Algorithm-oriented qubit mapping for variational quantum algorithms
- URL: http://arxiv.org/abs/2310.09826v2
- Date: Wed, 24 Jan 2024 13:48:18 GMT
- Title: Algorithm-oriented qubit mapping for variational quantum algorithms
- Authors: Yanjun Ji, Xi Chen, Ilia Polian, Yue Ban
- Abstract summary: We present an algorithm-oriented qubit mapping (AOQMAP) that capitalizes on the inherent regular substructures within quantum algorithms.
AOQMAP achieves up to 82.1% depth reduction and a 138% average increase in success probability compared to Qiskit, Tket, and SWAP network.
- Score: 4.359579392793038
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Optimizing qubit mapping is critical for the successful implementation of
algorithms on near-term quantum devices. In this paper we present an
algorithm-oriented qubit mapping (AOQMAP) that capitalizes on the inherent
regular substructures within quantum algorithms. While exact methods provide
optimal solutions, their exponential scaling renders them impractical. AOQMAP
addresses this challenge through a strategic two-step approach. First, it
adapts circuits onto subtopologies of the target quantum device to satisfy
connectivity constraints. Optimal and scalable solutions with minimum circuit
depth are provided for variational quantum algorithms with all-to-all connected
interactions on linear, T-shaped, and H-shaped subtopologies. Second, it
identifies the optimal mapping scheme by using a cost function based on current
device noise. Demonstrations on various IBM quantum devices indicate that
AOQMAP significantly reduces both gate count and circuit depth compared to
traditional mapping approaches, consequently enhancing performance.
Specifically, AOQMAP achieves up to 82.1% depth reduction and a 138% average
increase in success probability compared to Qiskit, Tket, and SWAP network.
This specialized and scalable mapping paradigm can potentially optimize broader
quantum algorithm classes. Tailoring qubit mapping to leverage algorithmic
features holds the promise of maximizing the performance of near-term quantum
algorithms.
Related papers
- A Fast and Adaptable Algorithm for Optimal Multi-Qubit Pathfinding in Quantum Circuit Compilation [0.0]
This work focuses on multi-qubit pathfinding as a critical subroutine within the quantum circuit compilation mapping problem.
We introduce an algorithm, modelled using binary integer linear programming, that navigates qubits on quantum hardware optimally with respect to circuit SWAP-gate depth.
We have benchmarked the algorithm across a variety of quantum hardware layouts, assessing properties such as computational runtimes, solution SWAP depths, and accumulated SWAP-gate error rates.
arXiv Detail & Related papers (2024-05-29T05:59:15Z) - Quantum Subroutine for Variance Estimation: Algorithmic Design and Applications [80.04533958880862]
Quantum computing sets the foundation for new ways of designing algorithms.
New challenges arise concerning which field quantum speedup can be achieved.
Looking for the design of quantum subroutines that are more efficient than their classical counterpart poses solid pillars to new powerful quantum algorithms.
arXiv Detail & Related papers (2024-02-26T09:32:07Z) - Hungarian Qubit Assignment for Optimized Mapping of Quantum Circuits on
Multi-Core Architectures [1.1288814203214292]
Quantum computers are expected to adopt a modular approach, featuring clusters of tightly connected quantum bits with sparser connections between these clusters.
Efficiently distributing qubits across multiple processing cores is critical for improving quantum computing systems' performance and scalability.
We propose the Hungarian Qubit Assignment (HQA) algorithm, which leverages the Hungarian algorithm to improve qubit-to-core assignment.
arXiv Detail & Related papers (2023-09-21T15:48:45Z) - 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) - Variational quantum iterative power algorithms for global optimization [2.526320329485241]
We introduce a family of variational quantum algorithms called quantum iterative power algorithms (QIPA)
QIPA outperforms existing hybrid near-term quantum algorithms of the same kind.
We anticipate large-scale implementation and adoption of the proposed algorithm across current major quantum hardware.
arXiv Detail & Related papers (2022-08-22T17:45:14Z) - Scaling Quantum Approximate Optimization on Near-term Hardware [49.94954584453379]
We quantify scaling of the expected resource requirements by optimized circuits for hardware architectures with varying levels of connectivity.
We show the number of measurements, and hence total time to synthesizing solution, grows exponentially in problem size and problem graph degree.
These problems may be alleviated by increasing hardware connectivity or by recently proposed modifications to the QAOA that achieve higher performance with fewer circuit layers.
arXiv Detail & Related papers (2022-01-06T21:02:30Z) - Optimal Qubit Mapping with Simultaneous Gate Absorption [9.530683922512873]
A key step in compilation is mapping the qubits in the program to physical qubits on a given quantum computer.
We present OLSQ-GA, an optimal qubit mapper with a key feature of simultaneous SWAP gate absorption.
OLSQ-GA reduces depth by up to 50.0% and SWAP count by 100% compared to other state-of-the-art methods.
arXiv Detail & Related papers (2021-09-14T05:15:36Z) - 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) - 2D Qubit Placement of Quantum Circuits using LONGPATH [1.6631602844999722]
Two algorithms are proposed to optimize the number of SWAP gates in any arbitrary quantum circuit.
Our approach has a significant reduction in number of SWAP gates in 1D and 2D NTC architecture.
arXiv Detail & Related papers (2020-07-14T04:09:52Z)
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.