Artificial Intelligence and Machine Learning for Quantum Technologies

In recent years, the dramatic progress in machine learning has begun to impact many areas of science and technology significantly. In the present perspective article, we explore how quantum technologies are benefiting from this revolution. We showcase in illustrative examples how scientists in the past few years have started to use machine learning and more broadly methods of artificial intelligence to analyze quantum measurements, estimate the parameters of quantum devices, discover new quantum experimental setups, protocols, and feedback strategies, and generally improve aspects of quantum computing, quantum communication, and quantum simulation. We highlight open challenges and future possibilities and conclude with some speculative visions for the next decade

Efficient cavity control with SNAP gates

Microwave cavities coupled to superconducting qubits have been demonstrated to be a promising platform for quantum information processing. A major challenge in this setup is to realize universal control over the cavity. A promising approach are selective number-dependent arbitrary phase (SNAP) gates combined with cavity displacements. It has been proven that this is a universal gate set, but a central question remained open so far: how can a given target operation be realized efficiently with a sequence of these operations. In this work, we present a practical scheme to address this problem. It involves a hierarchical strategy to insert new gates into a sequence, followed by a co-optimization of the control parameters, which generates short high-fidelity sequences. For a broad range of experimentally relevant applications, we find that they can be implemented with 3 to 4 SNAP gates, compared to up to 50 with previously known techniques

Quantum circuit optimization with deep reinforcement learning

A central aspect for operating future quantum computers is quantum circuit optimization, i.e., the search for efficient realizations of quantum algorithms given the device capabilities. In recent years, powerful approaches have been developed which focus on optimizing the high-level circuit structure. However, these approaches do not consider and thus cannot optimize for the hardware details of the quantum architecture, which is especially important for near-term devices. To address this point, we present an approach to quantum circuit optimization based on reinforcement learning. We demonstrate how an agent, realized by a deep convolutional neural network, can autonomously learn generic strategies to optimize arbitrary circuits on a specific architecture, where the optimization target can be chosen freely by the user. We demonstrate the feasibility of this approach by training agents on 12-qubit random circuits, where we find on average a depth reduction by 27% and a gate count reduction by 15%. We examine the extrapolation to larger circuits than used for training, and envision how this approach can be utilized for near-term quantum devices

Deep Learning of Quantum Many-Body Dynamics via Random Driving

Neural networks have emerged as a powerful way to approach many practical problems in quantumphysics. In this work, we illustrate the power of deep learning to predict the dynamics of a quantummany-body system, where the training is based purely on monitoring expectation values of observables under random driving. The trained recurrent network is able to produce accurate predictions for driving trajectories entirely different than those observed during training. As a proof of principle, here we train the network on numerical data generated from spin models, showing that it can learn the dynamics of observables of interest without needing information about the full quantum state.This allows our approach to be applied eventually to actual experimental data generated from aquantum many-body system that might be open, noisy, or disordered, without any need for a detailedunderstanding of the system. This scheme provides considerable speedup for rapid explorations andpulse optimization. Remarkably, we show the network is able to extrapolate the dynamics to times longer than those it has been trained on, as well as to the infinite-system-size limit

Realizing a deep reinforcement learning agent discovering real-time feedback control strategies for a quantum system

To realize the full potential of quantum technologies, finding good strategies to control quantum information processing devices in real time becomes increasingly important. Usually these strategies require a precise understanding of the device itself, which is generally not available. Model-free reinforcement learning circumvents this need by discovering control strategies from scratch without relying on an accurate description of the quantum system. Furthermore, important tasks like state preparation, gate teleportation and error correction need feedback at time scales much shorter than the coherence time, which for superconducting circuits is in the microsecond range. Developing and training a deep reinforcement learning agent able to operate in this real-time feedback regime has been an open challenge. Here, we have implemented such an agent in the form of a latency-optimized deep neural network on a field-programmable gate array (FPGA). We demonstrate its use to efficiently initialize a superconducting qubit into a target state. To train the agent, we use model-free reinforcement learning that is based solely on measurement data. We study the agent’s performance for strong and weak measurements, and for three-level readout, and compare with simple strategies based on thresholding. This demonstration motivates further research towards adoption of reinforcement learning for real-time feedback control of quantum devices and more generally any physical system requiring learnable low-latency feedback control

