Experimental multiparameter quantum metrology in adaptive regime

• URL: http://arxiv.org/abs/2208.14473v1
• Date: Tue, 30 Aug 2022 18:02:51 GMT
• Title: Experimental multiparameter quantum metrology in adaptive regime
• Authors: Mauro Valeri, Valeria Cimini, Simone Piacentini, Francesco Ceccarelli, Emanuele Polino, Francesco Hoch, Gabriele Bizzarri, Giacomo Corrielli, Nicol\`o Spagnolo, Roberto Osellame and Fabio Sciarrino
• Abstract summary: We demonstrate the simultaneous estimation of three optical phases in a programmable integrated photonic circuit. Results show the possibility of successfully combining different fundamental methodologies towards transition to quantum sensors applications.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Relevant metrological scenarios involve the simultaneous estimation of multiple parameters. The fundamental ingredient to achieve quantum-enhanced performances is based on the use of appropriately tailored quantum probes. However, reaching the ultimate resolution allowed by physical laws requires non trivial estimation strategies both from a theoretical and a practical point of view. A crucial tool for this purpose is the application of adaptive learning techniques. Indeed, adaptive strategies provide a flexible approach to obtain optimal parameter-independent performances, and optimize convergence to the fundamental bounds with limited amount of resources. Here, we combine on the same platform quantum-enhanced multiparameter estimation attaining the corresponding quantum limit and adaptive techniques. We demonstrate the simultaneous estimation of three optical phases in a programmable integrated photonic circuit, in the limited resource regime. The obtained results show the possibility of successfully combining different fundamental methodologies towards transition to quantum sensors applications.

Related papers

• Fully-Optimized Quantum Metrology: Framework, Tools, and Applications [8.98216737402976]
  The tutorial consists of a pedagogic introduction to the background and mathematical tools of optimal quantum metrology. The approach can identify the optimal precision for different sets of strategies, including parallel, sequential, quantum SWITCH-enhanced, causally superposed, and generic indefinite-causal-order strategies.
  arXiv  Detail & Related papers  (2024-09-11T07:36:40Z)
• Variational quantum algorithm for experimental photonic multiparameter estimation [0.0]
  We develop a variational approach to efficiently optimize a quantum phase sensor operating in a noisy environment. By exploiting the high reconfigurability of an integrated photonic device, we implement a hybrid quantum-classical feedback loop. Our experimental results reveal significant improvements in terms of estimation accuracy and noise robustness.
  arXiv  Detail & Related papers  (2023-08-04T18:01:14Z)
• 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)
• 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)
• Variational Quantum Optimization with Multi-Basis Encodings [62.72309460291971]
  We introduce a new variational quantum algorithm that benefits from two innovations: multi-basis graph complexity and nonlinear activation functions. Our results in increased optimization performance, two increase in effective landscapes and a reduction in measurement progress.
  arXiv  Detail & Related papers  (2021-06-24T20:16:02Z)
• FLIP: A flexible initializer for arbitrarily-sized parametrized quantum circuits [105.54048699217668]
  We propose a FLexible Initializer for arbitrarily-sized Parametrized quantum circuits. FLIP can be applied to any family of PQCs, and instead of relying on a generic set of initial parameters, it is tailored to learn the structure of successful parameters. We illustrate the advantage of using FLIP in three scenarios: a family of problems with proven barren plateaus, PQC training to solve max-cut problem instances, and PQC training for finding the ground state energies of 1D Fermi-Hubbard models.
  arXiv  Detail & Related papers  (2021-03-15T17:38:33Z)
• Certificates of quantum many-body properties assisted by machine learning [0.0]
  We propose a novel approach combining the power of relaxation techniques with deep reinforcement learning. We illustrate the viability of the method in the context of finding the ground state energy of many transfer systems. We provide tools to generalize the approach to other common applications in the field of quantum information processing.
  arXiv  Detail & Related papers  (2021-03-05T17:47:26Z)
• Adaptive pruning-based optimization of parameterized quantum circuits [62.997667081978825]
  Variisy hybrid quantum-classical algorithms are powerful tools to maximize the use of Noisy Intermediate Scale Quantum devices. We propose a strategy for such ansatze used in variational quantum algorithms, which we call "Efficient Circuit Training" (PECT) Instead of optimizing all of the ansatz parameters at once, PECT launches a sequence of variational algorithms.
  arXiv  Detail & Related papers  (2020-10-01T18:14:11Z)
• A variational toolbox for quantum multi-parameter estimation [0.7734726150561088]
  We introduce a general framework which allows for sequential updates of variational parameters to improve probe states and measurements. We then demonstrate the practical functioning of the approach through numerical simulations. We prove the validity of a general parameter-shift rule for noisy evolutions.
  arXiv  Detail & Related papers  (2020-06-11T10:10:20Z)
• Experimental adaptive Bayesian estimation of multiple phases with limited data [0.0]
  adaptive protocols, exploiting additional control parameters, provide a tool to optimize the performance of a quantum sensor to work in such limited data regime. Finding the optimal strategies to tune the control parameters during the estimation process is a non-trivial problem, and machine learning techniques are a natural solution to address such task. We employ a compact and flexible integrated photonic circuit, fabricated by femtosecond laser writing, which allows to implement different strategies with high degree of control.
  arXiv  Detail & Related papers  (2020-02-04T11:32:32Z)

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.