Reducing T-Count in quantum string matching algorithm using relative-phase Fredkin gate
- URL: http://arxiv.org/abs/2411.01283v1
- Date: Sat, 02 Nov 2024 15:27:18 GMT
- Title: Reducing T-Count in quantum string matching algorithm using relative-phase Fredkin gate
- Authors: Byeongyong Park, Hansol Noh, Doyeol Ahn,
- Abstract summary: This paper introduces the relative-phase Fredkin gate as a strategy to notably reduce the number of T gates (T-count) necessary for the QSM algorithm.
We demonstrate our method is advantageous in terms of other circuit costs, such as the depth of T gates and the number of CNOT gates.
- Score: 0.0
- License:
- Abstract: The string-matching problem, ubiquitous in computer science, can significantly benefit from quantum algorithms due to their potential for greater efficiency compared to classical approaches. The practical implementation of the quantum string matching (QSM) algorithm requires fault-tolerant quantum computation due to the fragility of quantum information. A major obstacle in implementing fault-tolerant quantum computation is the high cost associated with executing T gates. This paper introduces the relative-phase Fredkin gate as a strategy to notably reduce the number of T gates (T-count) necessary for the QSM algorithm. This reduces the T-count from 14N^(3/2) log_2 N-O(N^(3/2)) to 8N^(3/2) log_2 N-O(N^(3/2)), where N represents the size of the database to be searched. Additionally, we demonstrate that our method is advantageous in terms of other circuit costs, such as the depth of T gates and the number of CNOT gates. This advancement contributes to the ongoing development of the QSM algorithm, paving the way for more efficient solutions in the field of computer science.
Related papers
- Quantum Multiplexer Simplification for State Preparation [0.7270112855088837]
We propose an algorithm that detects whether a given quantum state can be factored into substates.
The simplification is done by eliminating controls of quantum multiplexers.
Considering efficiency in terms of depth and number of CNOT gates, our method is competitive with the methods in the literature.
arXiv Detail & Related papers (2024-09-09T13:53:02Z) - The exact lower bound of CNOT-complexity for fault-tolerant quantum Fourier transform [9.657072841833243]
We study the exact lower bound problem of CNOT gate complexity for fault-tolerant QFT.
Our work can provide a reference for designing algorithms based on active defense in a quantum setting.
arXiv Detail & Related papers (2024-09-04T08:06:11Z) - Linear Circuit Synthesis using Weighted Steiner Trees [45.11082946405984]
CNOT circuits are a common building block of general quantum circuits.
This article presents state-of-the-art algorithms for optimizing the number of CNOT gates.
A simulated evaluation shows that the suggested is almost always beneficial and reduces the number of CNOT gates by up to 10%.
arXiv Detail & Related papers (2024-08-07T19:51:22Z) - Optimizing Gate Decomposition for High-Level Quantum Programming [0.0]
Multi-controlled quantum gates naturally arise in high-level quantum programming.
This paper presents novel methods for optimizing multi-controlled quantum gates.
We demonstrate significant reductions in the number of CNOT gates.
arXiv Detail & Related papers (2024-06-08T21:36:08Z) - T-Count Optimizing Genetic Algorithm for Quantum State Preparation [0.05999777817331316]
We present and utilize a genetic algorithm for state preparation circuits consisting of gates from the Clifford + T gate set.
Our algorithm does automatically generate fault tolerantly implementable solutions where the number of the most error prone components is reduced.
arXiv Detail & Related papers (2024-06-06T12:26:14Z) - Quantum Circuit Optimization with AlphaTensor [47.9303833600197]
We develop AlphaTensor-Quantum, a method to minimize the number of T gates that are needed to implement a given circuit.
Unlike existing methods for T-count optimization, AlphaTensor-Quantum can incorporate domain-specific knowledge about quantum computation and leverage gadgets.
Remarkably, it discovers an efficient algorithm akin to Karatsuba's method for multiplication in finite fields.
arXiv Detail & Related papers (2024-02-22T09:20:54Z) - Optimizing Tensor Network Contraction Using Reinforcement Learning [86.05566365115729]
We propose a Reinforcement Learning (RL) approach combined with Graph Neural Networks (GNN) to address the contraction ordering problem.
The problem is extremely challenging due to the huge search space, the heavy-tailed reward distribution, and the challenging credit assignment.
We show how a carefully implemented RL-agent that uses a GNN as the basic policy construct can address these challenges.
arXiv Detail & Related papers (2022-04-18T21:45:13Z) - Realization of arbitrary doubly-controlled quantum phase gates [62.997667081978825]
We introduce a high-fidelity gate set inspired by a proposal for near-term quantum advantage in optimization problems.
By orchestrating coherent, multi-level control over three transmon qutrits, we synthesize a family of deterministic, continuous-angle quantum phase gates acting in the natural three-qubit computational basis.
arXiv Detail & Related papers (2021-08-03T17:49:09Z) - Space-efficient binary optimization for variational computing [68.8204255655161]
We show that it is possible to greatly reduce the number of qubits needed for the Traveling Salesman Problem.
We also propose encoding schemes which smoothly interpolate between the qubit-efficient and the circuit depth-efficient models.
arXiv Detail & Related papers (2020-09-15T18:17:27Z) - Improving the Performance of Deep Quantum Optimization Algorithms with
Continuous Gate Sets [47.00474212574662]
Variational quantum algorithms are believed to be promising for solving computationally hard problems.
In this paper, we experimentally investigate the circuit-depth-dependent performance of QAOA applied to exact-cover problem instances.
Our results demonstrate that the use of continuous gate sets may be a key component in extending the impact of near-term quantum computers.
arXiv Detail & Related papers (2020-05-11T17:20:51Z)
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.