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New Journal of Physics

Ultrafast artificial intelligence: machine learning with atomic-scale quantum systems

Thomas Pfeifer, M. Wollenhaupt, Manfred Lein

Published Aug 28, 2024• 1 citation
Publisher

Abstract

Abstract We train a model atom to recognize pixel-drawn digits based on hand-written numbers in the range 0–9, employing intense light–matter interaction as a computational resource. For training, the images of the digits are converted into shaped laser pulses (data input pulses). Simultaneously with an input pulse, another shaped pulse (program pulse), polarized in the orthogonal direction, is applied to the atom and the system evolves quantum mechanically according to the time-dependent Schrödinger equation. The purpose of the optimal program pulse is to direct the system into specific atomic final states (classification states) that correspond to the input digits. A success rate of about 40% is achieved when using a basic optimization scheme that might be limited by the computational resources for finding the optimal program pulse in a high-dimensional search space. Our key result is the demonstration that the laser-programmed atom is able to generalize, i.e. successful classification is not limited to the training examples, but also the classification of previously unseen images is improved by training. This atom-sized machine-learning image-recognition scheme operates on time scales down to tens of femtoseconds, is scalable towards larger (e.g. molecular) systems, and is readily reprogrammable towards other learning/classification tasks. An experimental implementation of the scheme using ultrafast polarization pulse shaping and differential photoelectron detection is within reach.

DOI
10.1088/1367-2630/ad7492

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Abstract We train a model atom to recognize pixel-drawn digits based on hand-written numbers in the range 0–9, employing intense light–matter interaction as a computational resource. For training, the images of the digits are converted into shaped laser pulses (data input pulses). Simultaneously with an input pulse, another shaped pulse (program pulse), polarized in the orthogonal direction, is applied to the atom and the system evolves quantum mechanically according to the time-dependent Schrödinger equation. The purpose of the optimal program pulse is to direct the system into specific atomic final states (classification states) that correspond to the input digits. A success rate of about 40% is achieved when using a basic optimization scheme that might be limited by the computational resources for finding the optimal program pulse in a high-dimensional search space. Our key result is the demonstration that the laser-programmed atom is able to generalize, i.e. successful classification is not limited to the training examples, but also the classification of previously unseen images is improved by training. This atom-sized machine-learning image-recognition scheme operates on time scales down to tens of femtoseconds, is scalable towards larger (e.g. molecular) systems, and is readily reprogrammable towards other learning/classification tasks. An experimental implementation of the scheme using ultrafast polarization pulse shaping and differential photoelectron detection is within reach.
We train a model atom to recognize pixel-drawn digits based on hand-written numbers in the range 0-9, employing intense light-matter interaction as a computational resource. For training, the images of the digits are converted into shaped laser pulses (data input pulses). Simultaneously with an input pulse, another shaped pulse (program pulse), polarized in the orthogonal direction, is applied to the atom and the system evolves quantum mechanically according to the time-dependent Schrödinger equation. The purpose of the optimal program pulse is to direct the system into specific atomic final states (classification states) that correspond to the input digits. A success rate of about 40% is achieved when using a basic optimization scheme that might be limited by the computational resources for finding the optimal program pulse in a high-dimensional search space. Our key result is the demonstration that the laser-programmed atom is able to generalize, i.e. successful classification is not limited to the training examples, but also the classification of previously unseen images is improved by training. This atom-sized machine-learning image-recognition scheme operates on time scales down to tens of femtoseconds, is scalable towards larger (e.g. molecular) systems, and is readily reprogrammable towards other learning/classification tasks. An experimental implementation of the scheme using ultrafast polarization pulse shaping and differential photoelectron detection is within reach.
We train a model atom to recognize hand-written digits between 0 and 9, employing intense light--matter interaction as a computational resource. For training, individual images of hand-written digits in the range 0-9 are converted into shaped laser pulses (data input pulses). Simultaneously with an input pulse, another shaped pulse (program pulse), polarized in the orthogonal direction, is applied to the atom and the system evolves quantum mechanically according to the time-dependent Schrödinger equation. The purpose of the optimal program pulse is to direct the system into specific atomic final states that correspond to the input digits. A success rate of about 40\% is demonstrated here for a basic optimization scheme, so far limited by the computational power to find the optimal program pulse in a high-dimensional search space. This atomic-intelligence image-recognition scheme is scalable towards larger (e.g. molecular) systems, is readily reprogrammable towards other learning/classification tasks and operates on time scales down to tens of femtoseconds. It has the potential to outpace other currently implemented machine-learning approaches, including the fastest optical on-chip neuromorphic systems and optical accelerators, by orders of magnitude.
Machine learning, with promising applications in quantum computation, has been introduced to a variety of quantum mechanical platforms, where its interplay with quantum physics offers exciting prospects toward quantum advantages. A central difficulty, however, lies in the access and control of the large Hilbert space required by quantum machine learning protocols, through the limited number of noisy qubits available to near-term quantum devices. Whereas it is recognized that a viable solution lies in the design of quantum algorithms that incorporates quantum entanglement and interference, a demonstration of quantum machine learning protocols capable of solving practical tasks is still lacking. Here we report the classification of real-life, hand-drawn images on a quantum mechanical platform of single photons. Adopting a tensor-network-based machine learning algorithm with an entanglement-guided optimization, we achieve an efficient representation of the quantum feature space using matrix product states. This allows us to demonstrate image classification with a high success rate using single-photon interferometry networks. Our experiment establishes a general and scalable framework for quantum machine learning, which is readily accessible on other physical platforms.
We have constructed an automated learning apparatus to control quantum systems. By directing intense shaped ultrafast laser pulses into a variety of samples and using a measurement of the system as a feedback signal, we are able to reshape the laser pulses to direct the system into a desired state. The feedback signal is the input to an adaptive learning algorithm. This algorithm programs a computer-controlled, acousto-optic modulator pulse shaper. The learning algorithm generates new shaped laser pulses based on the success of previous pulses in achieving a predetermined goal.
We have constructed an automated learning apparatus to control quantum systems. By directing intense shaped ultrafast laser pulses into a variety of samples and using a measurement of the system as a feedback signal, we are able to reshape the laser pulses to direct the quantum system into a desired physical state. The feedback signal is the input to an adaptive learning algorithm. This algorithm programs a computer-controlled, acousto-optic modulator (AOM) pulse shaper. The learning algorithm generates new shaped laser pulses based on the success of previous pulses in achieving a predetermined goal.
Abstract Classification is a central task in deep learning algorithms. Usually, images are first captured and then processed by a sequence of operations, of which the artificial neuron represents one of the fundamental units. This paradigm requires significant resources that scale (at least) linearly in the image resolution, both in terms of photons and computational operations. Here, we present a quantum optical pattern recognition method for binary classification tasks. It classifies objects without reconstructing their images, using the rate of two-photon coincidences at the output of a Hong-Ou-Mandel interferometer, where both the input and the classifier parameters are encoded into single-photon states. Our method exhibits the behaviour of a classical neuron of unit depth. Once trained, it shows a constant O(1){{\mathcal{O}}}(1)O(1) O ( 1 ) complexity in the number of computational operations and photons required by a single classification. This is a superexponential advantage over a classical artificial neuron.