Quantum Circuit Optimization: Current trends and future direction
- URL: http://arxiv.org/abs/2408.08941v1
- Date: Fri, 16 Aug 2024 15:07:51 GMT
- Title: Quantum Circuit Optimization: Current trends and future direction
- Authors: Geetha Karuppasamy, Varun Puram, Stevens Johnson, Johnson P Thomas,
- Abstract summary: Recent advancements in quantum circuit optimization are explored.
analytical algorithms, quantum algorithms, machine learning-based algorithms, and hybrid quantum-classical algorithms are discussed.
- Score: 0.0
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: Optimization of quantum circuits for a given problem is very important in order to achieve faster calculations as well as reduce errors due to noise. Optimization has to be achieved while ensuring correctness at all times. In this survey paper, recent advancements in quantum circuit optimization are explored. Both hardware independent as well as hardware dependent optimization are presented. State-of-the-art methods for optimizing quantum circuits, including analytical algorithms, heuristic algorithms, machine learning-based algorithms, and hybrid quantum-classical algorithms are discussed. Additionally, the advantages and disadvantages of each method and the challenges associated with them are highlighted. Moreover, the potential research opportunities in this field are also discussed.
Related papers
- Harnessing Inferior Solutions For Superior Outcomes: Obtaining Robust Solutions From Quantum Algorithms [0.0]
We adapt quantum algorithms to tackle robust optimization problems.
We present two innovative methods for obtaining robust optimal solutions.
Theses are applied on two use cases within the energy sector.
arXiv Detail & Related papers (2024-04-25T17:32:55Z) - Bayesian Parameterized Quantum Circuit Optimization (BPQCO): A task and hardware-dependent approach [49.89480853499917]
Variational quantum algorithms (VQA) have emerged as a promising quantum alternative for solving optimization and machine learning problems.
In this paper, we experimentally demonstrate the influence of the circuit design on the performance obtained for two classification problems.
We also study the degradation of the obtained circuits in the presence of noise when simulating real quantum computers.
arXiv Detail & Related papers (2024-04-17T11:00:12Z) - Challenges and Opportunities in Quantum Optimization [14.7608536260003]
Recent advances in quantum computers are demonstrating the ability to solve problems at a scale beyond brute force classical simulation.
Across computer science and physics, there are different approaches for major optimization problems.
arXiv Detail & Related papers (2023-12-04T19:00:44Z) - Randomized Benchmarking of Local Zeroth-Order Optimizers for Variational
Quantum Systems [65.268245109828]
We compare the performance of classicals across a series of partially-randomized tasks.
We focus on local zeroth-orders due to their generally favorable performance and query-efficiency on quantum systems.
arXiv Detail & Related papers (2023-10-14T02:13:26Z) - A Review on Quantum Approximate Optimization Algorithm and its Variants [47.89542334125886]
The Quantum Approximate Optimization Algorithm (QAOA) is a highly promising variational quantum algorithm that aims to solve intractable optimization problems.
This comprehensive review offers an overview of the current state of QAOA, encompassing its performance analysis in diverse scenarios.
We conduct a comparative study of selected QAOA extensions and variants, while exploring future prospects and directions for the algorithm.
arXiv Detail & Related papers (2023-06-15T15:28:12Z) - Surrogate-based optimization for variational quantum algorithms [0.0]
Variational quantum algorithms are a class of techniques intended to be used on near-term quantum computers.
We introduce the idea of learning surrogate models for variational circuits using few experimental measurements.
We then perform parameter optimization using these models as opposed to the original data.
arXiv Detail & Related papers (2022-04-12T00:15:17Z) - Stochastic optimization algorithms for quantum applications [0.0]
We review the use of first-order, second-order, and quantum natural gradient optimization methods, and propose new algorithms defined in the field of complex numbers.
The performance of all methods is evaluated by means of their application to variational quantum eigensolver, quantum control of quantum states, and quantum state estimation.
arXiv Detail & Related papers (2022-03-11T16:17:05Z) - Variational Quantum Optimization with Multi-Basis Encodings [62.72309460291971]
We introduce a new variational quantum algorithm that benefits from two innovations: multi-basis graph complexity and nonlinear activation functions.
Our results in increased optimization performance, two increase in effective landscapes and a reduction in measurement progress.
arXiv Detail & Related papers (2021-06-24T20:16:02Z) - Adaptive pruning-based optimization of parameterized quantum circuits [62.997667081978825]
Variisy hybrid quantum-classical algorithms are powerful tools to maximize the use of Noisy Intermediate Scale Quantum devices.
We propose a strategy for such ansatze used in variational quantum algorithms, which we call "Efficient Circuit Training" (PECT)
Instead of optimizing all of the ansatz parameters at once, PECT launches a sequence of variational algorithms.
arXiv Detail & Related papers (2020-10-01T18:14:11Z) - 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)
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.