Variational approach to photonic quantum circuits via the parameter shift rule

• URL: http://arxiv.org/abs/2410.06966v2
• Date: Tue, 10 Jun 2025 10:55:30 GMT
• Title: Variational approach to photonic quantum circuits via the parameter shift rule
• Authors: Francesco Hoch, Giovanni Rodari, Taira Giordani, Paul Perret, Nicolò Spagnolo, Gonzalo Carvacho, Ciro Pentangelo, Simone Piacentini, Andrea Crespi, Francesco Ceccarelli, Roberto Osellame, Fabio Sciarrino,
• Abstract summary: variational quantum algorithms are promising approaches for solving optimization tasks.<n>In this paper, we derive a formulation of the parameter shift rule for computing derivatives and integrals tailored to reconfigurable optical linear circuits.<n>We employ the developed approach to experimentally test variational algorithms with single-photon states processed in a reconfigurable 6-mode universal interferometer.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the era of noisy intermediate-scale quantum computers, variational quantum algorithms are promising approaches for solving optimization tasks by training parameterized quantum circuits with the aid of classical routines informed by quantum measurements. In this context, photonic platforms based on reconfigurable integrated optics are an ideal candidate for implementing these algorithms. Among various techniques to train variational circuits, the parameter shift rule enables the exact calculation of cost-function derivatives efficiently, facilitating gradient descent-based optimization. In this paper, we derive a formulation of the parameter shift rule for computing derivatives and integrals tailored to reconfigurable optical linear circuits and based on the Boson Sampling paradigm. This allows us to naturally embed common types of experimental noise, such as partial distinguishability and mixedness of the states, thus obtaining a resilient approach. Finally, we employ the developed approach to experimentally test variational algorithms with single-photon states processed in a reconfigurable 6-mode universal integrated interferometer. Specifically, we apply the photonic parameter shift rules to the variational implementation, on a photonic platform, of both an eigensolver and a Universal-Not gate.

Related papers

• A Photonic Parameter-shift Rule: Enabling Gradient Computation for Photonic Quantum Computers [0.0]
  We present a method for gradient computation in quantum computation algorithms implemented on linear optical quantum computing platforms. Our method scales linearly with the number of input photons and utilizes the same parameterized photonic circuit with shifted parameters for each evaluation.
  arXiv  Detail & Related papers  (2024-10-03T17:47:38Z)
• Exact gradients for linear optics with single photons [38.74529485263391]
  We derive an analytical formula for the gradients in quantum circuits with respect to phaseshifters via a generalized parameter shift rule. We propose two strategies through which one can reduce the number of shifts in the expression, and hence reduce the overall sample complexity. Numerically, we show that this generalized parameter-shift rule can converge to the minimum of a cost function with fewer parameter update steps than alternative techniques.
  arXiv  Detail & Related papers  (2024-09-24T18:02:06Z)
• Pulse-based variational quantum optimization and metalearning in superconducting circuits [3.770494165043573]
  We introduce pulse-based variational quantum optimization (PBVQO) as a hardware-level framework. We illustrate the framework by optimizing external superconducting on quantum interference devices. The synergy between PBVQO and meta-learning provides an advantage over conventional gate-based variational algorithms.
  arXiv  Detail & Related papers  (2024-07-17T15:05:36Z)
• 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)
• Parsimonious Optimisation of Parameters in Variational Quantum Circuits [1.303764728768944]
  We propose a novel Quantum-Gradient Sampling that requires the execution of at most two circuits per iteration to update the optimisable parameters. Our proposed method achieves similar convergence rates to classical gradient descent, and empirically outperforms gradient coordinate descent, and SPSA.
  arXiv  Detail & Related papers  (2023-06-20T18:50:18Z)
• 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)
• Quantum-inspired optimization for wavelength assignment [51.55491037321065]
  We propose and develop a quantum-inspired algorithm for solving the wavelength assignment problem. Our results pave the way to the use of quantum-inspired algorithms for practical problems in telecommunications.
  arXiv  Detail & Related papers  (2022-11-01T07:52:47Z)
• Retrieving space-dependent polarization transformations via near-optimal quantum process tomography [55.41644538483948]
  We investigate the application of genetic and machine learning approaches to tomographic problems. We find that the neural network-based scheme provides a significant speed-up, that may be critical in applications requiring a characterization in real-time. We expect these results to lay the groundwork for the optimization of tomographic approaches in more general quantum processes.
  arXiv  Detail & Related papers  (2022-10-27T11:37:14Z)
• Numerical Simulations of Noisy Quantum Circuits for Computational Chemistry [51.827942608832025]
  Near-term quantum computers can calculate the ground-state properties of small molecules. We show how the structure of the computational ansatz as well as the errors induced by device noise affect the calculation.
  arXiv  Detail & Related papers  (2021-12-31T16:33:10Z)
• 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)
• Single-component gradient rules for variational quantum algorithms [1.3047205680129093]
  A common bottleneck of any such algorithm is constituted by the optimization of the variational parameters. A popular set of optimization methods work on the estimate of the gradient, obtained by means of circuit evaluations. This work provides a comprehensive picture of the family of gradient rules that vary parameters of quantum gates individually.
  arXiv  Detail & Related papers  (2021-06-02T18:00:10Z)
• 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)
• Rapid characterisation of linear-optical networks via PhaseLift [51.03305009278831]
  Integrated photonics offers great phase-stability and can rely on the large scale manufacturability provided by the semiconductor industry. New devices, based on such optical circuits, hold the promise of faster and energy-efficient computations in machine learning applications. We present a novel technique to reconstruct the transfer matrix of linear optical networks.
  arXiv  Detail & Related papers  (2020-10-01T16:04:22Z)
• Measuring Analytic Gradients of General Quantum Evolution with the Stochastic Parameter Shift Rule [0.0]
  We study the problem of estimating the gradient of the function to be optimized directly from quantum measurements. We derive a mathematically exact formula that provides an algorithm for estimating the gradient of any multi-qubit parametric quantum evolution. Our algorithm continues to work, although with some approximations, even when all the available quantum gates are noisy.
  arXiv  Detail & Related papers  (2020-05-20T18:24:11Z)

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.