A most important theme in condensed matter physics is how to characterize the fundamental states of quantum matter. The celebrated paradigms include Landau symmetry-breaking and topological quantum phases, whose characterizations have been broadly developed based on the equilibrium theories over the past half century. In comparison, the non-equilibrium quantum dynamics are far less understood. In this talk, I will present pedagogically a generic theory to characterize the far-from-equilibrium quantum dynamics induced in topological quantum systems, by showing a universal correspondence between equilibrium topological phases and non-equilibrium quantum dynamics, which in turn provides the non-equilibrium characterization of equilibrium topological phases. This generic theory is built on the so-called dynamical bulk-surface correspondence, which connects dD bulk topology of an equilibrium phase to topological pattern of quench dynamics emerging in the (d-1)D momentum subspace, dubbed band-inversion surfaces (BISs), rendering a momentum-space counterpart of the well-known bulk-boundary correspondence for equilibrium topological phases in the real space. This dynamical bulk-surface correspondence can be extended to broad topological systems, including the correlated phases, Floquet phases, and topological states realized in synthetic dimensions. The rich nontrivial and universal far-from-equilibrium topological dynamics can be predicted. These results also provide conceptually new schemes to detect equilibrium topological phases by non-equilibrium quantum dynamics, which exhibit clear advantages in the detection and show the capabilities beyond equilibrium schemes. Finally, the future interesting issues will be commented.

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Quantum-enhanced sensing is one of the several potential applications of quantum technologies. I will discuss the quantum information theoretic aspects of estimating single and multiple parameters, with an emphasis on recent advances and applications. The latter will include applications in imaging and 3D magnetometry.

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Quantum error correction (QEC) is necessary for a practical quantum computer because of the inevitable coupling of quantum systems with the uncontrolled environment. A measurement-based QEC requires rapid extraction of error syndromes without disturbing the stored information and fast real-time feedback control for error corrections. Encoding quantum information on photonic states in a microwave cavity for QEC has attracted a lot of interests because of its hardware efficiency [1]. This scheme benefits from the infinite dimensional Hilbert space of a harmonic oscillator for redundant information encoding and only one error syndrome that needs to be monitored. In this talk, I will describe our experimental realizations of repetitive QEC and full control of a binomial bosonic code in a circuit quantum electrodynamics architecture [2], as well as error transparent gates based on the binomial code [3].
Reference:
[1] W. Cai, Y. Ma, W. Wang, C.-L. Zou and L. Sun, arXiv:2010.08699 (2020).
[2] L. Hu, Y. Ma, W. Cai, X. Mu, Y. Xu, W. Wang, Y. Wu, H. Wang, Y. P. Song, C.-L. Zou, S. M. Girvin, L-M. Duan and L. Sun, Nature Physics 15, 503 (2019).
[3] Y. Ma, Y. Xu, X. Mu, W. Cai, L. Hu, W. Wang, X. Pan, H. Wang, Y. P. Song, C.-L. Zou, L. Sun, Nature Physics 16, 827 (2020).

