Scalable quantum dynamics compilation via quantum machine learning

• URL: http://arxiv.org/abs/2409.16346v1
• Date: Tue, 24 Sep 2024 18:00:00 GMT
• Title: Scalable quantum dynamics compilation via quantum machine learning
• Authors: Yuxuan Zhang, Roeland Wiersema, Juan Carrasquilla, Lukasz Cincio, Yong Baek Kim,
• Abstract summary: variational quantum compilation (VQC) methods employ variational optimization to reduce gate costs while maintaining high accuracy. We show that our approach exceeds state-of-the-art compilation results in both system size and accuracy in one dimension ($1$D) For the first time, we extend VQC to systems on two-dimensional (2D) strips with a quasi-1D treatment, demonstrating a significant resource advantage over standard Trotterization methods.
• Score: 7.31922231703204
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum dynamics compilation is an important task for improving quantum simulation efficiency: It aims to synthesize multi-qubit target dynamics into a circuit consisting of as few elementary gates as possible. Compared to deterministic methods such as Trotterization, variational quantum compilation (VQC) methods employ variational optimization to reduce gate costs while maintaining high accuracy. In this work, we explore the potential of a VQC scheme by making use of out-of-distribution generalization results in quantum machine learning (QML): By learning the action of a given many-body dynamics on a small data set of product states, we can obtain a unitary circuit that generalizes to highly entangled states such as the Haar random states. The efficiency in training allows us to use tensor network methods to compress such time-evolved product states by exploiting their low entanglement features. Our approach exceeds state-of-the-art compilation results in both system size and accuracy in one dimension ($1$D). For the first time, we extend VQC to systems on two-dimensional (2D) strips with a quasi-1D treatment, demonstrating a significant resource advantage over standard Trotterization methods, highlighting the method's promise for advancing quantum simulation tasks on near-term quantum processors.

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)
• Adaptive variational quantum dynamics simulations with compressed circuits and fewer measurements [4.2643127089535104]
  We show an improved version of the adaptive variational quantum dynamics simulation (AVQDS) method, which we call AVQDS(T) The algorithm adaptively adds layers of disjoint unitary gates to the ansatz circuit so as to keep the McLachlan distance, a measure of the accuracy of the variational dynamics, below a fixed threshold. We also show a method based on eigenvalue truncation to solve the linear equations of motion for the variational parameters with enhanced noise resilience.
  arXiv  Detail & Related papers  (2024-08-13T02:56:43Z)
• QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum Circuits [82.50620782471485]
  QuantumSEA is an in-time sparse exploration for noise-adaptive quantum circuits. It aims to achieve two key objectives: (1) implicit circuits capacity during training and (2) noise robustness. Our method establishes state-of-the-art results with only half the number of quantum gates and 2x time saving of circuit executions.
  arXiv  Detail & Related papers  (2024-01-10T22:33:00Z)
• Mapping quantum circuits to shallow-depth measurement patterns based on graph states [0.0]
  We create a hybrid simulation technique for measurement-based quantum computing. We show that groups of fully commuting operators can be implemented using fully-parallel, i.e., non-adaptive, measurements. We discuss how such circuits can be implemented in constant quantum depths by employing quantum teleportation.
  arXiv  Detail & Related papers  (2023-11-27T19:00:00Z)
• Applicability of Measurement-based Quantum Computation towards Physically-driven Variational Quantum Eigensolver [17.975555487972166]
  Variational quantum algorithms are considered one of the most promising methods for obtaining near-term quantum advantages. The roadblock to developing quantum algorithms with the measurement-based quantum computation scheme is resource cost. We propose an efficient measurement-based quantum algorithm for quantum many-body system simulation tasks, called measurement-based Hamiltonian variational ansatz (MBHVA)
  arXiv  Detail & Related papers  (2023-07-19T08:07:53Z)
• 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)
• 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)
• Gradient-descent quantum process tomography by learning Kraus operators [63.69764116066747]
  We perform quantum process tomography (QPT) for both discrete- and continuous-variable quantum systems. We use a constrained gradient-descent (GD) approach on the so-called Stiefel manifold during optimization to obtain the Kraus operators. The GD-QPT matches the performance of both compressed-sensing (CS) and projected least-squares (PLS) QPT in benchmarks with two-qubit random processes.
  arXiv  Detail & Related papers  (2022-08-01T12:48:48Z)
• Continuous-variable quantum approximate optimization on a programmable photonic quantum processor [0.9558392439655016]
  Variational quantum algorithms (VQAs) provide a promising approach to achieving quantum advantage for practical problems on noisy quantum devices. We develop an automated collaborative computing system between a programmable photonic quantum computer and a classical computer. Our work can be extended to the minimization of more general functions, providing an alternative to achieve the quantum advantage in practical problems.
  arXiv  Detail & Related papers  (2022-06-15T00:00:47Z)
• Preparation of excited states for nuclear dynamics on a quantum computer [117.44028458220427]
  We study two different methods to prepare excited states on a quantum computer. We benchmark these techniques on emulated and real quantum devices. These findings show that quantum techniques designed to achieve good scaling on fault tolerant devices might also provide practical benefits on devices with limited connectivity and gate fidelity.
  arXiv  Detail & Related papers  (2020-09-28T17:21:25Z)
• Autoregressive Transformer Neural Network for Simulating Open Quantum Systems via a Probabilistic Formulation [5.668795025564699]
  We present an approach for tackling open quantum system dynamics. We compactly represent quantum states with autoregressive transformer neural networks. Efficient algorithms have been developed to simulate the dynamics of the Liouvillian superoperator.
  arXiv  Detail & Related papers  (2020-09-11T18:00:00Z)

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.