Exact gradients for linear optics with single photons
- URL: http://arxiv.org/abs/2409.16369v1
- Date: Tue, 24 Sep 2024 18:02:06 GMT
- Title: Exact gradients for linear optics with single photons
- Authors: Giorgio Facelli, David D. Roberts, Hugo Wallner, Alexander Makarovskiy, Zoƫ Holmes, William R. Clements,
- Abstract summary: 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.
- Score: 38.74529485263391
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Though parameter shift rules have drastically improved gradient estimation methods for several types of quantum circuits, leading to improved performance in downstream tasks, so far they have not been transferable to linear optics with single photons. In this work, we derive an analytical formula for the gradients in these circuits with respect to phaseshifters via a generalized parameter shift rule, where the number of parameter shifts depends linearly on the total number of photons. Experimentally, this enables access to derivatives in photonic systems without the need for finite difference approximations. Building on this, 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. We anticipate that this method will open up new avenues to solving optimization problems with photonic systems, as well as provide new techniques for the experimental characterization and control of linear optical systems.
Related papers
- Variational approach to photonic quantum circuits via the parameter shift rule [0.0]
We derive a formulation of the parameter shift rule for reconfigurable optical linear circuits based on the Boson Sampling paradigm.
We also present similar rules for the computations of integrals over the variational parameters.
We employ the developed approach to experimentally test variational algorithms with single-photon states processed in a reconfigurable 6-mode universal integrated interferometer.
arXiv Detail & Related papers (2024-10-09T15:06:17Z) - 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) - Differentiation of Linear Optical Circuits [0.0]
Experimental setups based on linear optical circuits and single photon sources offer a promising platform for near-term quantum machine learning.
We show that the derivative of the expectation values of a linear optical circuit can be computed by sampling from a larger circuit.
In order to express derivative in terms of expectation values, we develop a circuit extraction procedure based on unitary dilation.
arXiv Detail & Related papers (2024-01-15T22:43:22Z) - Universal Unitary Photonic Circuits by Interlacing Discrete Fractional
Fourier Transform and Phase Modulation [0.0]
We introduce a novel parameterization of complex unitary matrices, which allows for the efficient implementation of arbitrary linear discrete unitary operators.
We show that such a configuration can represent arbitrary unitary operators with $N+1$ phase layers.
We propose an integrated photonic circuit realization of this architecture with coupled waveguide arrays and reconfigurable phase modulators.
arXiv Detail & Related papers (2023-07-14T00:23:14Z) - 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) - 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) - Amplification of cascaded downconversion by reusing photons with a
switchable cavity [62.997667081978825]
We propose a scheme to amplify triplet production rates by using a fast switch and a delay loop.
Our proof-of-concept device increases the rate of detected photon triplets as predicted.
arXiv Detail & Related papers (2022-09-23T15:53:44Z) - 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) - Proximal Gradient Algorithm with Momentum and Flexible Parameter Restart
for Nonconvex Optimization [73.38702974136102]
Various types of parameter restart schemes have been proposed for accelerated algorithms to facilitate their practical convergence in rates.
In this paper, we propose an algorithm for solving nonsmooth problems.
arXiv Detail & Related papers (2020-02-26T16:06:27Z)
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.