Optimized Quantum Program Execution Ordering to Mitigate Errors in Simulations of Quantum Systems

• URL: http://arxiv.org/abs/2203.12713v1
• Date: Wed, 23 Mar 2022 20:22:08 GMT
• Title: Optimized Quantum Program Execution Ordering to Mitigate Errors in Simulations of Quantum Systems
• Abstract summary: Hamiltonian Simulation (HS) is an important and interesting problem across physics and chemistry. For this task, algorithms that run on quantum computers are known to be exponentially faster than classical algorithms. Our work proposes a compilation strategy that simultaneously advances both goals.
• Score: 6.945601123742983
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Simulating the time evolution of a physical system at quantum mechanical levels of detail -- known as Hamiltonian Simulation (HS) -- is an important and interesting problem across physics and chemistry. For this task, algorithms that run on quantum computers are known to be exponentially faster than classical algorithms; in fact, this application motivated Feynman to propose the construction of quantum computers. Nonetheless, there are challenges in reaching this performance potential. Prior work has focused on compiling circuits (quantum programs) for HS with the goal of maximizing either accuracy or gate cancellation. Our work proposes a compilation strategy that simultaneously advances both goals. At a high level, we use classical optimizations such as graph coloring and travelling salesperson to order the execution of quantum programs. Specifically, we group together mutually commuting terms in the Hamiltonian (a matrix characterizing the quantum mechanical system) to improve the accuracy of the simulation. We then rearrange the terms within each group to maximize gate cancellation in the final quantum circuit. These optimizations work together to improve HS performance and result in an average 40% reduction in circuit depth. This work advances the frontier of HS which in turn can advance physical and chemical modeling in both basic and applied sciences.

