Towards reconstructing quantum structured light on a quantum computer
- URL: http://arxiv.org/abs/2509.21804v2
- Date: Tue, 04 Nov 2025 15:29:33 GMT
- Title: Towards reconstructing quantum structured light on a quantum computer
- Authors: Mwezi Koni, Shawal Kassim, Paola C. Obando, Neelan Gounden, Isaac Nape,
- Abstract summary: We introduce a variational quantum computing approach for reconstructing quantum states from measurement data.<n>By mapping the reconstruction cost function onto an Ising model, the problem can be solved using a variational eigensolver on present-day quantum hardware.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We introduce a variational quantum computing approach for reconstructing quantum states from measurement data. By mapping the reconstruction cost function onto an Ising model, the problem can be solved using a variational eigensolver on present-day quantum hardware. As a proof of concept, we demonstrate the method on quantum structured light, in particular, entangled photons carrying orbital angular momentum and show that the reconstruction procedure can yield reliable performance even on noisy devices. Our results highlight the potential of variational algorithms for efficient quantum state tomography, particularly for high-dimensional structured light, where classical approaches can face bottlenecks.
Related papers
- Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise [49.97673761305336]
Noise remains a major obstacle to achieving reliable quantum algorithms.<n>We present a provably noise-resilient training theory and algorithm to enhance the robustness of parameterized quantum circuit classifiers.
arXiv Detail & Related papers (2025-05-24T02:51:34Z) - Quantum Cryptography Using Momentum and Position Variables in a Simple Optical Arrangement [49.1574468325115]
We explore an experimental implementation of quantum key distribution (QKD) using position and momentum quantum states.<n>By employing a setup that includes a laser, a slit, and lenses, we demonstrate a variation of the BB84 protocol.
arXiv Detail & Related papers (2025-05-07T09:11:37Z) - Hamiltonian Dynamics Learning: A Scalable Approach to Quantum Process Characterization [6.741097425426473]
We introduce an efficient quantum process learning method specifically designed for short-time Hamiltonian dynamics.<n>We demonstrate applications in quantum machine learning, where our protocol enables efficient training of variational quantum neural networks by directly learning unitary transformations.<n>This work establishes a new theoretical foundation for practical quantum dynamics learning, paving the way for scalable quantum process characterization in both near-term and fault-tolerant quantum computing.
arXiv Detail & Related papers (2025-03-31T14:50:00Z) - Prospects for quantum process tomography at high energies [0.0]
In quantum information theory, the evolution of an open quantum system is described by a quantum channel or, more generally, a quantum instrument.<n>We formulate spin and measurements in collider experiments as quantum instruments.
arXiv Detail & Related papers (2024-12-02T19:00:00Z) - Exploring quantum localization with machine learning [39.58317527488534]
We introduce an efficient neural network (NN) architecture for classifying wave functions in terms of their localization.
Our approach integrates a versatile quantum phase space parametrization leading to a custom 'quantum' NN, with the pattern recognition capabilities of a modified convolutional model.
arXiv Detail & Related papers (2024-06-01T08:50:26Z) - Corrupted sensing quantum state tomography [0.0]
We propose the concept of corrupted sensing quantum state tomography which enables the simultaneous reconstruction of quantum states and structured noise.<n>It is envisaged that the techniques can become a practical tool to greatly reduce the cost and computational effort for quantum tomography in noisy quantum systems.
arXiv Detail & Related papers (2024-05-23T10:13:59Z) - Experimental property-reconstruction in a photonic quantum extreme
learning machine [43.55994393060723]
We implement a quantum extreme learning machine in a photonic platform to achieve resource-efficient and accurate characterization of the polarization state of a photon.
We demonstrate how the reconstruction of an unknown polarization state does not need a careful characterization of the measurement apparatus and is robust to experimental imperfections.
arXiv Detail & Related papers (2023-08-08T19:22:03Z) - 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) - Engineering quantum states from a spatially structured quantum eraser [0.0]
Quantum interference can be enabled by projecting the quantum state onto ambiguous properties that render the photons indistinguishable.
By combining these ideas, here we design and experimentally demonstrate a simple and robust scheme that tailors quantum interference to engineer photonic states.
We believe these spatially-engineered multi-photon quantum states may be of significance in fields such as quantum metrology, microscopy, and communications.
arXiv Detail & Related papers (2023-06-24T00:11:36Z) - Towards interpretable quantum machine learning via single-photon quantum
walks [2.4047296366832307]
We present a variational method to quantize projective simulation (PS)
PS is a reinforcement learning model aimed at interpretable artificial intelligence.
We show that the quantized PS model can exploit quantum interference to acquire capabilities beyond those of its classical counterpart.
arXiv Detail & Related papers (2023-01-31T14:38:33Z) - 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) - 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)
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.