Graph-based identification of qubit network (GidNET) for qubit reuse
- URL: http://arxiv.org/abs/2410.08817v1
- Date: Fri, 11 Oct 2024 14:00:11 GMT
- Title: Graph-based identification of qubit network (GidNET) for qubit reuse
- Authors: Gideon Uchehara, Tor M. Aamodt, Olivia Di Matteo,
- Abstract summary: GidNET is an algorithm for optimizing qubit reuse in quantum circuits.
It consistently outperforms Qiskit in circuit width reduction.
It offers a solution for quantum computers with limited numbers of qubits.
- Score: 9.435498822573734
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Quantum computing introduces the challenge of optimizing quantum resources crucial for executing algorithms within the limited qubit availability of current quantum architectures. Existing qubit reuse algorithms face a trade-off between optimality and scalability, with some achieving optimal reuse but limited scalability due to computational complexities, while others exhibit reduced runtime at the expense of optimality. This paper introduces GidNET (Graph-based Identification of qubit NETwork), an algorithm for optimizing qubit reuse in quantum circuits. By analyzing the circuit's Directed Acyclic Graph (DAG) representation and its corresponding candidate matrix, GidNET identifies higher-quality pathways for qubit reuse more efficiently. Through a comparative study with established algorithms, notably QNET [1], GidNET not only achieves a consistent reduction in compiled circuit widths by a geometric mean of 4.4%, reaching up to 21% in larger circuits, but also demonstrates enhanced computational speed and scaling, with average execution time reduction of 97.4% (i.e., 38.5X geometric mean speedup) and up to 99.3% (142.9X speedup) across various circuit sizes. Furthermore, GidNET consistently outperforms Qiskit in circuit width reduction, achieving an average improvement of 59.3%, with maximum reductions of up to 72% in the largest tested circuits. These results demonstrate GidNET's ability to improve circuit width and runtime, offering a solution for quantum computers with limited numbers of qubits.
Related papers
- Optimization and Synthesis of Quantum Circuits with Global Gates [44.99833362998488]
We use global interactions, such as the Global Molmer-Sorensen gate present in ion trap hardware, to optimize and synthesize quantum circuits.<n>The algorithm is based on the ZX-calculus and uses a specialized circuit extraction routine that groups entangling gates into Global MolmerSorensen gates.<n>We benchmark the algorithm in a variety of circuits, and show how it improves their performance under state-of-the-art hardware considerations.
arXiv Detail & Related papers (2025-07-28T10:25:31Z) - Improving the Performance of Digitized Counterdiabatic Quantum Optimization via Algorithm-Oriented Qubit Mapping [0.4681661603096333]
This paper presents strategies to improve the performance of digitized counterdiabatic quantum optimization algorithms.
Our approach increases the approximation ratio by an average of 4.49$times$ without error mitigation.
Our findings provide valuable insights into the codesign of algorithm implementation, tailored to optimize qubit mapping and algorithm parameters.
arXiv Detail & Related papers (2023-11-24T17:39:08Z) - Powerful Quantum Circuit Resizing with Resource Efficient Synthesis [0.4980726355048842]
This paper introduces two such algorithms.
The first one leverages gate-dependency rules to reduce qubit count by 61.6% or 45.3% when optimizing depth as well.
The second algorithm finds resizing opportunities in previously unresizable circuits via dependency rules and other state-of-the-art tools.
arXiv Detail & Related papers (2023-11-22T02:18:34Z) - Large-scale quantum approximate optimization on non-planar graphs with machine learning noise mitigation [0.46040036610482665]
Error mitigation extends the size of the quantum circuits that noisy devices can meaningfully execute.
We show a quantum approximate optimization algorithm (QAOA) on non-planar random regular graphs with up to 40 nodes enabled by a machine learning-based error mitigation.
arXiv Detail & Related papers (2023-07-26T18:00:07Z) - Qubit efficient quantum algorithms for the vehicle routing problem on
NISQ processors [48.68474702382697]
Vehicle routing problem with time windows (VRPTW) is a common optimization problem faced within the logistics industry.
In this work, we explore the use of a previously-introduced qubit encoding scheme to reduce the number of binary variables.
arXiv Detail & Related papers (2023-06-14T13:44:35Z) - Quantized Neural Networks for Low-Precision Accumulation with Guaranteed
Overflow Avoidance [68.8204255655161]
We introduce a quantization-aware training algorithm that guarantees avoiding numerical overflow when reducing the precision of accumulators during inference.
We evaluate our algorithm across multiple quantized models that we train for different tasks, showing that our approach can reduce the precision of accumulators while maintaining model accuracy with respect to a floating-point baseline.
arXiv Detail & Related papers (2023-01-31T02:46:57Z) - Robust Qubit Mapping Algorithm via Double-Source Optimal Routing on Large Quantum Circuits [11.391158217994782]
Duostra is tailored to address the challenge of implementing large-scale quantum circuits on real hardware devices.
It operates by efficiently determining optimal paths for double-qubit gates and inserting SWAP gates.
It yields results of good quality within a reasonable runtime.
arXiv Detail & Related papers (2022-10-04T01:47:11Z) - Wide Quantum Circuit Optimization with Topology Aware Synthesis [0.8469686352132708]
Unitary synthesis is an optimization technique that can achieve optimal multi-qubit gate counts while mapping quantum circuits to restrictive qubit topologies.
We present TopAS, a topology aware synthesis tool built with the emphBQSKit framework that preconditions quantum circuits before mapping.
arXiv Detail & Related papers (2022-06-27T21:59:30Z) - OMPQ: Orthogonal Mixed Precision Quantization [64.59700856607017]
Mixed precision quantization takes advantage of hardware's multiple bit-width arithmetic operations to unleash the full potential of network quantization.
We propose to optimize a proxy metric, the concept of networkity, which is highly correlated with the loss of the integer programming.
This approach reduces the search time and required data amount by orders of magnitude, with little compromise on quantization accuracy.
arXiv Detail & Related papers (2021-09-16T10:59:33Z) - 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) - Effective and Fast: A Novel Sequential Single Path Search for
Mixed-Precision Quantization [45.22093693422085]
Mixed-precision quantization model can match different quantization bit-precisions according to the sensitivity of different layers to achieve great performance.
It is a difficult problem to quickly determine the quantization bit-precision of each layer in deep neural networks according to some constraints.
We propose a novel sequential single path search (SSPS) method for mixed-precision quantization.
arXiv Detail & Related papers (2021-03-04T09:15:08Z) - 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) - Channel Assignment in Uplink Wireless Communication using Machine
Learning Approach [54.012791474906514]
This letter investigates a channel assignment problem in uplink wireless communication systems.
Our goal is to maximize the sum rate of all users subject to integer channel assignment constraints.
Due to high computational complexity, machine learning approaches are employed to obtain computational efficient solutions.
arXiv Detail & Related papers (2020-01-12T15:54:20Z)
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.