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Recently time crystals have attracted major interest from the physics community as they are exotic phases of matter characterized by a robust dynamic response that breaks discrete time-translation symmetry [1–4]. The development of new insights into how such new phase of matter arise as well as determining what applications they may be useful for is much in demand. The use of graphs to analyze large sets of data has proven to be extremely useful to observe dynamical behaviors giving rise to the subfield of complex networks. In this talk I will discuss how to bridge these two areas by establishing a novel strategy to identify, characterize and analyze time crystals in terms of graphs [5]. Our approach completely changes the way we look at time-periodic systems and related phenomena [6–9]. It is under this perspective that we envisage the use of such unique physical systems as platforms where to simulate complex quantum networks. The simulation of such networks has wide applicability in diverse communities. Importantly, our proposal could be implemented in current noisy intermediate-scale quantum (NISQ) devices, ranging from trapped ions to superconducting qubit chips [10].
Reference:
[1] F. Wilczek, Phys. Rev. Lett. 109, 160401 (2012).
[2] V. Khemani, A. Lazarides, R. Moessner, S. L. Sondhi, Phys. Rev. Lett. 116, 250401 (2016).
[3] N. Y. Yao, A. C. Potter, I.-D. Potirniche, A. Vishwanath, Phys. Rev. Lett. 118, 030401 (2017).
[4] K. Sacha, J. Zakrzewski, Rep. Prog. Phys. 81, 016401 (2018).
[5] M. P. Estarellas, T. Osada, V. M. Bastidas, B. Renoust, K. Sanaka, W. J. Munro, Kae Nemoto, Science Advances 6, eaay8892 (2020).
[6] A. Lazarides, A. Das, and R. Moessner, Phys. Rev. Lett. 115, 030402 (2015).
[7] S. Restrepo, J. Cerrillo, V. M. Bastidas, D. G. Angelakis, and T. Brandes, Phys. Rev. Lett. 117, 250401 (2016).
[8] A. Eckardt, Rev. Mod. Phys. 89, 011004 (2017).
[9] V. M. Bastidas, B. Renoust, Kae Nemoto, W. J. Munro, Phys. Rev. B 98, 224307 (2018).
[10] P. Roushan, C. Neill, J. Tangpanitanon, V. M. Bastidas, A. Megrant, R. Barends, Y. Chen, Z. Chen, B. Chiaro, A. Dunsworth, A. Fowler, B. Foxen, M. Giustina, E. Jeffrey, J. Kelly, E. Lucero, J. Mutus,1 M. Neeley, C. Quintana, D. Sank, A. Vainsencher, J. Wenner, T. White, H. Neven, D. G. Angelakis, J. Martinis, Science 358, 1175 (2017).

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Quantum artificial intelligence (Quantum AI) is an emergent interdisciplinary field that explores the interplay between artificial intelligence and quantum physics. On the one hand, judiciously designed quantum algorithms may exhibit exponential advantages in solving certain AI problems; on the other hand, ideas and techniques from AI can also be exploited to tackle challenging problems in the quantum domain. In this talk, I will first make a brief introduction to this field and review some recent progresses. I will talk about several concrete examples to illustrate how AI and quantum physics can promote studies in both fields. At the end of the talk, I will pose ten fundamental challenges facing quantum AI that, if overcome, would give a significant boost to this fledgling field full of uncertainties and opportunities.

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Measurement and estimation of the magnetic field is essential for many practical applications, where the main quest is to find out the highest achievable precision with given resources and design schemes to attain it. In this talk I will present the ultimate precision that can be achieved for quantum magnetometry with the optimal entangled probe states. I will talk about how to further improve the precision with optimal quantum controls.

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The quantum perceptron is a fundamental building block for quantum machine learning. This is a multidisciplinary field that incorporates abilities of quantum computing, such as state superposition and entanglement, to classical machine learning schemes. Motivated by the techniques of shortcuts to adiabaticity, we propose a speed-up quantum perceptron where a control field on the perceptron is inversely engineered leading to a rapid nonlinear response with a sigmoid activation function. This results in faster overall perceptron performance compared to quasi-adiabatic protocols, as well as in enhanced robustness against imperfections in the controls.

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Classical feedforward neural nets are tunable logical gates. Training the neural nets means tuning these gates. These gates can be generalised to parametrised quantum unitaries. Training the network classically amounts to tuning these parameters. This can be used for quantum generalisations of classical neural nets like auto-encoders, as well as for discovering quantum algorithms for classical tasks. One can also train the network in a superposition of parameters, in a Grover-like training procedure which has a speed-up relative to classical brute force global optimisation.

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Combinatorial problems are ubiquitous for a large variety of scientific disciplines, encompassing portfolio optimization in finance, molecular dynamics in biology and drug design in chemistry. However, solving such problems is extremely challenging. Indeed, even elementary instances have been shown to be NP-complete, such that the fastest supercomputers today get pushed beyond their limits when addressing real-life problems.
Lately, a fundamentally different approach towards solving combinatorial problems has arisen attention. It has been shown that all of Karp’s 21 NP-complete computational problems can be formulated as finding round states of spin-glass systems. This shifted the focus shifted towards special-purpose machines – made to address solely the specific problems of interest – rather than using conventional classical universal computers. The fundamental idea is to let a system evolve naturally – in accordance to the laws of physics – instead of constructing algorithms to search for the optimal solution. Spin-glass systems are microscopic, and, hence, it deems crucial to utilize quantum systems for the efficient system evolution.
This talk provides an overview of one of the promising approaches to find the ground states of quantum spin-glass models: quantum annealing. Basic concepts, comparisons to classical approaches, as well as limitations of quantum annealing and future directions of research will be discussed.

