15 research outputs found
No-Collapse Accurate Quantum Feedback Control via Conditional State Tomography
The effectiveness of measurement-based feedback control (MBFC) protocols is
hampered by the presence of measurement noise, which affects the ability to
accurately infer the underlying dynamics of a quantum system from noisy
continuous measurement records to determine an accurate control strategy. To
circumvent such limitations, this work explores a real-time stochastic state
estimation approach that enables noise-free monitoring of the conditional
dynamics including the full density matrix of the quantum system using noisy
measurement records within a single quantum trajectory -- a method we name as
`conditional state tomography'. This, in turn, enables the development of
precise MBFC strategies that lead to effective control of quantum systems by
essentially mitigating the constraints imposed by measurement noise and has
potential applications in various feedback quantum control scenarios. This
approach is particularly useful for reinforcement-learning (RL)-based control,
where the RL-agent can be trained with arbitrary conditional averages of
observables, and/or the full density matrix as input (observation), to quickly
and accurately learn control strategies.Comment: 4 pages, 4 figures + 12 page supplementar
Hydration Properties of HnPO4nâ3 (n = 0â3) From Ab Initio Molecular Dynamics Simulations
For a comprehensive and detailed microscopic understanding of the hydration properties of primary aqueous phosphorus species of valence states V (viz., H3PO4, H2PO4â, HPO42â, and PO43â), a series of extensive ab initio molecular dynamics simulations is conducted at ambient temperature. In each of these cases, the spatially resolved, three-dimensional hydration shells are computed, allowing for a direct microscopic visual understanding of the hydration shells around the species. Since these species are excellent agents for the formation of hydrogen bonds (H-bonds) in water, which determine a wide range of their structural, dynamic, and spectroscopic features, a detailed analysis of the qualitative and quantitative aspects of the H-bonds, including their lifetime calculations, is performed. Vibrational density of states (VDOS) is calculated for each of the species in solute phases, resolved for each H-bonding site, and compared against the gas-phase normal modes of H3PO4 for the purpose of understanding the signatures of the peaks in VDOS plots and, in particular, the effects of solvation and H-bonding mechanisms. The results are well in line with available experimental data and other recent computer-aided studies in the literature
Accelerated motional cooling with deep reinforcement learning
Achieving fast cooling of motional modes is a prerequisite for leveraging such bosonic quanta for high-speed quantum information processing. In this Letter, we address the aspect of reducing the time limit for cooling, below that constrained by the conventional sideband cooling techniques, and propose a scheme to apply deep reinforcement learning (DRL) to achieve this. In particular, we have numerically demonstrated how the scheme can be used effectively to accelerate the dynamic motional cooling of a macroscopic magnonic sphere, and how it can be uniformly extended to more complex systems, for example, a tripartite opto-magno-mechanical system, to obtain cooling of the motional mode below the time bound of coherent cooling. While conventional sideband cooling methods do not work beyond the well-known rotating wave approximation (RWA) regimes, our proposed DRL scheme can be applied uniformly to regimes operating within and beyond the RWA, and thus, this offers a new and complete toolkit for rapid control and generation of macroscopic quantum states for application in quantum technologies.journal articl
Accelerated Magnonic Motional Cooling with Deep Reinforcement Learning
Achieving fast cooling of motional modes is a prerequisite for leveraging
such bosonic quanta for high-speed quantum information processing. In this
work, we address the aspect of reducing the time limit for cooling below that
constrained by the conventional sideband cooling techniques; and propose a
scheme to apply deep reinforcement learning (DRL) to achieve this. In
particular, we have shown how the scheme can be used effectively to accelerate
the dynamic motional cooling of a macroscopic magnonic sphere, and how it can
be uniformly extended for more complex systems, for example, a tripartite
opto-magno-mechanical system to obtain cooling of the motional mode below the
time bound of coherent cooling. While conventional sideband cooling methods do
not work beyond the well-known rotating wave approximation (RWA) regimes, our
proposed DRL scheme can be applied uniformly to regimes operating within and
beyond the RWA, and thus this offers a new and complete toolkit for rapid
control and generation of macroscopic quantum states for application in quantum
technologies.Comment: 15 pages, 10 figures (including supplemental material
Boosting the Gottesman-Kitaev-Preskill quantum error correction with non-Markovian feedback
Bosonic codes allow the encoding of a logical qubit in a single component
device, utilizing the infinitely large Hilbert space of a harmonic oscillator.
In particular, the Gottesman-Kitaev-Preskill code has recently been
demonstrated to be correctable well beyond the break-even point of the best
passive encoding in the same system. Current approaches to quantum error
correction (QEC) for this system are based on protocols that use feedback, but
the response is based only on the latest measurement outcome. In our work, we
use the recently proposed Feedback-GRAPE (Gradient Ascent Pulse Engineering
with Feedback) method to train a recurrent neural network that provides a QEC
scheme based on memory, responding in a non-Markovian way to the full history
of previous measurement outcomes, optimizing all subsequent unitary operations.
