Demonstration of Hardware Efficient Photonic Variational Quantum Algorithm

• URL: http://arxiv.org/abs/2408.10339v1
• Date: Mon, 19 Aug 2024 18:26:57 GMT
• Title: Demonstration of Hardware Efficient Photonic Variational Quantum Algorithm
• Authors: Iris Agresti, Koushik Paul, Peter Schiansky, Simon Steiner, Zhengao Yin, Ciro Pentangelo, Simone Piacentini, Andrea Crespi, Yue Ban, Francesco Ceccarelli, Roberto Osellame, Xi Chen, Philip Walther,
• Abstract summary: We show that single photons and linear optical networks are sufficient for implementing Variational Quantum Algorithms. We show this by a proof-of-principle demonstration of a variational approach to tackle an instance of a factorization task.
• Score: 2.4630731476141365
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum computing has brought a paradigm change in computer science, where non-classical technologies have promised to outperform their classical counterpart. Such an advantage was only demonstrated for tasks without practical applications, still out of reach for the state-of-art quantum technologies. In this context, a promising strategy to find practical use of quantum computers is to exploit hybrid quantum-classical models, where a quantum device estimates a hard-to-compute quantity, while a classical optimizer trains the parameters of the model. In this work, we demonstrate that single photons and linear optical networks are sufficient for implementing Variational Quantum Algorithms, when the problem specification, or ansatz, is tailored to this specific platform. We show this by a proof-of-principle demonstration of a variational approach to tackle an instance of a factorization task, whose solution is encoded in the ground state of a suitable Hamiltonian. This work which combines Variational Quantum Algorithms with hardware efficient ansatzes for linear-optics networks showcases a promising pathway towards practical applications for photonic quantum platforms.

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 consistent neural/tensor networks for photonic circuits with strongly/weakly entangled states [0.0]
  We propose an approach to approximate the exact unitary evolution of closed entangled systems in a precise, efficient and quantum consistent manner. By training the networks with a reasonably small number of examples of quantum dynamics, we enable efficient parameter estimation in larger Hilbert spaces.
  arXiv  Detail & Related papers  (2024-06-03T09:51:25Z)
• Demonstration of quantum projective simulation on a single-photon-based quantum computer [0.0]
  Variational quantum algorithms show potential in effectively operating on noisy intermediate-scale quantum devices. We present the implementation of this algorithm on Ascella, a single-photon-based quantum computer from Quandela.
  arXiv  Detail & Related papers  (2024-04-19T09:17:15Z)
• Quantum Annealing for Single Image Super-Resolution [86.69338893753886]
  We propose a quantum computing-based algorithm to solve the single image super-resolution (SISR) problem. The proposed AQC-based algorithm is demonstrated to achieve improved speed-up over a classical analog while maintaining comparable SISR accuracy.
  arXiv  Detail & Related papers  (2023-04-18T11:57:15Z)
• Quantum Machine Learning: from physics to software engineering [58.720142291102135]
  We show how classical machine learning approach can help improve the facilities of quantum computers. We discuss how quantum algorithms and quantum computers may be useful for solving classical machine learning tasks.
  arXiv  Detail & Related papers  (2023-01-04T23:37:45Z)
• Quantum Neural Architecture Search with Quantum Circuits Metric and Bayesian Optimization [2.20200533591633]
  We propose a new quantum gates distance that characterizes the gates' action over every quantum state. Our approach significantly outperforms the benchmark on three empirical quantum machine learning problems.
  arXiv  Detail & Related papers  (2022-06-28T16:23:24Z)
• Variational Quantum Policy Gradients with an Application to Quantum Control [0.0]
  Quantum Machine Learning models are composed by Variational Quantum Circuits (VQCs) in a very natural way. In this work, we consider Policy Gradients using a hardware-efficient ansatz. We prove that the complexity of obtaining an epsilon-approximation of the gradient using quantum hardware scales only logarithmically with the number of parameters.
  arXiv  Detail & Related papers  (2022-03-20T16:14:49Z)
• Surviving The Barren Plateau in Variational Quantum Circuits with Bayesian Learning Initialization [0.0]
  Variational quantum-classical hybrid algorithms are seen as a promising strategy for solving practical problems on quantum computers in the near term. Here, we introduce the fast-and-slow algorithm, which uses gradients to identify a promising region in Bayesian space. Our results move variational quantum algorithms closer to their envisioned applications in quantum chemistry, optimization, and quantum simulation problems.
  arXiv  Detail & Related papers  (2022-03-04T17:48:57Z)
• 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)
• The Hintons in your Neural Network: a Quantum Field Theory View of Deep Learning [84.33745072274942]
  We show how to represent linear and non-linear layers as unitary quantum gates, and interpret the fundamental excitations of the quantum model as particles. On top of opening a new perspective and techniques for studying neural networks, the quantum formulation is well suited for optical quantum computing.
  arXiv  Detail & Related papers  (2021-03-08T17:24:29Z)
• Experimental Quantum Generative Adversarial Networks for Image Generation [93.06926114985761]
  We experimentally achieve the learning and generation of real-world hand-written digit images on a superconducting quantum processor. Our work provides guidance for developing advanced quantum generative models on near-term quantum devices.
  arXiv  Detail & Related papers  (2020-10-13T06:57:17Z)

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.