Quantum-enhanced Markov Chain Monte Carlo for systems larger than your Quantum Computer

• URL: http://arxiv.org/abs/2405.04247v2
• Date: Mon, 13 Jan 2025 13:52:27 GMT
• Title: Quantum-enhanced Markov Chain Monte Carlo for systems larger than your Quantum Computer
• Authors: Stuart Ferguson, Petros Wallden,
• Abstract summary: We introduce a framework to coarse grain the algorithm in such a way that the quantum computation can be performed using considerably smaller quantum computers. Our method can be easily combined with other classical and quantum techniques and is adaptable to various quantum hardware specifications.
• Score: 0.9208007322096533
• License:
• Abstract: Quantum computers theoretically promise computational advantage in many tasks, but it is much less clear how such advantage can be maintained when using existing and near-term hardware that has limitations in the number and quality of its qubits. Layden et al. [Nature 619, 282 (2023)] proposed a promising application by introducing a Quantum-enhanced Markov Chain Monte Carlo (QeMCMC) approach to reduce the thermalization time required when sampling from hard probability distributions. In QeMCMC the size of the required quantum computer scales linearly with the problem, putting limitations on the sizes of systems that one can consider. In this work we introduce a framework to coarse grain the algorithm in such a way that the quantum computation can be performed using considerably smaller quantum computers and we term the method the Coarse Grained Quantum-enhanced Markov Chain Monte Carlo (CGQeMCMC). Example strategies within this framework are put to the test, with the quantum speedup persisting while using only $\sqrt{n}$ simulated qubits where $n$ is the number of qubits required in the original QeMCMC -- a quadratic reduction in resources. The coarse graining framework has the potential to be practically applicable in the near term as it requires very few qubits to approach classically intractable problem instances; in this case only 6 simulated qubits suffice to gain advantage compared to standard classical approaches when investigating the magnetization of a 36 spin system. Our method can be easily combined with other classical and quantum techniques and is adaptable to various quantum hardware specifications -- in particular those with limited connectivity.

Related papers

• Parallel Quantum Computing Simulations via Quantum Accelerator Platform Virtualization [44.99833362998488]
  We present a model for parallelizing simulation of quantum circuit executions. The model can take advantage of its backend-agnostic features, enabling parallel quantum circuit execution over any target backend.
  arXiv  Detail & Related papers  (2024-06-05T17:16:07Z)
• Quantum Dynamical Hamiltonian Monte Carlo [0.0]
  A ubiquitous problem in machine learning is sampling from probability distributions that we only have access to via their log probability. We extend the well-known Hamiltonian Monte Carlo (HMC) method for Chain Monte Carlo (MCMC) sampling to leverage quantum computation in a hybrid manner.
  arXiv  Detail & Related papers  (2024-03-04T07:08:23Z)
• A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity [50.387179833629254]
  We introduce a collaborative classical-quantum architecture called co-TenQu. Co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting. It outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.
  arXiv  Detail & Related papers  (2024-02-23T14:09:41Z)
• QAOA-MC: Markov chain Monte Carlo enhanced by Quantum Alternating Operator Ansatz [0.6181093777643575]
  We propose the use of Quantum Alternating Operator Ansatz (QAOA) for quantum-enhanced Monte Carlo. This work represents an important step toward realizing practical quantum advantage with currently available quantum computers.
  arXiv  Detail & Related papers  (2023-05-15T16:47:31Z)
• Towards Neural Variational Monte Carlo That Scales Linearly with System Size [67.09349921751341]
  Quantum many-body problems are central to demystifying some exotic quantum phenomena, e.g., high-temperature superconductors. The combination of neural networks (NN) for representing quantum states, and the Variational Monte Carlo (VMC) algorithm, has been shown to be a promising method for solving such problems. We propose a NN architecture called Vector-Quantized Neural Quantum States (VQ-NQS) that utilizes vector-quantization techniques to leverage redundancies in the local-energy calculations of the VMC algorithm.
  arXiv  Detail & Related papers  (2022-12-21T19:00:04Z)
• Optimal Stochastic Resource Allocation for Distributed Quantum Computing [50.809738453571015]
  We propose a resource allocation scheme for distributed quantum computing (DQC) based on programming to minimize the total deployment cost for quantum resources. The evaluation demonstrates the effectiveness and ability of the proposed scheme to balance the utilization of quantum computers and on-demand quantum computers.
  arXiv  Detail & Related papers  (2022-09-16T02:37:32Z)
• ScaleQC: A Scalable Framework for Hybrid Computation on Quantum and Classical Processors [25.18520278107402]
  Quantum processing unit (QPU) has to satisfy highly demanding quantity and quality requirements on its qubits. Quantum circuit cutting techniques cut and distribute a large quantum circuit into multiple smaller subcircuits feasible for less powerful QPUs. Our tool, called ScaleQC, addresses the bottlenecks by developing novel algorithmic techniques.
  arXiv  Detail & Related papers  (2022-07-03T01:44:31Z)
• Quantum Computing Quantum Monte Carlo [8.69884453265578]
  We propose a hybrid quantum-classical algorithm that integrates quantum computing and quantum Monte Carlo. Our work paves the way to solving practical problems with intermediatescale and early-fault tolerant quantum computers.
  arXiv  Detail & Related papers  (2022-06-21T14:26:24Z)
• Quantum-assisted Monte Carlo algorithms for fermions [5.625946422295428]
  We propose a family of scalable quantum-assisted Monte Carlo algorithms where the quantum computer is used at its minimal cost. We show that the hybrid Monte Carlo framework is a general way to suppress errors in the ground state obtained from classical algorithms.
  arXiv  Detail & Related papers  (2022-05-30T07:49:22Z)
• Efficient criteria of quantumness for a large system of qubits [58.720142291102135]
  We discuss the dimensionless combinations of basic parameters of large, partially quantum coherent systems. Based on analytical and numerical calculations, we suggest one such number for a system of qubits undergoing adiabatic evolution.
  arXiv  Detail & Related papers  (2021-08-30T23:50:05Z)
• Error mitigation and quantum-assisted simulation in the error corrected regime [77.34726150561087]
  A standard approach to quantum computing is based on the idea of promoting a classically simulable and fault-tolerant set of operations. We show how the addition of noisy magic resources allows one to boost classical quasiprobability simulations of a quantum circuit.
  arXiv  Detail & Related papers  (2021-03-12T20:58:41Z)

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.