Overflow-Safe Polylog-Time Parallel Minimum-Weight Perfect Matching Decoder: Toward Experimental Demonstration
- URL: http://arxiv.org/abs/2603.03776v1
- Date: Wed, 04 Mar 2026 06:32:24 GMT
- Title: Overflow-Safe Polylog-Time Parallel Minimum-Weight Perfect Matching Decoder: Toward Experimental Demonstration
- Authors: Ryo Mikami, Hayata Yamasaki,
- Abstract summary: Fault-tolerant quantum computation (FTQC) requires fast and accurate decoding of quantum errors.<n>We present a polylog-time MWPM decoder that detects overflow in finite-bit representations.<n>With algorithmic optimizations tailored to the structure of the determinant-based approach, we reduce the bit required to represent intermediate values by more than $99.9%$.
- Score: 1.0742675209112622
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Fault-tolerant quantum computation (FTQC) requires fast and accurate decoding of quantum errors, which is often formulated as a minimum-weight perfect matching (MWPM) problem. A determinant-based approach has been proposed as a promising method to surpass the conventional polynomial runtime of MWPM decoding via the blossom algorithm, asymptotically achieving polylogarithmic parallel runtime. However, the existing approach requires an impractically large bit length to represent intermediate values during the computation of the matrix determinant; moreover, when implemented on a finite-bit machine, the algorithm cannot detect overflow, and therefore, the mathematical correctness of such algorithms cannot be guaranteed. In this work, we address these issues by presenting a polylog-time MWPM decoder that detects overflow in finite-bit representations by employing an algebraic framework over a truncated polynomial ring. Within this framework, all arithmetic operations are implemented using bitwise XOR and shift operations, enabling efficient and hardware-friendly implementation. Furthermore, with algorithmic optimizations tailored to the structure of the determinant-based approach, we reduce the arithmetic bit length required to represent intermediate values in the determinant computation by more than $99.9\%$, while preserving its polylogarithmic runtime scaling. These results open the possibility of a proof-of-principle demonstration of the polylog-time MPWM decoding in the early FTQC regime.
Related papers
- Fast MLE and MAPE-Based Device Activity Detection for Grant-Free Access via PSCA and PSCA-Net [13.076905065264091]
Fast and accurate device activity is the critical challenge in grant-free access for supporting massive machine-type communications.<n>We propose new maximum likelihood estimation (MLE) based device activity detection methods.<n>We present a deep unrolling neural network implementation called PSCA-Net to further reduce the computation time.
arXiv Detail & Related papers (2025-03-19T14:31:09Z) - Fast Expectation Value Calculation Speedup of Quantum Approximate Optimization Algorithm: HoLCUs QAOA [55.2480439325792]
We present a new method for calculating expectation values of operators that can be expressed as a linear combination of unitary (LCU) operators.<n>This method is general for any quantum algorithm and is of particular interest in the acceleration of variational quantum algorithms.
arXiv Detail & Related papers (2025-03-03T17:15:23Z) - A Sample Efficient Alternating Minimization-based Algorithm For Robust Phase Retrieval [56.67706781191521]
In this work, we present a robust phase retrieval problem where the task is to recover an unknown signal.
Our proposed oracle avoids the need for computationally spectral descent, using a simple gradient step and outliers.
arXiv Detail & Related papers (2024-09-07T06:37:23Z) - From Optimization to Control: Quasi Policy Iteration [2.0769172070951067]
We introduce a novel control algorithm coined as quasi-policy iteration (QPI)<n>QPI exploits two linear structural constraints specific to MDPs and allows for the incorporation of prior information on the transition probability kernel.
arXiv Detail & Related papers (2023-11-18T21:00:14Z) - Quantum-Based Feature Selection for Multi-classification Problem in
Complex Systems with Edge Computing [15.894122816099133]
A quantum-based feature selection algorithm for the multi-classification problem, namely, QReliefF, is proposed.
Our algorithm is superior in finding the nearest neighbor, reducing the complexity from O(M) to O(sqrt(M)).
arXiv Detail & Related papers (2023-10-01T03:57:13Z) - Quantum Goemans-Williamson Algorithm with the Hadamard Test and
Approximate Amplitude Constraints [62.72309460291971]
We introduce a variational quantum algorithm for Goemans-Williamson algorithm that uses only $n+1$ qubits.
Efficient optimization is achieved by encoding the objective matrix as a properly parameterized unitary conditioned on an auxilary qubit.
We demonstrate the effectiveness of our protocol by devising an efficient quantum implementation of the Goemans-Williamson algorithm for various NP-hard problems.
arXiv Detail & Related papers (2022-06-30T03:15:23Z) - Adaptive Sampling for Best Policy Identification in Markov Decision
Processes [79.4957965474334]
We investigate the problem of best-policy identification in discounted Markov Decision (MDPs) when the learner has access to a generative model.
The advantages of state-of-the-art algorithms are discussed and illustrated.
arXiv Detail & Related papers (2020-09-28T15:22:24Z) - Accelerated Message Passing for Entropy-Regularized MAP Inference [89.15658822319928]
Maximum a posteriori (MAP) inference in discrete-valued random fields is a fundamental problem in machine learning.
Due to the difficulty of this problem, linear programming (LP) relaxations are commonly used to derive specialized message passing algorithms.
We present randomized methods for accelerating these algorithms by leveraging techniques that underlie classical accelerated gradient.
arXiv Detail & Related papers (2020-07-01T18:43:32Z) - Approximation Algorithms for Sparse Principal Component Analysis [57.5357874512594]
Principal component analysis (PCA) is a widely used dimension reduction technique in machine learning and statistics.
Various approaches to obtain sparse principal direction loadings have been proposed, which are termed Sparse Principal Component Analysis.
We present thresholding as a provably accurate, time, approximation algorithm for the SPCA problem.
arXiv Detail & Related papers (2020-06-23T04:25:36Z)
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.