arXiv subjects

Eloy Piñol

Publications and source records attributed to Eloy Piñol.

6 recordsLinked to original sources

arXiv2026

Distinguishing synthetic unravelings on quantum computers

Distinct monitoring or intervention schemes can produce different conditioned stochastic quantum trajectories while sharing the same unconditional (ensemble-averaged) dynamics. This is the essence of unravelings of a given Gorini-Kossakowski-Sudarshan-Lindblad (GKSL) master equation: any trajectory-ensemble average of a function that is linear in the conditional state is completely determined by the unconditional density matrix, whereas applying a nonlinear function before averaging can yield unraveling-dependent results beyond the average evolution. A paradigmatic example is resonance fluorescence, where direct photodetection (jump/Poisson) and homodyne or heterodyne detection (diffusive/Wiener) define inequivalent unravelings of the same GKSL dynamics. In earlier work, we showed that nonlinear trajectory averages can distinguish such unravelings, but observing the effect in that optical setting requires demanding experimental precision. Here we translate the same idea to a digital setting by introducing synthetic unravelings implemented as quantum circuits acting on one and two qubits. We design two unravelings - a projective measurement unraveling and a random-unitary "kick" unraveling - that share the same ensemble-averaged evolution while yielding different nonlinear conditional-state statistics. We implement the protocols on superconducting-qubit hardware provided by IBM Quantum to access trajectory-level information. We show that the variance across trajectories and the ensemble-averaged von Neumann entropy distinguish the unravelings in both theory and experiment, while the unconditional state and the ensemble-averaged expectation values that are linear in the state remain identical. Our results provide an accessible demonstration that quantum trajectories encode information about measurement backaction beyond what is fixed by the unconditional dynamics.

quant-ph↗

arXiv2024

Telling different unravelings apart via nonlinear quantum-trajectory averages

The Gorini-Kossakowski-Sudarshan-Lindblad master equation (ME) governs the density matrix of open quantum systems (OQSs). When an OQS is subjected to weak continuous measurement, its state evolves as a stochastic quantum trajectory, whose statistical average solves the ME. The ensemble of such trajectories is termed an unraveling of the ME. We propose a method to operationally distinguish unravelings produced by the same ME in different measurement scenarios, using nonlinear averages of observables over trajectories. We apply the method to the paradigmatic quantum nonlinear system of resonance fluorescence in a two-level atom. We compare the Poisson-type unraveling, induced by direct detection of photons scattered from the two-level emitter, and the Wiener-type unraveling, induced by phase-sensitive detection of the emitted field. We show that a quantum-trajectory-averaged variance is able to distinguish these measurement scenarios. We evaluate the performance of the method, which can be readily extended to more complex OQSs, under a range of realistic experimental conditions.

quant-ph↗

arXiv2024

Towards the Intuitive Understanding of Quantum World: Sonification of Rabi Oscillations, Wigner functions, and Quantum Simulators

Recently, there has been considerable interest in "sonifying" scientific data; however, sonifying quantum processes using the newest quantum technologies, including Noise Intermediate Scale Quantum devices and quantum random number generators, is still an emerging area of research. Music technologists and composers employ the growing accessibility to diverse data from quantum mechanics as musical tools in the hope of generating new sound expressions. How different is the quantum world from the classical one, and is it possible to express the quantum world using sounds? Quantum phenomena are very different from those that we experience in our everyday lives. Thus, it is challenging to understand them intuitively. In this paper, we propose sonification as a method toward an intuitive understanding of various quantum mechanical phenomena, from Rabi oscillations and resonance fluorescence of a single atom through the generation of Schrödinger cat states in strong laser field physics to insulator-superfluid transition in quantum many-body systems. This paper illustrates various methods we experimented with in sonification and score representations of quantum data depending on the source data and performance settings.

quant-ph↗

arXiv2024

Sonification of Wigner functions: case study of intense light-matter interactions

In quantum mechanics, the Wigner function $ρ_W(\textbf{r},\textbf{p})$ serves as a phase-space representation, capturing information about both the position $\textbf{r}$ and momentum $\textbf{p}$ of a quantum system. The Wigner function facilitates the calculation of expectation values of observables, examination of quantum system dynamics, and analysis of coherence and correlations. Therefore, it might serve as a tool to express quantum systems intuitively, for example, by using sonification techniques. This paper summarizes the experimental strategies employed in a previous project and delineates a new approach based on its outcomes. Emphasizing the attribution of specific Wigner functions to their underlying quantum states, dynamics, and sources; our proposed methodology seeks to refine the sonification and scoring process, aiming to enhance intuitive understanding and interpretation of quantum phenomena.

quant-ph↗

arXiv2023

Universal representation by Boltzmann machines with Regularised Axons

It is widely known that Boltzmann machines are capable of representing arbitrary probability distributions over the values of their visible neurons, given enough hidden ones. However, sampling -- and thus training -- these models can be numerically hard. Recently we proposed a regularisation of the connections of Boltzmann machines, in order to control the energy landscape of the model, paving a way for efficient sampling and training. Here we formally prove that such regularised Boltzmann machines preserve the ability to represent arbitrary distributions. This is in conjunction with controlling the number of energy local minima, thus enabling easy \emph{guided} sampling and training. Furthermore, we explicitly show that regularised Boltzmann machines can store exponentially many arbitrarily correlated visible patterns with perfect retrieval, and we connect them to the Dense Associative Memory networks.

cond-mat.stat-mech↗

arXiv2021

Efficient training of energy-based models via spin-glass control

We introduce a new family of energy-based probabilistic graphical models for efficient unsupervised learning. Its definition is motivated by the control of the spin-glass properties of the Ising model described by the weights of Boltzmann machines. We use it to learn the Bars and Stripes dataset of various sizes and the MNIST dataset, and show how they quickly achieve the performance offered by standard methods for unsupervised learning. Our results indicate that the standard initialization of Boltzmann machines with random weights equivalent to spin-glass models is an unnecessary bottleneck in the process of training. Furthermore, this new family allows for very easy access to low-energy configurations, which points to new, efficient training algorithms. The simplest variant of such algorithms approximates the negative phase of the log-likelihood gradient with no Markov chain Monte Carlo sampling costs at all, and with an accuracy sufficient to achieve good learning and generalization.

cond-mat.stat-mech↗