Related papers

• Quantum Simulation and Energy Estimation for Discretized Anharmonic oscillator [1.0079626733116613]
  A quantum circuit with a filter-based design and Toffoli gates is constructed to track quantum state evolution.<n>For energy estimation, the Variational Quantum Eigensolver (VQE) with a TwoLocal ansatz and variational Quantum Deflation (VQD) are used.
  arXiv  Detail & Related papers  (2025-09-25T08:49:09Z)
• Digital quantum simulation of many-body systems: Making the most of intermediate-scale, noisy quantum computers [51.56484100374058]
  This thesis is centered around simulating quantum dynamics on quantum devices.<n>We present an overview of the most relevant quantum algorithms for quantum dynamics.<n>We identify relevant problems within quantum dynamics that could benefit from quantum simulation in the near future.
  arXiv  Detail & Related papers  (2025-08-29T10:37:19Z)
• Correcting and extending Trotterized quantum many-body dynamics [0.0]
  We develop a hybrid ansatz that combines the strengths of quantum and classical methods. We show how this hybrid ansatz can avoid SWAP gates in the quantum circuit. We also show how it can extend the system size while keeping the number of qubits on the quantum device constant.
  arXiv  Detail & Related papers  (2025-02-19T14:50:12Z)
• Improved Quantum Computation using Operator Backpropagation [5.066470263702309]
  We show a framework that integrates classical simulations with quantum hardware to improve the computation of an observable's expectation value. In this framework, a quantum circuit is partitioned into two subcircuits: one that describes the backpropagated Heisenberg evolution of an observable, executed on a classical computer, and the other is a Schr"odinger evolution run on quantum processors. We demonstrate the effectiveness of this method on a Hamiltonian simulation problem, achieving more accurate expectation value estimates compared to using quantum hardware alone.
  arXiv  Detail & Related papers  (2025-02-04T00:03:07Z)
• Optimising Iteration Scheduling for Full-State Vector Simulation of Quantum Circuits on FPGAs [1.221089353510972]
  We present a memory access pattern to optimise the number of iterations that need to be scheduled to execute a quantum gate. We show that this approach results in a significant reduction in the time required to simulate a gate for each added control qubit.
  arXiv  Detail & Related papers  (2024-11-27T13:57:29Z)
• NN-AE-VQE: Neural network parameter prediction on autoencoded variational quantum eigensolvers [1.7400502482492273]
  In recent years, the field of quantum computing has become significantly more mature. We present an auto-encoded VQE with neural-network predictions: NN-AE-VQE. We demonstrate these methods on a $H$ molecule, achieving chemical accuracy.
  arXiv  Detail & Related papers  (2024-11-23T23:09:22Z)
• Q-gen: A Parameterized Quantum Circuit Generator [0.6062751776009752]
  We introduce Q-gen, a high-level, parameterized quantum circuit generator incorporating 15 realistic quantum algorithms. Q-gen is an open-source project that serves as the entrance for users with a classical computer science background to dive into the world of quantum computing.
  arXiv  Detail & Related papers  (2024-07-26T12:22:40Z)
• QNEAT: Natural Evolution of Variational Quantum Circuit Architecture [95.29334926638462]
  We focus on variational quantum circuits (VQC), which emerged as the most promising candidates for the quantum counterpart of neural networks. Although showing promising results, VQCs can be hard to train because of different issues, e.g., barren plateau, periodicity of the weights, or choice of architecture. We propose a gradient-free algorithm inspired by natural evolution to optimize both the weights and the architecture of the VQC.
  arXiv  Detail & Related papers  (2023-04-14T08:03:20Z)
• Quantum Machine Learning: from physics to software engineering [58.720142291102135]
  We show how classical machine learning approach can help improve the facilities of quantum computers. We discuss how quantum algorithms and quantum computers may be useful for solving classical machine learning tasks.
  arXiv  Detail & Related papers  (2023-01-04T23:37:45Z)
• Quantum Simulation on Noisy Superconducting Quantum Computers [0.0]
  Quantum simulation is a potentially powerful application of quantum computing. There is little introductory literature or demonstrations of the topic at a graduate or undergraduate student level. This artificially raises the barrier to entry into the field which already has a limited workforce.
  arXiv  Detail & Related papers  (2022-09-06T19:44:18Z)
• Quantum algorithms for quantum dynamics: A performance study on the spin-boson model [68.8204255655161]
  Quantum algorithms for quantum dynamics simulations are traditionally based on implementing a Trotter-approximation of the time-evolution operator. variational quantum algorithms have become an indispensable alternative, enabling small-scale simulations on present-day hardware. We show that, despite providing a clear reduction of quantum gate cost, the variational method in its current implementation is unlikely to lead to a quantum advantage.
  arXiv  Detail & Related papers  (2021-08-09T18:00:05Z)
• A semi-agnostic ansatz with variable structure for quantum machine learning [0.3774866290142281]
  Variational Quantum Algorithms (VQAs) offer a powerful, flexible paradigm for programming near-term quantum computers. We present a variable structure approach to build ansatzes for VQAs. We employ VAns in the variational quantum eigensolver for condensed matter and quantum chemistry applications.
  arXiv  Detail & Related papers  (2021-03-11T14:58:40Z)
• Quantum walk processes in quantum devices [55.41644538483948]
  We study how to represent quantum walk on a graph as a quantum circuit. Our approach paves way for the efficient implementation of quantum walks algorithms on quantum computers.
  arXiv  Detail & Related papers  (2020-12-28T18:04:16Z)
• Electronic structure with direct diagonalization on a D-Wave quantum annealer [62.997667081978825]
  This work implements the general Quantum Annealer Eigensolver (QAE) algorithm to solve the molecular electronic Hamiltonian eigenvalue-eigenvector problem on a D-Wave 2000Q quantum annealer. We demonstrate the use of D-Wave hardware for obtaining ground and electronically excited states across a variety of small molecular systems.
  arXiv  Detail & Related papers  (2020-09-02T22:46:47Z)
• Quantum-optimal-control-inspired ansatz for variational quantum algorithms [105.54048699217668]
  A central component of variational quantum algorithms (VQA) is the state-preparation circuit, also known as ansatz or variational form. Here, we show that this approach is not always advantageous by introducing ans"atze that incorporate symmetry-breaking unitaries. This work constitutes a first step towards the development of a more general class of symmetry-breaking ans"atze with applications to physics and chemistry problems.
  arXiv  Detail & Related papers  (2020-08-03T18:00:05Z)
• Quantum Solver of Contracted Eigenvalue Equations for Scalable Molecular Simulations on Quantum Computing Devices [0.0]
  We introduce a quantum solver of contracted eigenvalue equations, the quantum analogue of classical methods for the energies. We demonstrate the algorithm though computations on both a quantum simulator and two IBM quantum processing units.
  arXiv  Detail & Related papers  (2020-04-23T18:35:26Z)

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.