CutQAS: Topology-aware quantum circuit cutting via reinforcement learning

• URL: http://arxiv.org/abs/2504.04167v1
• Date: Sat, 05 Apr 2025 13:13:50 GMT
• Title: CutQAS: Topology-aware quantum circuit cutting via reinforcement learning
• Authors: Abhishek Sadhu, Aritra Sarkar, Akash Kundu,
• Abstract summary: We propose CutQAS, a framework that integrates quantum circuit cutting with quantum architecture search (QAS) to enhance quantum chemistry simulations.<n>First, an RL agent explores all possible topologies to identify an optimal circuit structure. Subsequently, a second RL agent refines the selected topology by determining optimal circuit cuts, ensuring efficient execution on constrained hardware.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Simulating molecular systems on quantum processors has the potential to surpass classical methods in computational resource efficiency. The limited qubit connectivity, small processor size, and short coherence times of near-term quantum hardware constrain the applicability of quantum algorithms like QPE and VQE. Quantum circuit cutting mitigates these constraints by partitioning large circuits into smaller subcircuits, enabling execution on resource-limited devices. However, finding optimal circuit partitions remains a significant challenge, affecting both computational efficiency and accuracy. To address these limitations, in this article, we propose CutQAS, a novel framework that integrates quantum circuit cutting with quantum architecture search (QAS) to enhance quantum chemistry simulations. Our framework employs a multi-step reinforcement learning (RL) agent to optimize circuit configurations. First, an RL agent explores all possible topologies to identify an optimal circuit structure. Subsequently, a second RL agent refines the selected topology by determining optimal circuit cuts, ensuring efficient execution on constrained hardware. Through numerical simulations, we demonstrate the effectiveness of our method in improving simulation accuracy and resource efficiency. This approach presents a scalable solution for quantum chemistry applications, offering a systematic pathway to overcoming hardware constraints in near-term quantum computing.

Related papers

• 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)
• Attention-Based Deep Reinforcement Learning for Qubit Allocation in Modular Quantum Architectures [1.8781124875646162]
  This research contributes to the advancement of scalable quantum computing systems by introducing a novel learning-based approach for efficient quantum circuit compilation and mapping. In this work, we propose a novel approach employing Deep Reinforcement Learning (DRL) methods to learn theses for a specific multi-core architecture.
  arXiv  Detail & Related papers  (2024-06-17T12:09:11Z)
• 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)
• 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)
• Reinforcement learning-assisted quantum architecture search for variational quantum algorithms [0.0]
  This thesis focuses on identifying functional quantum circuits in noisy quantum hardware. We introduce a tensor-based quantum circuit encoding, restrictions on environment dynamics to explore the search space of possible circuits efficiently. In dealing with various VQAs, our RL-based QAS outperforms existing QAS.
  arXiv  Detail & Related papers  (2024-02-21T12:30:39Z)
• Initial-State Dependent Optimization of Controlled Gate Operations with Quantum Computer [1.2019888796331233]
  We introduce a new circuit called AQCEL, which aims to remove redundant controlled operations from controlled gates. As a benchmark, the AQCEL is deployed on a quantum algorithm designed to model final state radiation in high energy physics. We have demonstrated that the AQCEL-optimized circuit can produce equivalent final states with much smaller number of gates.
  arXiv  Detail & Related papers  (2022-09-06T09:19:07Z)
• 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)
• Synergy Between Quantum Circuits and Tensor Networks: Short-cutting the Race to Practical Quantum Advantage [43.3054117987806]
  We introduce a scalable procedure for harnessing classical computing resources to provide pre-optimized initializations for quantum circuits. We show this method significantly improves the trainability and performance of PQCs on a variety of problems. By demonstrating a means of boosting limited quantum resources using classical computers, our approach illustrates the promise of this synergy between quantum and quantum-inspired models in quantum computing.
  arXiv  Detail & Related papers  (2022-08-29T15:24:03Z)
• Optimizing Tensor Network Contraction Using Reinforcement Learning [86.05566365115729]
  We propose a Reinforcement Learning (RL) approach combined with Graph Neural Networks (GNN) to address the contraction ordering problem. The problem is extremely challenging due to the huge search space, the heavy-tailed reward distribution, and the challenging credit assignment. We show how a carefully implemented RL-agent that uses a GNN as the basic policy construct can address these challenges.
  arXiv  Detail & Related papers  (2022-04-18T21:45:13Z)
• Quantum circuit debugging and sensitivity analysis via local inversions [62.997667081978825]
  We present a technique that pinpoints the sections of a quantum circuit that affect the circuit output the most. We demonstrate the practicality and efficacy of the proposed technique by applying it to example algorithmic circuits implemented on IBM quantum machines.
  arXiv  Detail & Related papers  (2022-04-12T19:39:31Z)
• 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)

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.