PIMutation: Exploring the Potential of PIM Architecture for Quantum Circuit Simulation

• URL: http://arxiv.org/abs/2503.00668v1
• Date: Sat, 01 Mar 2025 23:48:12 GMT
• Title: PIMutation: Exploring the Potential of PIM Architecture for Quantum Circuit Simulation
• Abstract summary: We introduce PIMutation (PIM framework for qUanTum circuit simulATION) for achieving fast and energy-efficient quantum circuit simulation.<n> PIMutation incorporates three optimization strategies to overcome the overhead of quantum circuit simulation using the real PIM system.<n>Our evaluations show that PIMutation achieves an average speedup of 2.99x and 16.51x with a reduction of energy of 25.23% and 75.29% over the QuEST simulator on CPU.
• Score: 3.981093560880829
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum circuit simulations are essential for the verification of quantum algorithms on behalf of real quantum devices. However, the memory requirements for such simulations grow exponentially with the number of qubits involved in quantum programs. Moreover, a substantial number of computations in quantum circuit simulations cause low locality data accesses, as they require extensive computations across the entire table of the full state vector. These characteristics lead to significant latency and energy overheads during data transfers between the CPU and main memory. Processing-in-Memory (PIM), which integrates computational logic near DRAM banks, could present a promising solution to address these challenges. In this paper, we introduce PIMutation (PIM framework for qUanTum circuit simulATION) for achieving fast and energy-efficient quantum circuit simulation. PIMutation is the first attempt to leverage UPMEM, a publicly available PIM-integrated DIMM, to implement quantum circuit simulations. PIMutation incorporates three optimization strategies to overcome the overhead of quantum circuit simulation using the real PIM system: (i) gate merging, (ii) row swapping, and (iii) vector partitioning. Our evaluations show that PIMutation achieves an average speedup of 2.99x and 16.51x with a reduction of energy of 25.23% and 75.29% over the QuEST simulator on CPU in 16- and 32-qubit benchmarks, respectively.

