Performance analysis of a filtering variational quantum algorithm
- URL: http://arxiv.org/abs/2404.08933v1
- Date: Sat, 13 Apr 2024 08:50:44 GMT
- Title: Performance analysis of a filtering variational quantum algorithm
- Authors: Gabriel Marin-Sanchez, David Amaro,
- Abstract summary: Filtering Variational Quantum Eigensolver (F-VQE) is a variational hybrid quantum algorithm designed to solve optimization problems on existing quantum computers.
We employ Instantaneous Quantum Polynomial circuits as our parameterized quantum circuits.
Despite some observed positive signs, we conclude that significant development is necessary for a practical advantage with F-VQE.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Even a minor boost in solving combinatorial optimization problems can greatly benefit multiple industries. Quantum computers, with their unique information processing capabilities, hold promise for delivering such enhancements. The Filtering Variational Quantum Eigensolver (F-VQE) is a variational hybrid quantum algorithm designed to solve combinatorial optimization problems on existing quantum computers with limited qubit number, connectivity, and fidelity. In this work we employ Instantaneous Quantum Polynomial circuits as our parameterized quantum circuits. We propose a hardware-efficient implementation that respects limited qubit connectivity and show that they halve the number of circuits necessary to evaluate the gradient with the parameter-shift rule. To assess the potential of this protocol in the context of combinatorial optimization, we conduct extensive numerical analysis. We compare the performance against three classical baseline algorithms on weighted MaxCut and the Asymmetric Traveling Salesperson Problem (ATSP). We employ noiseless simulators for problems encoded on 13 to 29 qubits, and up to 37 qubits on the IBMQ real quantum devices. The ATSP encoding employed reduces the number of qubits and avoids the need of constraints compared to the standard QUBO / Ising model. Despite some observed positive signs, we conclude that significant development is necessary for a practical advantage with F-VQE.
Related papers
- Bias-Field Digitized Counterdiabatic Quantum Algorithm for Higher-Order Binary Optimization [39.58317527488534]
We present an enhanced bias-field digitized counterdiabatic quantum optimization (BF-DCQO) algorithm to address higher-order unconstrained binary optimization (HUBO) problems.
Our protocol is experimentally validated using 156 qubits on an IBM quantum processor with a heavy-hex architecture.
arXiv Detail & Related papers (2024-09-05T17:38:59Z) - Variational Quantum Algorithms for Combinatorial Optimization [0.571097144710995]
Variational Algorithms (VQA) have emerged as one of the strongest candidates towards reaching practical applicability of NISQ systems.
This paper explores the current state and recent developments of VQAs, emphasizing their applicability to Approximate optimization.
We implement QAOA circuits with varying depths to solve the MaxCut problem on graphs with 10 and 20 nodes.
arXiv Detail & Related papers (2024-07-08T22:02:39Z) - Scaling Up the Quantum Divide and Conquer Algorithm for Combinatorial Optimization [0.8121127831316319]
We propose a method for constructing quantum circuits which greatly reduces inter-device communication costs.
We show that we can construct tractable circuits nearly three times the size of previous QDCA methods while retaining a similar or greater level of quality.
arXiv Detail & Related papers (2024-05-01T20:49:50Z) - Parallel circuit implementation of variational quantum algorithms [0.0]
We present a method to split quantum circuits of variational quantum algorithms (VQAs) to allow for parallel training and execution.
We apply this specifically to optimization problems, where inherent structures from the problem can be identified.
We show that not only can our method address larger problems, but that it is also possible to run full VQA models while training parameters using only one slice.
arXiv Detail & Related papers (2023-04-06T12:52:29Z) - Quantum-inspired optimization for wavelength assignment [51.55491037321065]
We propose and develop a quantum-inspired algorithm for solving the wavelength assignment problem.
Our results pave the way to the use of quantum-inspired algorithms for practical problems in telecommunications.
arXiv Detail & Related papers (2022-11-01T07:52:47Z) - Decomposition of Matrix Product States into Shallow Quantum Circuits [62.5210028594015]
tensor network (TN) algorithms can be mapped to parametrized quantum circuits (PQCs)
We propose a new protocol for approximating TN states using realistic quantum circuits.
Our results reveal one particular protocol, involving sequential growth and optimization of the quantum circuit, to outperform all other methods.
arXiv Detail & Related papers (2022-09-01T17:08:41Z) - Adiabatic Quantum Graph Matching with Permutation Matrix Constraints [75.88678895180189]
Matching problems on 3D shapes and images are frequently formulated as quadratic assignment problems (QAPs) with permutation matrix constraints, which are NP-hard.
We propose several reformulations of QAPs as unconstrained problems suitable for efficient execution on quantum hardware.
The proposed algorithm has the potential to scale to higher dimensions on future quantum computing architectures.
arXiv Detail & Related papers (2021-07-08T17:59:55Z) - 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) - Filtering variational quantum algorithms for combinatorial optimization [0.0]
We introduce the Variational Quantum Eigensolver (F-VQE) which utilizes filtering operators to achieve faster and more reliable convergence to the optimal solution.
We also explore the use of causal cones to reduce the number of qubits required on a quantum computer.
arXiv Detail & Related papers (2021-06-18T11:07:33Z) - Quantum circuit architecture search for variational quantum algorithms [88.71725630554758]
We propose a resource and runtime efficient scheme termed quantum architecture search (QAS)
QAS automatically seeks a near-optimal ansatz to balance benefits and side-effects brought by adding more noisy quantum gates.
We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks.
arXiv Detail & Related papers (2020-10-20T12:06:27Z) - 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.