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1. Geometric deep optical sensing

   https://doi.org/10.1126/science.ade1220  

   Yuan, Shaofan ; Ma, Chao ; Fetaya, Ethan ; Mueller, Thomas ; Naveh, Doron ; Zhang, Fan ; Xia, Fengnian ( March 2023 , Science)

   BACKGROUND Optical sensing devices measure the rich physical properties of an incident light beam, such as its power, polarization state, spectrum, and intensity distribution. Most conventional sensors, such as power meters, polarimeters, spectrometers, and cameras, are monofunctional and bulky. For example, classical Fourier-transform infrared spectrometers and polarimeters, which characterize the optical spectrum in the infrared and the polarization state of light, respectively, can occupy a considerable portion of an optical table. Over the past decade, the development of integrated sensing solutions by using miniaturized devices together with advanced machine-learning algorithms has accelerated rapidly, and optical sensing research has evolved into a highly interdisciplinary field that encompasses devices and materials engineering, condensed matter physics, and machine learning. To this end, future optical sensing technologies will benefit from innovations in device architecture, discoveries of new quantum materials, demonstrations of previously uncharacterized optical and optoelectronic phenomena, and rapid advances in the development of tailored machine-learning algorithms. ADVANCES Recently, a number of sensing and imaging demonstrations have emerged that differ substantially from conventional sensing schemes in the way that optical information is detected. A typical example is computational spectroscopy. In this new paradigm, a compact spectrometer first collectively captures the comprehensive spectral information of an incident light beam using multiple elements or a single element under different operational states and generates a high-dimensional photoresponse vector. An advanced algorithm then interprets the vector to achieve reconstruction of the spectrum. This scheme shifts the physical complexity of conventional grating- or interference-based spectrometers to computation. Moreover, many of the recent developments go well beyond optical spectroscopy, and we discuss them within a common framework, dubbed “geometric deep optical sensing.” The term “geometric” is intended to emphasize that in this sensing scheme, the physical properties of an unknown light beam and the corresponding photoresponses can be regarded as points in two respective high-dimensional vector spaces and that the sensing process can be considered to be a mapping from one vector space to the other. The mapping can be linear, nonlinear, or highly entangled; for the latter two cases, deep artificial neural networks represent a natural choice for the encoding and/or decoding processes, from which the term “deep” is derived. In addition to this classical geometric view, the quantum geometry of Bloch electrons in Hilbert space, such as Berry curvature and quantum metrics, is essential for the determination of the polarization-dependent photoresponses in some optical sensors. In this Review, we first present a general perspective of this sensing scheme from the viewpoint of information theory, in which the photoresponse measurement and the extraction of light properties are deemed as information-encoding and -decoding processes, respectively. We then discuss demonstrations in which a reconfigurable sensor (or an array thereof), enabled by device reconfigurability and the implementation of neural networks, can detect the power, polarization state, wavelength, and spatial features of an incident light beam. OUTLOOK As increasingly more computing resources become available, optical sensing is becoming more computational, with device reconfigurability playing a key role. On the one hand, advanced algorithms, including deep neural networks, will enable effective decoding of high-dimensional photoresponse vectors, which reduces the physical complexity of sensors. Therefore, it will be important to integrate memory cells near or within sensors to enable efficient processing and interpretation of a large amount of photoresponse data. On the other hand, analog computation based on neural networks can be performed with an array of reconfigurable devices, which enables direct multiplexing of sensing and computing functions. We anticipate that these two directions will become the engineering frontier of future deep sensing research. On the scientific frontier, exploring quantum geometric and topological properties of new quantum materials in both linear and nonlinear light-matter interactions will enrich the information-encoding pathways for deep optical sensing. In addition, deep sensing schemes will continue to benefit from the latest developments in machine learning. Future highly compact, multifunctional, reconfigurable, and intelligent sensors and imagers will find applications in medical imaging, environmental monitoring, infrared astronomy, and many other areas of our daily lives, especially in the mobile domain and the internet of things. Schematic of deep optical sensing. The n -dimensional unknown information ( w ) is encoded into an m -dimensional photoresponse vector ( x ) by a reconfigurable sensor (or an array thereof), from which w′ is reconstructed by a trained neural network ( n ′ = n and w′   ≈   w ). Alternatively, x may be directly deciphered to capture certain properties of w . Here, w , x , and w′ can be regarded as points in their respective high-dimensional vector spaces ℛ n , ℛ m , and ℛ n ′ . 

