Conceptual Understanding through Efficient Automated Design of Quantum Optical Experiments

Krenn, Mario; Kottmann, Jakob S.; Tischler, Nora; Aspuru‐Guzik, Alán

doi:10.1103/physrevx.11.031044

Cited by 31 publications

(39 citation statements)

References 69 publications

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“…However, K =True does not guarantee the generation of this state due to the possible interference between PMs is not encoded in the clauses. For this reason, solutions such as the complete graph (all possible edges are True) outputs K =True, although heuristic optimization algorithms such as Theseus [15] show that some states are not representable by graphs. For this reason, we mix this optimization strategies with Klaus to obtain and guarantee physical and interpretable solutions.…”

Section: B Logic Encodingmentioning

confidence: 99%

“…To be precise, our goal is to find a feasible photonic setup that generates an arbitrary quantum state. We benchmark our approach by comparing its performance with the best algorithm up to date, which is based on continuous optimization, Theseus [15]. To that aim, we will take advantage of the graph-theoretical representation that these setups can take, which can also be used for other quantum experiments such as gate-based quantum circuits or unitary operations generation.…”

Section: Introductionmentioning

confidence: 99%

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Design of quantum optical experiments with logic artificial intelligence

Cervera-Lierta¹,

Krenn²,

Aspuru‐Guzik³

2021

Preprint

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Logic artificial intelligence (AI) is a subfield of AI where variables can take two defined arguments, True or False, and are arranged in clauses that follow the rules of formal logic. Several problems that span from physical systems to mathematical conjectures can be encoded into these clauses and be solved by checking their satisfiability (SAT). Recently, SAT solvers have become a sophisticated and powerful computational tool capable, among other things, of solving long-standing mathematical conjectures. In this work, we propose the use of logic AI for the design of optical quantum experiments. We show how to map into a SAT problem the experimental preparation of an arbitrary quantum state and propose a logic-based algorithm, called Klaus, to find an interpretable representation of the photonic setup that generates it. We compare the performance of Klaus with the state-of-the-art algorithm for this purpose based on continuous optimization. We also combine both logic and numeric strategies to find that the use of logic AI improves significantly the resolution of this problem, paving the path to develop more formal-based approaches in the context of quantum physics experiments.

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Section: B Logic Encodingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Design of quantum optical experiments with logic artificial intelligence

Cervera-Lierta¹,

Krenn²,

Aspuru‐Guzik³

2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Some of these works show potential quantum advantages over their classical counterparts, which have boosted the development of quantum AI [25]. Although some recent efforts have been initiated to interpret behavior of AI in quantum optical experiments [42], a generally explainable quantum AI is still in its infancy.…”

Section: Introductionmentioning

confidence: 99%

Quantum Capsule Networks

Zidu¹,

Shen²,

Li³

et al. 2022

Preprint

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Capsule networks, which incorporate the paradigms of connectionism and symbolism, have brought fresh insights into artificial intelligence. The capsule, as the building block of capsule networks, is a group of neurons represented by a vector to encode different features of an entity. The information is extracted hierarchically through capsule layers via routing algorithms. Here, we introduce a quantum capsule network (dubbed QCap-sNet) together with a quantum dynamic routing algorithm. Our model enjoys an exponential speedup in the dynamic routing process and exhibits an enhanced representation power. To benchmark the performance of the QCapsNet, we carry out extensive numerical simulations on the classification of handwritten digits and symmetry-protected topological phases, and show that the QCapsNet can achieve the state-of-the-art accuracy and outperforms conventional quantum classifiers evidently. We further unpack the output capsule state and find that a particular subspace may correspond to a human-understandable feature of the input data, which indicates the potential explainability of such networks. Our work reveals an intriguing prospect of quantum capsule networks in quantum machine learning, which may provide a valuable guide towards explainable quantum artificial intelligence.

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“…Numerous variations have been developed since then. For example, genetic algorithms [11,12] coupled with neural networks [13], reinforcement-learning-based search [14], gradient-descent of a continuous experimental space [15,16] or efficient human-interpretable representations [17] and unsupervised deep generative models [18]. See a recent review about these developments [19].…”

Section: Introductionmentioning

confidence: 99%

Quantum Optical Experiments Modeled by Long Short-Term Memory

et al. 2021

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We demonstrate how machine learning is able to model experiments in quantum physics. Quantum entanglement is a cornerstone for upcoming quantum technologies, such as quantum computation and quantum cryptography. Of particular interest are complex quantum states with more than two particles and a large number of entangled quantum levels. Given such a multiparticle high-dimensional quantum state, it is usually impossible to reconstruct an experimental setup that produces it. To search for interesting experiments, one thus has to randomly create millions of setups on a computer and calculate the respective output states. In this work, we show that machine learning models can provide significant improvement over random search. We demonstrate that a long short-term memory (LSTM) neural network can successfully learn to model quantum experiments by correctly predicting output state characteristics for given setups without the necessity of computing the states themselves. This approach not only allows for faster search, but is also an essential step towards the automated design of multiparticle high-dimensional quantum experiments using generative machine learning models.

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Conceptual Understanding through Efficient Automated Design of Quantum Optical Experiments

Cited by 31 publications

References 69 publications

Design of quantum optical experiments with logic artificial intelligence

Design of quantum optical experiments with logic artificial intelligence

Quantum Capsule Networks

Quantum Optical Experiments Modeled by Long Short-Term Memory

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