This approach significantly outperforms current strategies and paves the way
for more powerful measurement-based QEC protocols.Comment: 15 pages, 16 figure
Measurement based estimator scheme for continuous quantum error correction
Canonical discrete quantum error correction (DQEC) schemes use projective von
Neumann measurements on stabilizers to discretize the error syndromes into a
finite set, and fast unitary gates are applied to recover the corrupted
information. Quantum error correction (QEC) based on continuous measurement,
known as continuous quantum error correction (CQEC), in principle, can be
executed faster than DQEC and can also be resource efficient. However, CQEC
requires meticulous filtering of noisy continuous measurement data to reliably
extract error syndromes on the basis of which errors could be detected. In this
paper, we show that by constructing a measurement-based estimator (MBE) of the
logical qubit to be protected, which is driven by the noisy continuous
measurement currents of the stabilizers, it is possible to accurately track the
errors occurring on the physical qubits in real time. We use this MBE to
develop a continuous quantum error correction (MBE-CQEC) scheme that can
protect the logical qubit to a high degree, surpassing the performance of DQEC,
and also allows QEC to be conducted either immediately or in delayed time with
instantaneous feedbacks.Comment: 10 pages, 4 figures, journal articl
Measurement-Based Feedback Quantum Control with Deep Reinforcement Learning for a Double-Well Nonlinear Potential
Closed loop quantum control uses measurement to control the dynamics of a quantum system to achieve either a desired target state or target dynamics. In the case when the quantum Hamiltonian is quadratic in x and p, there are known optimal control techniques to drive the dynamics toward particular states, e.g., the ground state. However, for nonlinear Hamiltonian such control techniques often fail. We apply deep reinforcement learning (DRL), where an artificial neural agent explores and learns to control the quantum evolution of a highly nonlinear system (double well), driving the system toward the ground state with high fidelity. We consider a DRL strategy which is particularly motivated by experiment where the quantum system is continuously but weakly measured. This measurement is then fed back to the neural agent and used for training. We show that the DRL can effectively learn counterintuitive strategies to cool the system to a nearly pure âcatâ state, which has a high overlap fidelity with the true ground state
Measurement-based estimator scheme for continuous quantum error correction
Canonical discrete quantum error correction (DQEC) schemes use projective von Neumann measurements on stabilizers to discretize the error syndromes into a finite set, and fast unitary gates are applied to recover the corrupted information. Quantum error correction (QEC) based on continuous measurement, known as continuous quantum error correction (CQEC), in principle, can be executed faster than DQEC and can also be resource efficient. However, CQEC requires meticulous filtering of noisy continuous measurement data to reliably extract error syndromes on the basis of which errors could be detected. In this paper, we show that by constructing a measurement-based estimator (MBE) of the logical qubit to be protected, which is driven by the noisy continuous measurement currents of the stabilizers, it is possible to accurately track the errors occurring on the physical qubits in real time. We use this MBE to develop a continuous quantum error correction (MBE-CQEC) scheme that can protect the logical qubit to a high degree, surpassing the performance of DQEC, and also allows QEC to be conducted either immediately or in delayed time with instantaneous feedbacks
Spatially resolved hydration shells and dynamics of different sulfur species in water from first-principle molecular dynamics simulations
A comprehensive ab initio molecular dynamics (AIMD) simulation study is performed on the waterborne S-IV and S-VI species for different oxidation states and characterized for their spatial hydration nature, hydrogen bonding (H-bonding) and spectroscopic aspects. The vibrational modes of the power spectra and the influence of H-bonding for each species in the condensed phases are characterized by comparing with the normal modes of the species in the gas phase. In particular, it has been found that the H-bonds formed by SâIV species are more in number than those of SâVI species per oxygen site and are at least 2 times more durable than those for the latter. It has been predicted that such characteristics of H-bonds will significantly change the transport characteristics of the species
First-Principle Molecular Dynamics Investigation of Waterborne AsâV Species
The toxicity, mobility,
and geochemical behaviors of arsenic are
known to vary enormously with its speciation and oxidation states.
The present work details results on the basis of ab initio molecular
dynamics analysis of various waterborne As-V species, namely, H<sub>3</sub>AsO<sub>4</sub>, H<sub>2</sub>AsO<sub>4</sub><sup>â</sup>, HAsO<sub>4</sub><sup>2â</sup>, and AsO<sub>4</sub><sup>3â</sup>. The nature of hydrogen bonding of these species with water and
its influence on the solvent structure and relaxation behavior are
discussed. Useful microscopic insights on the structural and spectroscopic
signatures of the species in aqueous media are reported. Comparison
of normal-mode frequencies of the species in gas phases to the vibrational
density of states in solution provides insights on the influences
of solvation and H bonding. The results are compared with the previous
experimental and simulation studies, where available