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arXiv:1911.12192 · New Journal of Physics

Extending qubit coherence by adaptive quantum environment learning

Eleanor Scerri, Erik M. Gauger, Cristian Bonato

Published Mar 1, 2020• 16 citations
arXivHTMLPDF

Abstract

Abstract Decoherence, resulting from unwanted interaction between a qubit and its environment, poses a serious challenge towards the development of quantum technologies. Recently, researchers have started analysing how real-time Hamiltonian learning approaches, based on estimating the qubit state faster than the environmental fluctuations, can be used to counteract decoherence. In this work, we investigate how the back-action of the quantum measurements used in the learning process can be harnessed to extend qubit coherence. We propose an adaptive protocol that, by learning the qubit environment, narrows down the distribution of possible environment states. While the outcomes of quantum measurements are random, we show that real-time adaptation of measurement settings (based on previous outcomes) allows a deterministic decrease of the width of the bath distribution, and hence an increase of the qubit coherence. We numerically simulate the performance of the protocol for the electronic spin of a nitrogen-vacancy centre in diamond subject to a dilute bath of 13C nuclear spin, finding a considerable improvement over the performance of non-adaptive strategies.

DOI
10.1088/1367-2630/ab7bf3

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Abstract Decoherence, resulting from unwanted interaction between a qubit and its environment, poses a serious challenge towards the development of quantum technologies. Recently, researchers have started analysing how real-time Hamiltonian learning approaches, based on estimating the qubit state faster than the environmental fluctuations, can be used to counteract decoherence. In this work, we investigate how the back-action of the quantum measurements used in the learning process can be harnessed to extend qubit coherence. We propose an adaptive protocol that, by learning the qubit environment, narrows down the distribution of possible environment states. While the outcomes of quantum measurements are random, we show that real-time adaptation of measurement settings (based on previous outcomes) allows a deterministic decrease of the width of the bath distribution, and hence an increase of the qubit coherence. We numerically simulate the performance of the protocol for the electronic spin of a nitrogen-vacancy centre in diamond subject to a dilute bath of 13C nuclear spin, finding a considerable improvement over the performance of non-adaptive strategies.
Unwanted interaction between a quantum system and its fluctuating environment leads to decoherence and is the primary obstacle to establishing a scalable quantum information processing architecture. Strategies such as environmental and materials engineering, quantum error correction and dynamical decoupling can mitigate decoherence, but generally increase experimental complexity. Here we improve coherence in a qubit using real-time Hamiltonian parameter estimation. Using a rapidly converging Bayesian approach, we precisely measure the splitting in a singlet-triplet spin qubit faster than the surrounding nuclear bath fluctuates. We continuously adjust qubit control parameters based on this information, thereby improving the inhomogenously broadened coherence time (T2*) from tens of nanoseconds to >2 μs. Because the technique demonstrated here is compatible with arbitrary qubit operations, it is a natural complement to quantum error correction and can be used to improve the performance of a wide variety of qubits in both meteorological and quantum information processing applications.
This paper investigates the development of an adaptive quantum state measurement protocol designed to enhance information extraction efficiency from quantum states while mitigating the detrimental effects of decoherence. The core idea revolves around employing a reinforcement learning algorithm to dynamically adjust the measurement basis, based on the observed quantum state characteristics and feedback signals generated during the measurement process. Real-time adjustments to measurement device parameters via quantum control techniques further optimize the measurement outcome. The proposed method addresses the limitations of traditional fixed-basis measurements, which are highly susceptible to decoherence. Through adaptive basis selection, this approach aims to maximize information extraction and minimize interference with the quantum state. The theoretical framework and underlying principles are presented, outlining the key components and their interrelationship. The potential impact of this technique on quantum information processing is discussed.
This paper presents a novel dynamic quantum decoherence correction algorithm designed to mitigate the detrimental effects of decoherence on quantum information processing. Decoherence, the loss of quantum coherence due to interactions with the environment, remains a significant obstacle to the reliable implementation of quantum technologies. Our algorithm leverages real-time monitoring and adaptive control strategies to actively counteract decoherence, aiming to preserve quantum information fidelity. The core principle involves precisely modulating the state of individual quantum bits (qubits) using quantum control techniques, effectively reducing the decoherence rate. We detail the mathematical framework underpinning the algorithm, including the relevant equations governing decoherence and the control pulses employed. The algorithm's dynamic nature allows it to respond to changes in the decoherence environment, offering a more robust solution compared to static correction methods. Simulation results (presented within) demonstrate the algorithm's efficacy in maintaining coherence for extended periods, highlighting its potential for applications in quantum communication, quantum computation, and quantum sensing. Finally, we outline potential future research directions and challenges associated with the broader implementation of this dynamic decoherence correction approach.
Preserving quantum coherence in the presence of environmental noise is one of the principal challenges for quantum technologies. Noise mitigation using spectator qubits (SQs) has recently emerged as a promising approach, enabling indirect probing of the noise without disturbing the data qubit (DQ). However, existing analyses that probe ultimate performance have been restricted to two-state random telegraph process noise, which does not capture more complex noise processes that may arise in the environment. Therefore, we here develop a SQ-based noise mitigation for DQs subject to general multi-level fluctuator noise. We first derive the coherence dynamics of the DQ under such noise, then develop a mitigation scheme in which information about the noise is inferred from sequential SQ measurements and used for phase correction. A memory-efficient heuristic adaptive protocol is proposed to dynamically select the SQ measurement time and angle based on the current noise estimate. Numerical simulations demonstrate that the proposed strategy significantly suppresses decoherence under multi-level noise, achieving performance comparable to that in the two-level case despite the increased complexity of the noise process.
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Environmental fluctuations degrade the performance of solid-state qubits but can in principle be mitigated by real-time Hamiltonian estimation down to timescales set by the estimation efficiency. We implement a physics-informed and an adaptive Bayesian estimation strategy and apply them in real time to a semiconductor spin qubit. The physics-informed strategy propagates a probability distribution inside the quantum controller according to the Fokker-Planck equation, appropriate for describing the effects of nuclear spin diffusion in gallium arsenide. Evaluating and narrowing the anticipated distribution by a predetermined qubit probe sequence enables improved dynamical tracking of the uncontrolled magnetic field gradient within the singlet-triplet qubit. The adaptive strategy replaces the probe sequence by a small number of qubit probe cycles, with each probe time conditioned on the previous measurement outcomes, thereby further increasing the estimation efficiency. The combined real-time estimation strategy efficiently tracks low-frequency nuclear spin fluctuations in solid-state qubits, and can be applied to other qubit platforms by tailoring the appropriate update equation to capture their distinct noise sources. Published by the American Physical Society 2024