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Erik Torrontegui

Publications and source records attributed to Erik Torrontegui.

9 recordsLinked to original sources

Quantum metrological advantage of high-order squeezed states

Quantum correlations can be harnessed to improve the precision in parameter estimation beyond classical capabilities. Under a standard interferometric or rotation protocol, it is well established that the optimal single-mode Gaussian state is a standard squeezed vacuum, which enables Heisenberg limited precision. In this work, we investigate the potential metrological advantage of two distinct families involving high-order squeezing, namely, mth-phase and multisqueezed states. Our results show that these non-Gaussian states can grant a significant metrological advantage with respect to the optimal squeezed vacuum under equivalent conditions, i.e. at equal occupations. Their advantage holds both at low and large occupations, but its behavior critically depends on the chosen family of high-order squeezing. While higher squeezing orders enhance the advantage, this comes at the cost of higher-order observables in the measurement for full metrological performance. Finally, we study their robustness to standard decoherence channels, i.e. pure dephasing and zero-temperature damping. Employing standard squeezing as reference state, our results indicate a reasonable robustness against damping up to a certain noise strength, while their metrological advantage becomes fragile under pure dephasing. Our work shows the potential enhancement in quantum metrology beyond Gaussian states, carefully detailing the main challenges and limitations.

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Microwave-free vector magnetometry and crystal orientation determination with Nitrogen-Vacancy centers using Bayesian inference

Nitrogen-vacancy (NV) centers in diamond provide a solid-state platform for quantum sensing. While optically detected magnetic resonance techniques offer high sensitivity, their reliance on microwaves introduces heating and stray electromagnetic fields that can perturb nearby samples. Optical approaches based on cross-relaxation between differently oriented NV centers remove this constraint but have so far required stringent alignment of the external field with crystallographic axes, restricting their practicality. Here we introduce a general framework for microwave-free vector magnetometry at near-zero field that leverages Bayesian inference to extract both the magnetic field vector and the NV orientation directly from photoluminescence maps. An analytical model of cross-relaxation resonances enables efficient inference under arbitrary field and orientation configurations, while naturally incorporating the discrete degeneracies of the NV symmetry. We experimentally demonstrate robust orientation determination and vector-field reconstruction, establishing a general route toward compact and alignment-free NV magnetometers for practical sensing applications.

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Harnessing individual nitrogen-vacancy centers with a compact and portable confocal microscope

Recent advancements in quantum technology have highlighted the potential of nitrogen-vacancy (NV) centers in diamond. However, fully realizing this potential requires addressing challenges related to the size, complexity, and cost of current optical systems used for NV center manipulation. In this work, we present a compact and portable confocal setup specifically designed for the efficient detection and control of single NV centers. Our system facilitates optical initialization and readout of individual NV center photoluminescence signals, enabling coherent spin control and nanoscale-resolution magnetic field sensing.

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Control of open quantum systems via dynamical invariants

In this study, we address the challenge of controlling quantum systems under environmental influences using the theory of dynamical invariants. We employ a reverse engineering approach to develop control protocols designed to be robust against environmental noise and dissipation. This technique offers significant improvements over traditional quantum control methods by accounting for the time-dependent dissipation factor in the master equation, which results from modulating the system's Hamiltonian (the control fields). Additionally, our method obviates the need for iterative propagation of the system state, a resource-intensive process. The method can be applied to any open system dynamics that can be described using a time-dependent Master equation. We demonstrate the effectiveness and practicality of our approach through applications to two fundamental models: a two-level quantum system and a quantum harmonic oscillator, both interacting with a thermal bath.

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Regressions on quantum neural networks at maximal expressivity

We analyze the expressivity of a universal deep neural network that can be organized as a series of nested qubit rotations, accomplished by adjustable data re-uploads. While the maximal expressive power increases with the depth of the network and the number of qubits, it is fundamentally bounded by the data encoding mechanism. Focusing on regression problems, we systematically investigate the expressivity limits for different measurements and architectures. The presence of entanglement, either by entangling layers or global measurements, saturate towards this bound. In these cases, entanglement leads to an enhancement of the approximation capabilities of the network compared to local readouts of the individual qubits in non-entangling networks. We attribute this enhancement to a larger survival set of Fourier harmonics when decomposing the output signal.

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Invariant-based control of quantum many-body systems across critical points

Quantum many-body systems are emerging as key elements in the quest for quantum-based technologies and in the study of fundamental physics. In this study, we address the challenge of achieving fast and high-fidelity evolutions across quantum phase transitions, a crucial requirement for practical applications. We introduce a control technique based on dynamical invariants tailored to ensure adiabatic-like evolution within the lowest-energy subspace of the many-body systems described by the transverse-field Ising and long-range Kitaev models. By tuning the controllable parameter according to analytical control results, we achieve high-fidelity evolutions operating close to the speed limit. Remarkably, our approach leads to the breakdown of Kibble-Zurek scaling laws, offering tunable and significantly improved time scaling behavior. We provide detailed numerical simulations to illustrate our findings, demonstrating scalability with the system size and robustness against noisy controls and disorder, as well as its applicability to a non-integrable system.

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Characterization of the photon emission statistics in nitrogen-vacancy centers

We model and experimentally demonstrate the full time-dependent counting statistics of photons emitted by a single nitrogen-vacancy (NV) center in diamond under non-resonant laser excitation and resonant microwave control. A generalization of the quantum jump formalism for the seven electronic states involved in the fast intrinsic dynamics of an NV center provides a self-contained model that allows for the characterization of its emission and clarifies the relation between the quantum system internal states and the measurable detected photon counts. The model allows the elaboration of detection protocols to optimize the energy and time resources while maximizing the system sensitivity to magnetic-field measurements.

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Quantum control of tunable-coupling transmons using dynamical invariants of motion

We analyse the implementation of a fast nonadiabatic CZ gate between two transmon qubits with tuneable coupling. The gate control method is based on a theory of dynamical invariants which leads to reduced leakage and robustness against decoherence. The gate is based on a description of the resonance between the $|11\rangle$ and $|20\rangle$ using an effective Hamiltonian with the 6 lowest energy states. A modification of the invariants method allows us to take into account the higher-order perturbative corrections of this effective model. This enables a gate fidelity several orders of magnitude higher than other quasiadiabatic protocols, with gate times that approach the theoretical limit.

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Mutual Reinforcement between Neural Networks and Quantum Physics

Quantum machine learning emerges from the symbiosis of quantum mechanics and machine learning. In particular, the latter gets displayed in quantum sciences as: (i) the use of classical machine learning as a tool applied to quantum physics problems, (ii) or the use of quantum resources such as superposition, entanglement, or quantum optimization protocols to enhance the performance of classification and regression tasks compare to their classical counterparts. This paper reviews examples in these two scenarios. On the one hand, a classical neural network is applied to design a new quantum sensing protocol. On the other hand, the design of a quantum neural network based on the dynamics of a quantum perceptron with the application of shortcuts to adiabaticity gives rise to a short operation time and robust performance. These examples demonstrate the mutual reinforcement of both neural networks and quantum physics.

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