Improving the Reliability of Quantum Circuits by Evolving Heterogeneous Ensembles

• URL: http://arxiv.org/abs/2409.09103v1
• Date: Fri, 13 Sep 2024 12:38:17 GMT
• Title: Improving the Reliability of Quantum Circuits by Evolving Heterogeneous Ensembles
• Authors: Owain Parry, John Clark, Phil McMinn,
• Abstract summary: Quantum computers can perform certain operations exponentially faster than classical computers, but designing quantum circuits is challenging. We use evolutionary algorithms to produce probabilistic quantum circuits that give the correct output more often than not for any input. Our results indicate that evolving heterogeneous ensembles is an effective strategy for improving the reliability of quantum circuits.
• Score: 4.014524824655107
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum computers can perform certain operations exponentially faster than classical computers, but designing quantum circuits is challenging. To that end, researchers used evolutionary algorithms to produce probabilistic quantum circuits that give the correct output more often than not for any input. They can be executed multiple times, with the outputs combined using a classical method (such as voting) to produce the final output, effectively creating a homogeneous ensemble of circuits (i.e., all identical). Inspired by n-version programming and ensemble learning, we developed a tool that uses an evolutionary algorithm to generate heterogeneous ensembles of circuits (i.e., all different), named QuEEn. We used it to evolve ensembles to solve the Iris classification problem. When using ideal simulation, we found the performance of heterogeneous ensembles to be greater than that of homogeneous ensembles to a statistically significant degree. When using noisy simulation, we still observed a statistically significant improvement in the majority of cases. Our results indicate that evolving heterogeneous ensembles is an effective strategy for improving the reliability of quantum circuits. This is particularly relevant in the current NISQ era of quantum computing where computers do not yet have good tolerance to quantum noise.

Related papers

• Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits [63.733312560668274]
  Given a quantum circuit containing d tunable RZ gates and G-d Clifford gates, can a learner perform purely classical inference to efficiently predict its linear properties? We prove that the sample complexity scaling linearly in d is necessary and sufficient to achieve a small prediction error, while the corresponding computational complexity may scale exponentially in d. We devise a kernel-based learning model capable of trading off prediction error and computational complexity, transitioning from exponential to scaling in many practical settings.
  arXiv  Detail & Related papers  (2024-08-22T08:21:28Z)
• Compact quantum algorithms for time-dependent differential equations [0.0]
  We build on an idea based on linear combination of unitaries to simulate non-unitary, non-Hermitian quantum systems. We generate hybrid quantum-classical algorithms that efficiently perform iterative matrix-vector multiplication and matrix inversion operations.
  arXiv  Detail & Related papers  (2024-05-16T02:14:58Z)
• Quantum Subroutine for Variance Estimation: Algorithmic Design and Applications [80.04533958880862]
  Quantum computing sets the foundation for new ways of designing algorithms. New challenges arise concerning which field quantum speedup can be achieved. Looking for the design of quantum subroutines that are more efficient than their classical counterpart poses solid pillars to new powerful quantum algorithms.
  arXiv  Detail & Related papers  (2024-02-26T09:32:07Z)
• Adaptive Circuit Learning of Born Machine: Towards Realization of Amplitude Embedding and Data Loading [7.88657961743755]
  We present a novel algorithm "Adaptive Circuit Learning of Born Machine" (ACLBM) Our algorithm is tailored to selectively integrate two-qubit entangled gates that best capture the complex entanglement present within the target state. Empirical results underscore the proficiency of our approach in encoding real-world data through amplitude embedding.
  arXiv  Detail & Related papers  (2023-11-29T16:47:31Z)
• Quantum Clustering with k-Means: a Hybrid Approach [117.4705494502186]
  We design, implement, and evaluate three hybrid quantum k-Means algorithms. We exploit quantum phenomena to speed up the computation of distances. We show that our hybrid quantum k-Means algorithms can be more efficient than the classical version.
  arXiv  Detail & Related papers  (2022-12-13T16:04:16Z)
• A single $T$-gate makes distribution learning hard [56.045224655472865]
  This work provides an extensive characterization of the learnability of the output distributions of local quantum circuits. We show that for a wide variety of the most practically relevant learning algorithms -- including hybrid-quantum classical algorithms -- even the generative modelling problem associated with depth $d=omega(log(n))$ Clifford circuits is hard.
  arXiv  Detail & Related papers  (2022-07-07T08:04:15Z)
• Estimating the randomness of quantum circuit ensembles up to 50 qubits [9.775777593425452]
  We show that the ability of random circuits to approximate any random unitaries has consequences on their complexity, expressibility, and trainability. Our work shows that large-scale tensor network simulations could provide important hints toward open problems in quantum information science.
  arXiv  Detail & Related papers  (2022-05-19T23:43:15Z)
• 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 Ensemble for Classification [2.064612766965483]
  A powerful way to improve performance in machine learning is to construct an ensemble that combines the predictions of multiple models. We propose a new quantum algorithm that exploits quantum superposition, entanglement and interference to build an ensemble of classification models.
  arXiv  Detail & Related papers  (2020-07-02T11:26:54Z)
• Efficient classical simulation of random shallow 2D quantum circuits [104.50546079040298]
  Random quantum circuits are commonly viewed as hard to simulate classically. We show that approximate simulation of typical instances is almost as hard as exact simulation. We also conjecture that sufficiently shallow random circuits are efficiently simulable more generally.
  arXiv  Detail & Related papers  (2019-12-31T19:00:00Z)

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.