ECDQC: Efficient Compilation for Distributed Quantum Computing with Linear Layout
- URL: http://arxiv.org/abs/2410.23857v2
- Date: Fri, 01 Nov 2024 14:13:27 GMT
- Title: ECDQC: Efficient Compilation for Distributed Quantum Computing with Linear Layout
- Authors: Kecheng Liu, Yidong Zhou, Haochen Luo, Lingjun Xiong, Yuchen Zhu, Eilis Casey, Jinglei Cheng, Samuel Yen-Chi Chen, Zhiding Liang,
- Abstract summary: We propose an efficient compilation method for distributed quantum computing (DQC) using the Linear Nearest Neighbor (LNN) architecture.
Our approach significantly decreases compilation time, gate count, and circuit depth, improving robustness for large-scale quantum computations.
- Score: 6.382954852270525
- License:
- Abstract: In this paper, we propose an efficient compilation method for distributed quantum computing (DQC) using the Linear Nearest Neighbor (LNN) architecture. By exploiting the LNN topology's symmetry, we optimize quantum circuit compilation for High Local Connectivity, Sparse Full Connectivity (HLC-SFC) algorithms like Quantum Approximate Optimization Algorithm (QAOA) and Quantum Fourier Transform (QFT). We also utilize dangling qubits to minimize non-local interactions and reduce SWAP gates. Our approach significantly decreases compilation time, gate count, and circuit depth, improving scalability and robustness for large-scale quantum computations.
Related papers
- SWAP-less Implementation of Quantum Algorithms [0.0]
We present a formalism based on tracking the flow of parity quantum information to implement algorithms on devices with limited connectivity.
We leverage the fact that entangling gates not only manipulate quantum states but can also be exploited to transport quantum information.
arXiv Detail & Related papers (2024-08-20T14:51:00Z) - Coqa: Blazing Fast Compiler Optimizations for QAOA [3.165516590671437]
We propose Coqa to optimize QAOA circuit compilation tailored to different types of quantum hardware.
We are able to achieve an average of 30% reduction in gate count and a 39x acceleration in compilation time across our benchmarks.
arXiv Detail & Related papers (2024-08-15T18:12:04Z) - Subspace-Based Local Compilation of Variational Quantum Circuits for Large-Scale Quantum Many-Body Simulation [0.0]
This paper proposes a hybrid quantum-classical algorithm for compiling the time-evolution operator.
It achieves a 95% reduction in circuit depth compared to Trotterization while maintaining accuracy.
We estimate the gate count needed to execute the quantum simulations using the LSVQC on near-term quantum computing architectures.
arXiv Detail & Related papers (2024-07-19T09:50:01Z) - Quantum Compiling with Reinforcement Learning on a Superconducting Processor [55.135709564322624]
We develop a reinforcement learning-based quantum compiler for a superconducting processor.
We demonstrate its capability of discovering novel and hardware-amenable circuits with short lengths.
Our study exemplifies the codesign of the software with hardware for efficient quantum compilation.
arXiv Detail & Related papers (2024-06-18T01:49:48Z) - 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) - Compiler for Distributed Quantum Computing: a Reinforcement Learning Approach [6.347685922582191]
We introduce a novel compiler that prioritizes reducing the expected execution time by jointly managing the generation and routing of EPR pairs.
We present a real-time, adaptive approach to compiler design, accounting for the nature of entanglement generation and the operational demands of quantum circuits.
Our contributions are twofold: (i) we model the optimal compiler for DQC using a Markov Decision Process (MDP) formulation, establishing the existence of an optimal algorithm, and (ii) we introduce a constrained Reinforcement Learning (RL) method to approximate this optimal compiler.
arXiv Detail & Related papers (2024-04-25T23:03:20Z) - Near-Term Distributed Quantum Computation using Mean-Field Corrections
and Auxiliary Qubits [77.04894470683776]
We propose near-term distributed quantum computing that involve limited information transfer and conservative entanglement production.
We build upon these concepts to produce an approximate circuit-cutting technique for the fragmented pre-training of variational quantum algorithms.
arXiv Detail & Related papers (2023-09-11T18:00:00Z) - 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) - 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) - Fast Swapping in a Quantum Multiplier Modelled as a Queuing Network [64.1951227380212]
We propose that quantum circuits can be modeled as queuing networks.
Our method is scalable and has the potential speed and precision necessary for large scale quantum circuit compilation.
arXiv Detail & Related papers (2021-06-26T10:55:52Z) - 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.