Quantum computing with differentiable quantum transforms

• URL: http://arxiv.org/abs/2202.13414v1
• Date: Sun, 27 Feb 2022 18:11:55 GMT
• Title: Quantum computing with differentiable quantum transforms
• Authors: Olivia Di Matteo, Josh Izaac, Tom Bromley, Anthony Hayes, Christina Lee, Maria Schuld, Antal Sz\'ava, Chase Roberts, Nathan Killoran
• Abstract summary: We present a framework for differentiable quantum transforms. Such transforms are metaprograms capable of manipulating quantum programs in a way that preserves their differentiability. We highlight their potential with a set of relevant examples across quantum computing.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a framework for differentiable quantum transforms. Such transforms are metaprograms capable of manipulating quantum programs in a way that preserves their differentiability. We highlight their potential with a set of relevant examples across quantum computing (gradient computation, circuit compilation, and error mitigation), and implement them using the transform framework of PennyLane, a software library for differentiable quantum programming. In this framework, the transforms themselves are differentiable and can be parametrized and optimized, which opens up the possibility of improved quantum resource requirements across a spectrum of tasks.

Related papers

• A Survey of Quantum Transformers: Architectures, Challenges and Outlooks [82.4736481748099]
  Quantum Transformers integrate the representational power of classical Transformers with the computational advantages of quantum computing.<n>Since 2022, research in this area has rapidly expanded, giving rise to diverse technical paradigms and early applications.<n>This paper presents the first comprehensive, systematic, and in-depth survey of quantum Transformer models.
  arXiv  Detail & Related papers  (2025-04-04T05:40:18Z)
• Quantum memristors for neuromorphic quantum machine learning [0.0]
  Quantum memristors are promising as a way of combining, in the same quantum hardware, a unitary evolution with the nonlinearity provided by the measurement and feedforward. An efficient way of deploying neuromorphic quantum computing for quantum machine learning may be enabled.
  arXiv  Detail & Related papers  (2024-12-25T20:21:24Z)
• Large-scale quantum reservoir learning with an analog quantum computer [45.21335836399935]
  We develop a quantum reservoir learning algorithm that harnesses the quantum dynamics of neutral-atom analog quantum computers to process data. We experimentally implement the algorithm, achieving competitive performance across various categories of machine learning tasks. Our findings demonstrate the potential of utilizing classically intractable quantum correlations for effective machine learning.
  arXiv  Detail & Related papers  (2024-07-02T18:00:00Z)
• Variational methods for solving high dimensional quantum systems [0.0]
  We explore the effectiveness of three variational methods for solving high-dimensional quantum systems. We apply these methods to two different quantum systems: the fermi-Hubbard model in condensed matter physics and the Schwinger model in high energy physics. We calculate the ground state of both quantum systems and compare the results obtained using the three variational methods.
  arXiv  Detail & Related papers  (2024-04-17T15:46:27Z)
• Quantum data learning for quantum simulations in high-energy physics [55.41644538483948]
  We explore the applicability of quantum-data learning to practical problems in high-energy physics. We make use of ansatz based on quantum convolutional neural networks and numerically show that it is capable of recognizing quantum phases of ground states. The observation of non-trivial learning properties demonstrated in these benchmarks will motivate further exploration of the quantum-data learning architecture in high-energy physics.
  arXiv  Detail & Related papers  (2023-06-29T18:00:01Z)
• Efficient estimation of trainability for variational quantum circuits [43.028111013960206]
  We find an efficient method to compute the cost function and its variance for a wide class of variational quantum circuits. This method can be used to certify trainability for variational quantum circuits and explore design strategies that can overcome the barren plateau problem.
  arXiv  Detail & Related papers  (2023-02-09T14:05:18Z)
• Implicit differentiation of variational quantum algorithms [0.8594140167290096]
  We show how to leverage implicit differentiation for computation through variational quantum algorithms. We explore applications in condensed matter physics, quantum machine learning, and quantum information.
  arXiv  Detail & Related papers  (2022-11-24T19:00:19Z)
• Differentiable Quantum Programming with Unbounded Loops [10.648855845619705]
  We provide the first differentiable quantum programming framework with unbounded loops. We introduce a randomized estimator for derivatives to deal with the infinite sum in the differentiation of unbounded loops. We showcase an exciting application of our framework in automatically identifying close-to-optimal parameters for several parameterized quantum applications.
  arXiv  Detail & Related papers  (2022-11-08T19:07:06Z)
• Quantum Phase Processing and its Applications in Estimating Phase and Entropies [10.8525801756287]
  "quantum phase processing" can directly apply arbitrary trigonometric transformations to eigenphases of a unitary operator. Quantum phase processing can extract the eigen-information of quantum systems by simply measuring the ancilla qubit. We propose a new quantum phase estimation algorithm without quantum Fourier transform, which requires the fewest ancilla qubits and matches the best performance so far.
  arXiv  Detail & Related papers  (2022-09-28T17:41:19Z)
• 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)
• 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)
• Numerical hardware-efficient variational quantum simulation of a soliton solution [0.0]
  We discuss the capabilities of quantum algorithms with special attention paid to a hardware-efficient variational eigensolver. A delicate interplay between magnetic interactions allows one to stabilize a chiral state that destroys the homogeneity of magnetic ordering. We argue that, while being capable of correctly reproducing a uniform magnetic configuration, the hardware-efficient ansatz meets difficulties in providing a detailed description to a noncollinear magnetic structure.
  arXiv  Detail & Related papers  (2021-05-13T11:58:18Z)
• Information Scrambling in Computationally Complex Quantum Circuits [56.22772134614514]
  We experimentally investigate the dynamics of quantum scrambling on a 53-qubit quantum processor. We show that while operator spreading is captured by an efficient classical model, operator entanglement requires exponentially scaled computational resources to simulate.
  arXiv  Detail & Related papers  (2021-01-21T22:18:49Z)

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.