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2. Topological on-chip lasers

   https://doi.org/10.1063/5.0150421  

   Li, Zhitong ; Luo, Xi-Wang ; Gu, Qing ( July 2023 , APL Photonics)

   A miniature on-chip laser is an essential component of photonic integrated circuits for a plethora of applications, including optical communication and quantum information processing. However, the contradicting requirements of small footprint, robustness, single-mode operation, and high output power have led to a multi-decade search for the optimal on-chip laser design. During this search, topological phases of matter—conceived initially in electronic materials in condensed matter physics—were successfully extended to photonics and applied to miniature laser designs. Benefiting from the topological protection, a topological edge mode laser can emit more efficiently and more robustly than one emitting from a trivial bulk mode. In addition, single-mode operation over a large range of excitation energies can be achieved by strategically manipulating topological modes in a laser cavity. In this Perspective, we discuss the recent progress of topological on-chip lasers and an outlook on future research directions.

    

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3. Provably efficient machine learning for quantum many-body problems

   https://doi.org/10.1126/science.abk3333  

   Huang, Hsin-Yuan ; Kueng, Richard ; Torlai, Giacomo ; Albert, Victor V. ; Preskill, John ( September 2022 , Science)

   INTRODUCTION Solving quantum many-body problems, such as finding ground states of quantum systems, has far-reaching consequences for physics, materials science, and chemistry. Classical computers have facilitated many profound advances in science and technology, but they often struggle to solve such problems. Scalable, fault-tolerant quantum computers will be able to solve a broad array of quantum problems but are unlikely to be available for years to come. Meanwhile, how can we best exploit our powerful classical computers to advance our understanding of complex quantum systems? Recently, classical machine learning (ML) techniques have been adapted to investigate problems in quantum many-body physics. So far, these approaches are mostly heuristic, reflecting the general paucity of rigorous theory in ML. Although they have been shown to be effective in some intermediate-size experiments, these methods are generally not backed by convincing theoretical arguments to ensure good performance. RATIONALE A central question is whether classical ML algorithms can provably outperform non-ML algorithms in challenging quantum many-body problems. We provide a concrete answer by devising and analyzing classical ML algorithms for predicting the properties of ground states of quantum systems. We prove that these ML algorithms can efficiently and accurately predict ground-state properties of gapped local Hamiltonians, after learning from data obtained by measuring other ground states in the same quantum phase of matter. Furthermore, under a widely accepted complexity-theoretic conjecture, we prove that no efficient classical algorithm that does not learn from data can achieve the same prediction guarantee. By generalizing from experimental data, ML algorithms can solve quantum many-body problems that could not be solved efficiently without access to experimental data. RESULTS We consider a family of gapped local quantum Hamiltonians, where the Hamiltonian H ( x ) depends smoothly on m parameters (denoted by x ). The ML algorithm learns from a set of training data consisting of sampled values of x , each accompanied by a classical representation of the ground state of H ( x ). These training data could be obtained from either classical simulations or quantum experiments. During the prediction phase, the ML algorithm predicts a classical representation of ground states for Hamiltonians different from those in the training data; ground-state properties can then be estimated using the predicted classical representation. Specifically, our classical ML algorithm predicts expectation values of products of local observables in the ground state, with a small error when averaged over the value of x . The run time of the algorithm and the amount of training data required both scale polynomially in m and linearly in the size of the quantum system. Our proof of this result builds on recent developments in quantum information theory, computational learning theory, and condensed matter theory. Furthermore, under the widely accepted conjecture that nondeterministic polynomial-time (NP)–complete problems cannot be solved in randomized polynomial time, we prove that no polynomial-time classical algorithm that does not learn from data can match the prediction performance achieved by the ML algorithm. In a related contribution using similar proof techniques, we show that classical ML algorithms can efficiently learn how to classify quantum phases of matter. In this scenario, the training data consist of classical representations of quantum states, where each state carries a label indicating whether it belongs to phase A or phase B . The ML algorithm then predicts the phase label for quantum states that were not encountered during training. The classical ML algorithm not only classifies phases accurately, but also constructs an explicit classifying function. Numerical experiments verify that our proposed ML algorithms work well in a variety of scenarios, including Rydberg atom systems, two-dimensional random Heisenberg models, symmetry-protected topological phases, and topologically ordered phases. CONCLUSION We have rigorously established that classical ML algorithms, informed by data collected in physical experiments, can effectively address some quantum many-body problems. These rigorous results boost our hopes that classical ML trained on experimental data can solve practical problems in chemistry and materials science that would be too hard to solve using classical processing alone. Our arguments build on the concept of a succinct classical representation of quantum states derived from randomized Pauli measurements. Although some quantum devices lack the local control needed to perform such measurements, we expect that other classical representations could be exploited by classical ML with similarly powerful results. How can we make use of accessible measurement data to predict properties reliably? Answering such questions will expand the reach of near-term quantum platforms. Classical algorithms for quantum many-body problems. Classical ML algorithms learn from training data, obtained from either classical simulations or quantum experiments. Then, the ML algorithm produces a classical representation for the ground state of a physical system that was not encountered during training. Classical algorithms that do not learn from data may require substantially longer computation time to achieve the same task. 

