithm, in which the quantum state exactly encodes the classical data table.<n>We also exploit the encoded data structure to reduce the gate complexity of computing the regression map.
arXiv Detail & Related papers (2026-04-17T03:42:12Z) - A Scalable Distributed Quantum Optimization Framework via Factor Graph Paradigm [46.08923284345648]
We introduce a structure-aware framework for distributed quantum optimization.<n>We find that for a search space of size $N$, our framework achieves $O(sqrtN)$ query complexity up to processors and separator dependent factors.<n>We show that structure-aware decomposition delivers a practical path to scalable distributed quantum optimization on quantum networks.
arXiv Detail & Related papers (2026-03-08T15:15:52Z) - Adaptive Sparsification for Linear Programming [3.735586259382096]
We introduce a generic framework for solving linear programs with many constraints $(n gg d)$ via adaptive sparsification.<n>We present a quantum version of Clarkson's algorithm that finds an exact solution to an LP using $tildeO(sqrtn d3)$ row-queries to the constraint matrix.<n>Second, our framework yields new state-of-the-art algorithms for mixed packing and covering problems when the packing constraints are simple''
arXiv Detail & Related papers (2025-10-09T15:36:00Z) - Quantum-Classical Hybrid Quantized Neural Network [8.382617481718643]
We present a novel Quadratic Binary Optimization (QBO) model for quantized neural network training, enabling the use of arbitrary activation and loss functions.<n>We employ the Quantum Gradient Conditional Descent (QCGD) algorithm, which leverages quantum computing to directly solve the QCBO problem.
arXiv Detail & Related papers (2025-06-23T02:12:36Z) - Single-loop Algorithms for Stochastic Non-convex Optimization with Weakly-Convex Constraints [49.76332265680669]
This paper examines a crucial subset of problems where both the objective and constraint functions are weakly convex.<n>Existing methods often face limitations, including slow convergence rates or reliance on double-loop designs.<n>We introduce a novel single-loop penalty-based algorithm to overcome these challenges.
arXiv Detail & Related papers (2025-04-21T17:15:48Z) - Qubit-Efficient Quantum Annealing for Stochastic Unit Commitment [4.2251752131402585]
Unit Commitment (SUC) has been proposed to manage the uncertainties driven by the integration of renewable energy sources.<n>This paper introduces the Powell-Hestenes-Rockafellar Augmented Lagrangian Multiplier (PHR-ALM) method to eliminate the need for slack variables.<n>To further reduce the qubit overhead, quantum ADMM is applied to break large-scale SUC into smaller blocks and enables a sequential solution.
arXiv Detail & Related papers (2025-02-21T20:15:40Z) - A quantum-classical hybrid algorithm with Ising model for the learning with errors problem [13.06030390635216]
We propose a quantum-classical hybrid algorithm with Ising model (HAWI) to address the Learning-With-Errors (LWE) problem.
We identify the low-energy levels of the Hamiltonian to extract the solution, making it suitable for implementation on current noisy intermediate-scale quantum (NISQ) devices.
Our algorithm is iterations, and its time complexity depends on the specific quantum algorithm employed to find the Hamiltonian's low-energy levels.
arXiv Detail & Related papers (2024-08-15T05:11:35Z) - A quantum annealing approach to the minimum distance problem of quantum codes [0.0]
We introduce an approach to compute the minimum distance of quantum stabilizer codes by reformulating the problem as a Quadratic Unconstrained Binary Optimization problem.
We demonstrate practical viability of our method by comparing the performance of purely classical algorithms with the D-Wave Advantage 4.1 quantum annealer.
arXiv Detail & Related papers (2024-04-26T21:29:42Z) - CBQ: Cross-Block Quantization for Large Language Models [66.82132832702895]
Post-training quantization (PTQ) has played a key role in compressing large language models (LLMs) with ultra-low costs.<n>We propose CBQ, a cross-block reconstruction-based PTQ method for LLMs.<n> CBQ employs a cross-block dependency using a reconstruction scheme, establishing long-range dependencies across multiple blocks to minimize error accumulation.
arXiv Detail & Related papers (2023-12-13T07:56:27Z) - Quantum Worst-Case to Average-Case Reductions for All Linear Problems [66.65497337069792]
We study the problem of designing worst-case to average-case reductions for quantum algorithms.
We provide an explicit and efficient transformation of quantum algorithms that are only correct on a small fraction of their inputs into ones that are correct on all inputs.
arXiv Detail & Related papers (2022-12-06T22:01:49Z) - Faster One-Sample Stochastic Conditional Gradient Method for Composite
Convex Minimization [61.26619639722804]
We propose a conditional gradient method (CGM) for minimizing convex finite-sum objectives formed as a sum of smooth and non-smooth terms.
The proposed method, equipped with an average gradient (SAG) estimator, requires only one sample per iteration. Nevertheless, it guarantees fast convergence rates on par with more sophisticated variance reduction techniques.
arXiv Detail & Related papers (2022-02-26T19:10:48Z) - A Hybrid Quantum-Classical Algorithm for Robust Fitting [47.42391857319388]
We propose a hybrid quantum-classical algorithm for robust fitting.
Our core contribution is a novel robust fitting formulation that solves a sequence of integer programs.
We present results obtained using an actual quantum computer.
arXiv Detail & Related papers (2022-01-25T05:59:24Z) - Covariance-Free Sparse Bayesian Learning [62.24008859844098]
We introduce a new SBL inference algorithm that avoids explicit inversions of the covariance matrix.
Our method can be up to thousands of times faster than existing baselines.
We showcase how our new algorithm enables SBL to tractably tackle high-dimensional signal recovery problems.
arXiv Detail & Related papers (2021-05-21T16:20:07Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.