Parity Quantum Optimization: Benchmarks

• URL: http://arxiv.org/abs/2105.06240v2
• Date: Wed, 8 Mar 2023 13:36:42 GMT
• Title: Parity Quantum Optimization: Benchmarks
• Authors: Michael Fellner, Kilian Ender, Roeland ter Hoeven, Wolfgang Lechner
• Abstract summary: We analyse the gate resources required to implement a single QAOA cycle for real-world scenarios. We consider random spin models with higher order terms, as well as the problems of predicting financial crashes and finding the ground states of electronic structure Hamiltonians.
• Score: 0.4499833362998487
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present benchmarks of the parity transformation for the Quantum Approximate Optimization Algorithm (QAOA). We analyse the gate resources required to implement a single QAOA cycle for real-world scenarios. In particular, we consider random spin models with higher order terms, as well as the problems of predicting financial crashes and finding the ground states of electronic structure Hamiltonians. For the spin models studied our findings imply a significant advantage of the parity mapping compared to the standard gate model. In combination with full parallelizability of gates this has the potential to boost the race for demonstrating quantum advantage.

Related papers

• An Introduction to the Quantum Approximate Optimization Algorithm [51.56484100374058]
  The tutorial begins by outlining variational quantum circuits and QUBO problems.<n>Next, it explores the QAOA in detail, covering its Hamiltonian formulation, gate decomposition, and example applications.<n>The tutorial extends these concepts to higher-order Hamiltonians and discussing the associated symmetries and circuit construction.
  arXiv  Detail & Related papers  (2025-11-23T09:54:20Z)
• Extending QAOA-GPT to Higher-Order Quantum Optimization Problems [0.0]
  We extend QAOA-GPT to Higher-Order Unconstrained Binary Optimization problems.<n>We train the model on graph-circuit pairs generated via ADAPT-QAOA and evaluate its performance on 8- and 16-qubit instances embedded on heavy-hex lattices.<n>Results demonstrate that QAOA-GPT generalizes effectively to higher-order cost Hamiltonians and complex energy landscapes.
  arXiv  Detail & Related papers  (2025-11-10T18:46:38Z)
• Approximate Quantum State Preparation with Tree-Based Bayesian Optimization Surrogates [4.946006905837039]
  We study the problem of approximate state preparation on near-term quantum computers.<n>The goal is to construct a parameterized circuit that reproduces the output distribution of a target quantum state while minimizing resource overhead.<n>We propose CircuitTree, a surrogate-guided optimization framework based on Bayesian Optimization with tree-based models.
  arXiv  Detail & Related papers  (2025-09-30T18:19:37Z)
• Scalable Parity Architecture With a Shuttling-Based Spin Qubit Processor [0.32985979395737786]
  We present sequences of spin shuttling and quantum gates that implement the Parity Quantum Approximate Optimization Algorithm (QAOA) We develop a detailed error model for a hardware-specific analysis of the Parity Architecture. We find that with high-fidelity spin shuttling the performance of the spin qubits is competitive or even exceeds the results of the transmons.
  arXiv  Detail & Related papers  (2024-03-14T17:06:50Z)
• A Framework for Demonstrating Practical Quantum Advantage: Racing Quantum against Classical Generative Models [62.997667081978825]
  We build over a proposed framework for evaluating the generalization performance of generative models. We establish the first comparative race towards practical quantum advantage (PQA) between classical and quantum generative models. Our results suggest that QCBMs are more efficient in the data-limited regime than the other state-of-the-art classical generative models.
  arXiv  Detail & Related papers  (2023-03-27T22:48:28Z)
• Evaluating the Convergence of Tabu Enhanced Hybrid Quantum Optimization [58.720142291102135]
  We introduce the Tabu Enhanced Hybrid Quantum Optimization metaheuristic approach useful for optimization problem solving on a quantum hardware. We address the theoretical convergence of the proposed scheme from the viewpoint of the collisions in the object which stores the tabu states, based on the Ising model.
  arXiv  Detail & Related papers  (2022-09-05T07:23:03Z)
• Symmetric Pruning in Quantum Neural Networks [111.438286016951]
  Quantum neural networks (QNNs) exert the power of modern quantum machines. QNNs with handcraft symmetric ansatzes generally experience better trainability than those with asymmetric ansatzes. We propose the effective quantum neural tangent kernel (EQNTK) to quantify the convergence of QNNs towards the global optima.
  arXiv  Detail & Related papers  (2022-08-30T08:17:55Z)
• Theory of Quantum Generative Learning Models with Maximum Mean Discrepancy [67.02951777522547]
  We study learnability of quantum circuit Born machines (QCBMs) and quantum generative adversarial networks (QGANs) We first analyze the generalization ability of QCBMs and identify their superiorities when the quantum devices can directly access the target distribution. Next, we prove how the generalization error bound of QGANs depends on the employed Ansatz, the number of qudits, and input states.
  arXiv  Detail & Related papers  (2022-05-10T08:05:59Z)
• Quantum Approximate Optimization Algorithm applied to the binary perceptron [0.46664938579243564]
  We apply digitized Quantum Annealing (QA) and Quantum Approximate Optimization Algorithm (QAOA) to a paradigmatic task of supervised learning in artificial neural networks. We provide evidence for the existence of optimal smooth solutions for the QAOA parameters, which are transferable among typical instances of the same problem. We prove numerically an enhanced performance of QAOA over traditional QA.
  arXiv  Detail & Related papers  (2021-12-19T18:33:22Z)
• Parent Hamiltonian as a benchmark problem for variational quantum eigensolvers [0.6946929968559495]
  Variational quantum eigensolver (VQE) finds a ground state of a given Hamiltonian by variationally optimizing the parameters of quantum circuits called ansatz. This work provides a systematic way to analyze energies for VQE and contribute to the design of ansatz and its initial parameters.
  arXiv  Detail & Related papers  (2021-09-24T06:09:10Z)
• Benchmarking variational quantum eigensolvers for the square-octagon-lattice Kitaev model [3.6810704401578724]
  Quantum spin systems may offer the first opportunities for beyond-classical quantum computations of scientific interest. The variational quantum eigensolver (VQE) is a promising approach to finding energy eigenvalues on noisy quantum computers. We demonstrate the implementation of HVA circuits on Rigetti's Aspen-9 chip with error mitigation.
  arXiv  Detail & Related papers  (2021-08-30T16:58:43Z)
• Realization of arbitrary doubly-controlled quantum phase gates [62.997667081978825]
  We introduce a high-fidelity gate set inspired by a proposal for near-term quantum advantage in optimization problems. By orchestrating coherent, multi-level control over three transmon qutrits, we synthesize a family of deterministic, continuous-angle quantum phase gates acting in the natural three-qubit computational basis.
  arXiv  Detail & Related papers  (2021-08-03T17:49:09Z)
• 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)

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.