Related papers

• Large-Scale Quantum Circuit Simulation on HPC Cluster via Cache Blocking, Boosting, and Gate Fusion Optimization [5.552529422486661]
  Quantum circuit simulation is crucial for the development of quantum algorithms.<n>Full-state quantum circuit simulation poses challenges because of the exponential increase in simulation time for large quantum systems.<n>We propose a framework designed to enhance simulation performance by optimizing both data locality and computational efficiency.
  arXiv  Detail & Related papers  (2026-04-14T04:07:24Z)
• SparQSim: Simulating Scalable Quantum Algorithms via Sparse Quantum State Representations [3.7112784544167248]
  We present SparQSim, a quantum simulator implemented in C++ inspired by the Feynman-based method. SparQSim operates at the register level by storing only the nonzero components of the quantum state.
  arXiv  Detail & Related papers  (2025-03-19T11:23:26Z)
• Circuit Partitioning and Full Circuit Execution: A Comparative Study of GPU-Based Quantum Circuit Simulation [0.0]
  Executing large quantum circuits is not feasible using the currently available NISQ (noisy intermediate-scale quantum) devices. This study presents a comparative analysis of two simulation methods: circuit-splitting and full-circuit execution using distributed memory. Results indicate that full-circuit executions are faster than circuit-splitting for simulations performed on a single node.
  arXiv  Detail & Related papers  (2025-02-17T03:04:43Z)
• Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits [62.46800898243033]
  Recent progress in quantum learning theory prompts a question: can linear properties of a large-qubit circuit be efficiently learned from measurement data generated by varying classical inputs?<n>We prove that the sample complexity scaling linearly in $d$ is required to achieve a small prediction error, while the corresponding computational complexity may scale exponentially in d.<n>We propose a kernel-based method leveraging classical shadows and truncated trigonometric expansions, enabling a controllable trade-off between prediction accuracy and computational overhead.
  arXiv  Detail & Related papers  (2024-08-22T08:21:28Z)
• Simulator Demonstration of Large Scale Variational Quantum Algorithm on HPC Cluster [0.0]
  This study aims to accelerate quantum simulation using two newly proposed methods. We achieved 200 times higher speed over VQE simulations and demonstrated 32 qubits ground-state energy calculations in acceptable time.
  arXiv  Detail & Related papers  (2024-02-19T06:34:01Z)
• State of practice: evaluating GPU performance of state vector and tensor network methods [2.4851820343103035]
  This article investigates the limits of current state-of-the-art simulation techniques on a test bench made of eight widely used quantum subroutines.<n>We highlight how to select the best simulation strategy, obtaining a speedup of up to an order of magnitude.
  arXiv  Detail & Related papers  (2024-01-11T09:22:21Z)
• Deep Quantum Circuit Simulations of Low-Energy Nuclear States [51.823503818486394]
  We present advances in high-performance numerical simulations of deep quantum circuits. circuits up to 21 qubits and more than 115,000,000 gates can be efficiently simulated.
  arXiv  Detail & Related papers  (2023-10-26T19:10:58Z)
• A self-consistent field approach for the variational quantum eigensolver: orbital optimization goes adaptive [52.77024349608834]
  We present a self consistent field approach (SCF) within the Adaptive Derivative-Assembled Problem-Assembled Ansatz Variational Eigensolver (ADAPTVQE) This framework is used for efficient quantum simulations of chemical systems on nearterm quantum computers.
  arXiv  Detail & Related papers  (2022-12-21T23:15:17Z)
• A Reorder Trick for Decision Diagram Based Quantum Circuit Simulation [0.4358626952482686]
  We study two classes of quantum circuits on which the state-of-the-art decision diagram based simulators failed to perform well in terms of simulation time. We propose a simple and powerful reorder trick to boost the simulation of such quantum circuits.
  arXiv  Detail & Related papers  (2022-11-14T04:55:25Z)
• 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)
• QSAN: A Near-term Achievable Quantum Self-Attention Network [73.15524926159702]
  Self-Attention Mechanism (SAM) is good at capturing the internal connections of features. A novel Quantum Self-Attention Network (QSAN) is proposed for image classification tasks on near-term quantum devices.
  arXiv  Detail & Related papers  (2022-07-14T12:22:51Z)
• Recompilation-enhanced simulation of electron-phonon dynamics on IBM Quantum computers [62.997667081978825]
  We consider the absolute resource cost for gate-based quantum simulation of small electron-phonon systems. We perform experiments on IBM quantum hardware for both weak and strong electron-phonon coupling. Despite significant device noise, through the use of approximate circuit recompilation we obtain electron-phonon dynamics on current quantum computers comparable to exact diagonalisation.
  arXiv  Detail & Related papers  (2022-02-16T19:00:00Z)
• Parallel Simulation of Quantum Networks with Distributed Quantum State Management [56.24769206561207]
  We identify requirements for parallel simulation of quantum networks and develop the first parallel discrete event quantum network simulator. Our contributions include the design and development of a quantum state manager that maintains shared quantum information distributed across multiple processes. We release the parallel SeQUeNCe simulator as an open-source tool alongside the existing sequential version.
  arXiv  Detail & Related papers  (2021-11-06T16:51:17Z)
• Tensor Network Quantum Virtual Machine for Simulating Quantum Circuits at Exascale [57.84751206630535]
  We present a modernized version of the Quantum Virtual Machine (TNQVM) which serves as a quantum circuit simulation backend in the e-scale ACCelerator (XACC) framework. The new version is based on the general purpose, scalable network processing library, ExaTN, and provides multiple quantum circuit simulators. By combining the portable XACC quantum processors and the scalable ExaTN backend we introduce an end-to-end virtual development environment which can scale from laptops to future exascale platforms.
  arXiv  Detail & Related papers  (2021-04-21T13:26:42Z)

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.