Universal compilation for quantum state preparation and tomography

• URL: http://arxiv.org/abs/2204.11635v1
• Date: Mon, 25 Apr 2022 13:10:33 GMT
• Title: Universal compilation for quantum state preparation and tomography
• Authors: Vu Tuan Hai and Le Bin Ho
• Abstract summary: We propose a universal compilation-based variational algorithm for the preparation and tomography of quantum states in low-depth quantum circuits. We evaluate the performance of various unitary topologies and the trainability of different unitarys for getting high efficiency.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Universal compilation is a training process that compiles a trainable unitary into a target unitary and it serves vast potential applications from quantum dynamic simulations to optimal circuits with deep-compressing, device benchmarking, quantum error mitigation, and so on. Here, we propose a universal compilation-based variational algorithm for the preparation and tomography of quantum states in low-depth quantum circuits. We apply the Fubini-Study distance to be a trainable cost function under various gradient-based optimizers, including the quantum natural gradient approach. We evaluate the performance of various unitary topologies and the trainability of different optimizers for getting high efficiency. In practice, we address different circuit ansatzes in quantum state preparation, including the linear and graph-based ansatzes for preparing different entanglement target states such as representative GHZ and W states. We also discuss the effect of the circuit depth, barren plateau, readout noise in the model, and the error mitigation solution. We next evaluate the reconstructing efficiency in quantum state tomography via various popular circuit ansatzes and reveal the crucial role of the circuit depth in the robust fidelity. The results are comparable with the shadow tomography method, a similar fashion in the field. Our work expresses the adequate capacity of the universal compilation-based variational algorithm to maximize the efficiency in the quantum state preparation and tomography. Further, it promises applications in quantum metrology and sensing and is applicable in the near-term quantum computers for verification of the circuits fidelity and various quantum computing tasks.

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)
• Quantum landscape tomography for efficient single-gate optimization on quantum computers [0.0]
  Circuit optimization is a fundamental task for practical applications of near-term quantum computers. We propose a process called quantum landscape tomography to characterize the influence of individual gates on the entire circuit. Our findings highlight the potential of quantum landscape tomography to enhance circuit optimization in near-term quantum computing applications.
  arXiv  Detail & Related papers  (2024-07-25T18:00:06Z)
• Multi-target quantum compilation algorithm [0.0]
  Quantum compilation is a process of converting a target unitary operation into a trainable unitary represented by a quantum circuit. We develop a multi-target quantum compilation algorithm to improve the performance and flexibility of simulating multiple quantum systems.
  arXiv  Detail & Related papers  (2024-07-01T06:47:24Z)
• Learning the expressibility of quantum circuit ansatz using transformer [5.368973814856243]
  We propose using a transformer model to predict the expressibility of quantum circuit ansatze. This research can enhance the understanding of the expressibility of quantum circuit ansatze and advance quantum architecture search algorithms.
  arXiv  Detail & Related papers  (2024-05-29T07:34:07Z)
• Near-Term Distributed Quantum Computation using Mean-Field Corrections and Auxiliary Qubits [77.04894470683776]
  We propose near-term distributed quantum computing that involve limited information transfer and conservative entanglement production. We build upon these concepts to produce an approximate circuit-cutting technique for the fragmented pre-training of variational quantum algorithms.
  arXiv  Detail & Related papers  (2023-09-11T18:00:00Z)
• Variational preparation of entangled states on quantum computers [0.0]
  We propose a variational approach for preparing entangled quantum states on quantum computers. We employ various gradient-based optimization techniques to enhance performance. We demonstrate the effectiveness of the variational algorithm in maximizing the efficiency of quantum state preparation.
  arXiv  Detail & Related papers  (2023-06-30T06:29:24Z)
• 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)
• Quantum circuit debugging and sensitivity analysis via local inversions [62.997667081978825]
  We present a technique that pinpoints the sections of a quantum circuit that affect the circuit output the most. We demonstrate the practicality and efficacy of the proposed technique by applying it to example algorithmic circuits implemented on IBM quantum machines.
  arXiv  Detail & Related papers  (2022-04-12T19:39:31Z)
• Circuit Symmetry Verification Mitigates Quantum-Domain Impairments [69.33243249411113]
  We propose circuit-oriented symmetry verification that are capable of verifying the commutativity of quantum circuits without the knowledge of the quantum state. In particular, we propose the Fourier-temporal stabilizer (STS) technique, which generalizes the conventional quantum-domain formalism to circuit-oriented stabilizers.
  arXiv  Detail & Related papers  (2021-12-27T21:15:35Z)
• Feasible Architecture for Quantum Fully Convolutional Networks [4.849886707973093]
  We propose a feasible pure quantum architecture that can be operated on noisy intermediate-scale quantum devices. Our study represents the successful training of a pure quantum fully convolutional network and discusses advantages by comparing it with the hybrid solution.
  arXiv  Detail & Related papers  (2021-10-05T01:06:54Z)
• 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)

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.