Photoproduction of pi0 omega off protons for E(gamma) < 3 GeV

Differential and total cross-sections for photoproduction of gamma proton to proton pi0 omega and gamma proton to Delta+ omega were determined from measurements of the CB-ELSA experiment, performed at the electron accelerator ELSA in Bonn. The measurements covered the photon energy range from the production threshold up to 3GeV.Comment: 8 pages, 13 figure

Learning Control of Quantum Systems

This paper provides a brief introduction to learning control of quantum systems. In particular, the following aspects are outlined, including gradient-based learning for optimal control of quantum systems, evolutionary computation for learning control of quantum systems, learning-based quantum robust control, and reinforcement learning for quantum control.Comment: 9 page

Neutral pion photoproduction off protons in the energy range 0.3 GeV < E(gamma) < 3 GeV

Single pi0 photoproduction has been studied with the CB-ELSA experiment at Bonn using tagged photon energies between 0.3 and 3.0 GeV. The experimental setup covers a very large solid angle of about 98% of 4 pi. Differential cross sections (d sigma)/(d Omega) have been measured. Complicated structures in the angular distributions indicate a variety of different resonances being produced in the s channel intermediate state gamma p --> N* (Delta*) --> p pi0. A combined analysis including the data presented in this letter along with other data sets reveals contributions from known resonances and evidence for a new resonance N(2070)D15.Comment: LaTeX file, 4 pages, 4 encapsulated postscript figures, submitted to Phys. Rev. Lett. The publication of hep-ex/0407022 is accompanied by hep-ex/0311045 on photoproduction of eta mesons. Reference [3]: changed, reference [17]: citation added. Figure 3, 4: SAID added up to 2 GeV for comparison, update

Photoproduction of eta mesons off protons for photon energies from 0.75 GeV to 3 GeV

Total and differential cross sections for the reaction p(gamma, eta)p have been measured for photon energies in the range from 750 MeV to 3 GeV. The low-energy data are dominated by the S11 wave which has two poles in the energy region below 2 GeV. Eleven nucleon resonances are observed in their decay into p eta. At medium energies we find evidence for a new resonance N(2070)D15 with (mass, width) = (2068+-22, 295+-40) MeV. At photon energies above 1.5 GeV, a strong peak in forward direction develops, signalling the exchange of vector mesons in the t channel.Comment: LaTeX, 4 pages including 4 eps-figures, submitted to Phys. Rev. Lett. The publication of hep-ex/0311045 is accompanied by hep-ex/0407022 on photoproduction of neutral pions, submitted to Phys. Rev. Lett. Fits published in the latest version are based on additional data, new beam asymmetry data from GRAAL are included, for instance. The data demanded more resonant contributions which were studied in detail. PWA reference adde

N* and Delta* decays into N pi0 pi0

Decays of baryon resonances in the second and the third resonance region into N pi0 pi0 are studied by photoproduction of two neutral pions off protons. Partial decay widths of N* and Delta* resonances decaying into Delta(1232) pi, N(\pi\pi)_{S}, N(1440)P_{11} pi, and N(1520)D_{13} pi are determined in a partial wave analysis of this data, and data from other reactions. Several partial decay widths were not known before. Interesting decay patterns are observed which are not even qualitatively reproduced by quark model calculations. In the second resonance region, decays into Delta(1232) pi dominate clearly. The N(\pi\pi)_{S}-wave provides a significant contribution to the cross section, especially in the third resonance region. The P_{13}(1720) properties found here are at clear variance to PDG values.Comment: 13 pages, 4 figures, long author's lis