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arXiv:2112.01105 · Philosophy of Science

How to Engineer a Quantum Wavefunction

Peter W. Evans, Dominik Hangleiter, Karim P. Y. Thébault

Published Jun 20, 2023• 2 citations
arXivHTMLPDF

Abstract

Abstract In a conventional experiment, scientists typically aim to learn about target systems by manipulating source systems of the same material type. In an analogue quantum simulation, by contrast, scientists typically aim to learn about target quantum systems of one material type via an experiment on a source quantum system of a different material type. In this article, we argue that such inferences can be justified by reference to source and target quantum systems being of the same empirical type. We illustrate this novel experimental practice of wavefunction engineering with reference to the example of Bose–Hubbard systems.

DOI
10.1017/psa.2023.87

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Abstract In a conventional experiment, scientists typically aim to learn about target systems by manipulating source systems of the same material type. In an analogue quantum simulation, by contrast, scientists typically aim to learn about target quantum systems of one material type via an experiment on a source quantum system of a different material type. In this article, we argue that such inferences can be justified by reference to source and target quantum systems being of the same empirical type. We illustrate this novel experimental practice of wavefunction engineering with reference to the example of Bose–Hubbard systems.
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Electrical and mechanical engineers, materials scientists and applied physicists will find Levi's uniquely practical 2006 explanation of quantum mechanics invaluable. This updated and expanded edition of the bestselling original text covers quantization of angular momentum and quantum communication, and problems and additional references are included. Using real-world engineering examples to engage the reader, the author makes quantum mechanics accessible and relevant to the engineering student. Numerous illustrations, exercises, worked examples and problems are included; Matlab source codes to support the text are available from www.cambridge.org//9780521183994
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.
The endeavour to control increasingly larger systems of particles at the quantum level is a natural goal, and will be a driving force for the physical sciences in the coming decades. The control of a many-body system at the highest level possible can indeed be regarded as the ultimate form of engineering. Within this general research avenue, building quantum simulators and performing experimental quantum simulations will play a key role. A quantum simulator is a promising candidate to become the first application of quantum information science reaching beyond classical limitations [ 1 ], since the requirements on the number of quantum particles and fidelities of operations are predicted to be substantially relaxed compared to that envisioned for a universal quantum computer. This issue forms an extensive open-access resource spanning the various areas of experimental quantum simulation, from its relation to quantum information processing to its potential use for different applications.