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4. Hunting for Majoranas

   https://doi.org/10.1126/science.ade0850  

   Yazdani, Ali ; von Oppen, Felix ; Halperin, Bertrand I. ; Yacoby, Amir ( June 2023 , Science)

   BACKGROUND The past decade has witnessed considerable progress toward the creation of new quantum technologies. Substantial advances in present leading qubit technologies, which are based on superconductors, semiconductors, trapped ions, or neutral atoms, will undoubtedly be made in the years ahead. Beyond these present technologies, there exist blueprints for topological qubits, which leverage fundamentally different physics for improved qubit performance. These qubits exploit the fact that quasiparticles of topological quantum states allow quantum information to be encoded and processed in a nonlocal manner, providing inherent protection against decoherence and potentially overcoming a major challenge of the present generation of qubits. Although still far from being experimentally realized, the potential benefits of this approach are evident. The inherent protection against decoherence implies better scalability, promising a considerable reduction in the number of qubits needed for error correction. Transcending possible technological applications, the underlying physics is rife with exciting concepts and challenges, including topological superconductors, non-abelian anyons such as Majorana zero modes (MZMs), and non-abelian quantum statistics.­­ ADVANCES In a wide-ranging and ongoing effort, numerous potential material platforms are being explored that may realize the required topological quantum states. Non-abelian anyons were first predicted as quasiparticles of topological states known as fractional quantum Hall states, which are formed when electrons move in a plane subject to a strong perpendicular magnetic field. The prediction that hybrid materials that combine topological insulators and conventional superconductors can support localized MZMs, the simplest type of non-abelian anyon, brought entirely new material platforms into view. These include, among others, semiconductor-superconductor hybrids, magnetic adatoms on superconducting substrates, and Fe-based superconductors. One-dimensional systems are playing a particularly prominent role, with blueprints for quantum information applications being most developed for hybrid semiconductor-superconductor systems. There have been numerous attempts to observe non-abelian anyons in the laboratory. Several experimental efforts observed signatures that are consistent with some of the theoretical predictions for MZMs. A few extensively studied platforms were subjected to intense scrutiny and in-depth analyses of alternative interpretations, revealing a more complex reality than anticipated, with multiple possible interpretations of the data. Because advances in our understanding of a physical system often rely on discrepancies between experiment and theory, this has already led to an improved understanding of Majorana signatures; however, our ability to detect and manipulate non-abelian anyons such as MZMs remains in its infancy. Future work can build on improved materials in some of the existing platforms but may also exploit new materials such as van der Waals heterostructures, including twisted layers, which promise many new options for engineering topological phases of matter. OUTLOOK Experimentally establishing the existence of non-abelian anyons constitutes an outstandingly worthwhile goal, not only from the point of view of fundamental physics but also because of their potential applications. Future progress will be accelerated if claims of Majorana discoveries are based on experimental tests that go substantially beyond indicators such as zero-bias peaks that, at best, suggest consistency with a Majorana interpretation. It will be equally important that these discoveries build on an excellent understanding of the underlying material systems. Most likely, further material improvements of existing platforms and the exploration of new material platforms will both be important avenues for progress toward obtaining solid evidence for MZMs. Once that has been achieved, we can hope to explore—and harness—the fascinating physics of non-abelian anyons such as the topologically protected ground state manifold and non-abelian statistics. Proposed topological platforms. (Left) Proposed state of electrons in a high magnetic field (even-denominator fractional quantum Hall states) are predicted to host Majorana quasiparticles. (Right) Hybrid structures of superconductors and other materials have also been proposed to host such quasiparticles and can be tailored to create topological quantum bits based on Majoranas. 

