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arXiv:2112.08409 · New Journal of Physics

Quantum model learning agent: characterisation of quantum systems through machine learning

Brian Flynn, Antonio A. Gentile, Nathan Wiebe, Raffaele Santagati, Anthony Laing

Published Apr 21, 2022• 11 citations
arXivHTMLPDF

Abstract

Abstract Accurate models of real quantum systems are important for investigating their behaviour, yet are difficult to distil empirically. Here, we report an algorithm—the quantum model learning agent (QMLA)—to reverse engineer Hamiltonian descriptions of a target system. We test the performance of QMLA on a number of simulated experiments, demonstrating several mechanisms for the design of candidate Hamiltonian models and simultaneously entertaining numerous hypotheses about the nature of the physical interactions governing the system under study. QMLA is shown to identify the true model in the majority of instances, when provided with limited a priori information, and control of the experimental setup. Our protocol can explore Ising, Heisenberg and Hubbard families of models in parallel, reliably identifying the family which best describes the system dynamics. We demonstrate QMLA operating on large model spaces by incorporating a genetic algorithm to formulate new hypothetical models. The selection of models whose features propagate to the next generation is based upon an objective function inspired by the Elo rating scheme, typically used to rate competitors in games such as chess and football. In all instances, our protocol finds models that exhibit F 1 score ⩾ 0.88 when compared with the true model, and it precisely identifies the true model in 72% of cases, whilst exploring a space of over 250 000 potential models. By testing which interactions actually occur in the target system, QMLA is a viable tool for both the exploration of fundamental physics and the characterisation and calibration of quantum devices.