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5. (Invited) Oxide Electronics and Recent Progress in Bipolar Applications

   https://doi.org/10.1149/MA2022-01191071mtgabs  

   Lee, Sunghwan ; Lee, Donghun ; Qin, Fei ; Zhang, Yuxuan ; Rothschild, Molly ; Song, Han Wook ; No, Kwangsoo ( July 2022 , ECS Meeting Abstracts)

   The discovery of oxide electronics is of increasing importance today as one of the most promising new technologies and manufacturing processes for a variety of electronic and optoelectronic applications such as next-generation displays, batteries, solar cells, memory devices, and photodetectors[1]. The high potential use seen in oxide electronics is due primarily to their high carrier mobilities and their ability to be fabricated at low temperatures[2]. However, since the majority of oxide semiconductors are n-type oxides, current applications are limited to unipolar devices, eventually developing oxide-based bipolar devices such as p-n diodes and complementary metal-oxide semiconductors. We have contributed to a wide range of oxide semiconductors and their electronics and optoelectronic device applications. Particularly, we have demonstrated n-type oxide-based thin film transistors (TFT), integrating In 2 O 3 -based n-type oxide semiconductors from binary cation materials to ternary cation species including InZnO, InGaZnO (IGZO), and InAlZnO. We have suggested channel/metallization contact strategies to achieve stable and high TFT performance[3, 4], identified vacancy-based native defect doping mechanisms[5], suggested interfacial buffer layers to promote charge injection capability[6], and established the role of third cation species on the carrier generation and carrier transport[7]. More recently, we have reported facile manufacturing of p-type SnOx through reactive magnetron sputtering from a Sn metal target[8]. The fabricated p-SnOx was found to be devoid of metallic phase of Sn from x-ray photoelectron spectroscopy and demonstrated stable performance in a fully oxide-based p-n heterojunction together with n-InGaZnO. The oxide-based p-n junctions exhibited a high rectification ratio greater than 10 3 at ±3 V, a low saturation current of ~2x10 -10 , and a small turn-on voltage of -0.5 V. In this presentation, we review recent achievements and still remaining issues in transition metal oxide semiconductors and their device applications, in particular, bipolar applications including p-n heterostructures and complementary metal-oxide-semiconductor devices as well as single polarity devices such as TFTs and memristors. In addition, the fundamental mechanisms of carrier transport behaviors and doping mechanisms that govern the performance of these oxide-based devices will also be discussed. ACKNOWLEDGMENT This work was supported by the U.S. National Science Foundation (NSF) Award No. ECCS-1931088. S.L. and H.W.S. acknowledge the support from the Improvement of Measurement Standards and Technology for Mechanical Metrology (Grant No. 20011028) by KRISS. K.N. was supported by Basic Science Research Program (NRF-2021R11A1A01051246) through the NRF Korea funded by the Ministry of Education. REFERENCES [1] K. Nomura et al. , Nature, vol. 432, no. 7016, pp. 488-492, Nov 25 2004. [2] D. C. Paine et al. , Thin Solid Films, vol. 516, no. 17, pp. 5894-5898, Jul 1 2008. [3] S. Lee et al. , Journal of Applied Physics, vol. 109, no. 6, p. 063702, Mar 15 2011, Art. no. 063702. [4] S. Lee et al. , Applied Physics Letters, vol. 104, no. 25, p. 252103, 2014. [5] S. Lee et al. , Applied Physics Letters, vol. 102, no. 5, p. 052101, Feb 4 2013, Art. no. 052101. [6] M. Liu et al. , ACS Applied Electronic Materials, vol. 3, no. 6, pp. 2703-2711, 2021/06/22 2021. [7] A. Reed et al. , Journal of Materials Chemistry C, 10.1039/D0TC02655G vol. 8, no. 39, pp. 13798-13810, 2020. [8] D. H. Lee et al. , ACS Applied Materials & Interfaces, vol. 13, no. 46, pp. 55676-55686, 2021/11/24 2021. 

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