DOI
10.1088/1367-2630/ac68ff

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Abstract Accurate models of real quantum systems are important for investigating their behaviour, yet are difficult to distil empirically. Here, we report an algorithm—the quantum model learning agent (QMLA)—to reverse engineer Hamiltonian descriptions of a target system. We test the performance of QMLA on a number of simulated experiments, demonstrating several mechanisms for the design of candidate Hamiltonian models and simultaneously entertaining numerous hypotheses about the nature of the physical interactions governing the system under study. QMLA is shown to identify the true model in the majority of instances, when provided with limited a priori information, and control of the experimental setup. Our protocol can explore Ising, Heisenberg and Hubbard families of models in parallel, reliably identifying the family which best describes the system dynamics. We demonstrate QMLA operating on large model spaces by incorporating a genetic algorithm to formulate new hypothetical models. The selection of models whose features propagate to the next generation is based upon an objective function inspired by the Elo rating scheme, typically used to rate competitors in games such as chess and football. In all instances, our protocol finds models that exhibit F 1 score ⩾ 0.88 when compared with the true model, and it precisely identifies the true model in 72% of cases, whilst exploring a space of over 250 000 potential models. By testing which interactions actually occur in the target system, QMLA is a viable tool for both the exploration of fundamental physics and the characterisation and calibration of quantum devices.
Characterizing quantum processes is crucial for the execution of quantum algorithms on available quantum devices. A powerful framework for this purpose is the Quantum Model Learning Agent (QMLA) which characterizes a given system by learning its Hamiltonian via adaptive generations of informative experiments and their validation against simulated models. Identifying the incoherent noise of a quantum device in addition to its coherent interactions is, however, as essential. Precise knowledge of such imperfections of a quantum device allows to devise strategies to mitigate detrimental effects, for example via quantum error correction. We introduce the open Quantum Model Learning Agent (oQMLA) framework to account for Markovian noise through the Liouvillian formalism. By simultaneously learning the Hamiltonian and jump operators, oQMLA independently captures both the coherent and incoherent dynamics of a system. The added complexity of open systems necessitates advanced algorithmic strategies. Among these, we implement regularization to steer the algorithm towards plausible models and an unbiased metric to evaluate the quality of the results. We validate our implementation in simulated scenarios of increasing complexity, demonstrating its robustness to hardware-induced measurement errors and its ability to characterize systems using only local operations. Additionally, we develop a scheme to interface oQMLA with a publicly available superconducting quantum computer, showcasing its practical utility. These advancements represent a significant step toward improving the performance of quantum hardware and contribute to the broader goal of advancing quantum technologies and their applications.
Quantum machine learning (QML) is a computational paradigm that seeks to apply quantum-mechanical resources to solve learning problems. As such, the goal of this framework is to leverage quantum processors to tackle optimization, supervised, unsupervised and reinforcement learning, and generative modeling-among other tasks-more efficiently than classical models. Here we offer a high level overview of QML, focusing on settings where the quantum device is the primary learning or data generating unit. We outline the field's tensions between practicality and guarantees, access models and speedups, and classical baselines and claimed quantum advantages-flagging where evidence is strong, where it is conditional or still lacking, and where open questions remain. By shedding light on these nuances and debates, we aim to provide a friendly map of the QML landscape so that the reader can judge when-and under what assumptions-quantum approaches may offer real benefits.
Quantum technologies exploit quantum mechanical processes to achieve outcomes beyond the reach of classical machinery.One of their most promising applications is quantum simulation, whereby particles, atoms and molecules can be examined thoroughly for the first time, having been beyond the scope of even the most powerful supercomputers.Models have been useful tools in understanding physical systems: these are mathematical structures encoding physical interactions, which allow us to predict how the system will behave under various conditions.Models of quantum systems are particularly difficult to design and test, owing to the huge computational resources required to represent them accurately.In this thesis, we introduce and develop an algorithm to characterise quantum systems efficiently, by inferring a model consistent with their observed dynamics.The Quantum Model Learning Agent (QMLA) is an extensible framework which permits the study of any quantum system of interest, by combining quantum simulation with state of the art machine learning.QMLA iteratively proposes candidate models and trains them against the target system, finally declaring a single model as the best representation for the system of interest.We describe QMLA and its implementation through open source software, before testing it under a series of physical scenarios.First, we consider idealised theoretical systems in simulation, verifying the core principles of QMLA.Next, we incorporate strategies for generating candidate models by exploiting the information QMLA has gathered to date; by incorporating a genetic algorithm within QMLA, we explore vast spaces of valid candidate models, with QMLA reliably identifying the precise target model.Finally, we apply QMLA to realistic quantum systems, including operating on experimental data measured from an electron spin in a nitrogen vacancy centre.QMLA is shown to be effective in all cases studied in this thesis; however, of greater interest is the platform it provides for examining quantum systems.QMLA can aid engineers in configuring experimental setups, facilitate calibration of near term quantum devices, and ultimately enable complete characterisation of natural quantum structures.This thesis marks the beginning of a new line of research, into automating the understanding of quantum mechanical systems.
Quantum Machine Learning (QML) is an amalgamation of quantum computing paradigms with machine learning models, providing significant prospects for solving complex problems. However, with the expansion of numerous third-party vendors in the Noisy Intermediate-Scale Quantum (NISQ) era of quantum computing, the security of QML models is of prime importance, particularly against reverse engineering, which could expose sensitive parameters and proprietary algorithms embedded within the models. We assume the untrusted third-party quantum cloud provider is an adversary having white-box access to the transpiled version of the user-designed trained QML model during inference. Although the adversary can steal and use the model without any modification, reverse engineering (RE) to extract the pre-transpiled copy of the QML circuit will enable re-transpilation and usage of the model for various hardware with completely different native gate sets and even different qubit technology. The information about the parameters (e.g., number of parameters, their placements, and optimized values) can allow further training of the QML model if the adversary plans to alter the QML model to tamper with the watermark and/or embed their own watermark or refine the model for other purposes. In this first effort to investigate the RE of QML circuits, we examine quantum classifiers by comparing the training accuracy of original and reverse-engineered models across various sizes (i.e., number of qubits and number of parametric layers) of Quantum Neural Networks (QNNs). We note that multi-qubit classifiers can be reverse-engineered under specific conditions with a mean error of order 10^-2 in a reasonable time. We also propose adding dummy rotation gates in the QML model with fixed parameters to increase the RE overhead for defense. For instance, an addition of 2 dummy qubits and 2 layers increases the overhead by ~1.76 times for a classifier with 2 qubits and 3 layers with a performance overhead of less than 9%. We note that RE is a very powerful attack model which warrants further efforts on defenses.
Abstract Traditional methods for determining critical parameters are often influenced by human factors. This study introduces a physics-inspired adaptive reinforcement learning framework that enables agents to autonomously interact with physical environments, simultaneously identifying both the critical temperature and various types of critical exponents in the Ising model with precision. Interestingly, our algorithm exhibits search behavior reminiscent of phase transitions, efficiently converging to target parameters regardless of initial conditions. Experimental results demonstrate that this method significantly outperforms traditional approaches, particularly in environments with strong perturbations. This study not only incorporates physical concepts into machine learning to enhance algorithm interpretability but also establishes a new paradigm for scientific exploration, transitioning from manual analysis to autonomous AI discovery.