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Certifying unknown quantum entanglement experimentally is a fundamental problem in quantum information. However, due to the imperfection of quantum operations, this is a very challenging task, not to mention quantifying unknown (multipartite) entanglement experimentally. Fortunately, it turns out these two tasks can be fulfilled reliably and efficiently using device-independent approaches, where the certification and quantification are achieved based on the observed nonlocality only and one does not have to care about the precision and internal workings of involved quantum devices. In this talk, we will introduce several recent progresses on this problem.

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I will briefly introduce quantum metrology and then focus on quantum metrology for squeezing. Usually the theory uses the resolution gains obtainable thanks to the entanglement among N probes. Typically, a quadratic gain in resolution is achievable, going from the 1/\sqrt(N) of the central limit theorem to the 1/N of the Heisenberg bound. We focus on quantum squeezing and provide a unified framework for metrology with squeezing, showing that, similarly, one can generally attain a quadratic gain when comparing the resolution achievable by a squeezed probe to the best N-probe classical strategy achievable with the same energy. Namely, here we give a quantification of the Heisenberg squeezing bound for arbitrary estimation strategies that employ squeezing. Our theory recovers known results (e.g. in quantum optics and spin squeezing), but it uses the general theory of squeezing and holds for arbitrary quantum systems. The talk is based on the paper:
L. Maccone, A. Riccardi, Squeezing Metrology: a unified framework, Quantum 4, 292 (2020).

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Quantum-State Transfer (QST) is an important requisite in many Quantum Information Processing (QIP) protocols [1]. For short-distance communication, interacting spin-1/2 chains as a data bus fulfilling faithful QST between a sender and a receiver qubit have been widely investigated and different protocols have been proposed. A step forward would be to allow for the use of a single data bus by many senders and receivers for QIP protocols. In this talk a protocol for high-fidelity QST from one sender to a selected receiver out of many possible ones is proposed by exploiting a weak-coupling mechanics inducing resonant tunnelling of the excitation between the sender and the selected receiver location. The same protocol is also explored in order to achieve QST of more than a single qubit [2], generation of multiple pairs of Bell states [3], and transfer of multipartite entanglement [4].
Reference:
[1] S. Bose (2007) Quantum communication through spin chain dynamics: an introductory overview, Contemporary Physics, 48:1, 13-30, http://dx.doi.org/10.1080/00107510701342313
[2] W.J. Chetcuti, C. Sanavio, S. Lorenzo and T.J.G. Apollaro, Perturbative many-body transfer, 2020 New J. Phys. 22 033030 https://doi.org/10.1088/1367-2630/ab7a33
[3] T.J.G. Apollaro, G.M.A. Almeida, S. Lorenzo, A. Ferraro, and S. Paganelli, Spin chains for two-qubit teleportation, Phys. Rev. A 100, 052308 (2019) https://doi.org/10.1103/PhysRevA.100.052308
[4] T.J.G. Apollaro, C. Sanavio, W.J. Chetcuti, S. Lorenzo, Multipartite entanglement transfer in spin chains, Physics Letters A 384, 126306 2020 https://doi.org/10.1016/j.physleta.2020.126306ony

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Isolating a quantum system from ambient environment is practically impossible. Interaction with the environment makes obtaining an exact master equation for the dynamics of an open system hard. Most of the existing techniques for deriving a dynamical master equation for an open system are approximative and based on weak-coupling expansions. I give a brief review of such techniques and in particular how the celebrated Lindblad equation is obtained under the weak-coupling, Markovian assumption. I then introduce a novel correlation picture which enables us to derive an exact Lindblad-like master equation, with system-environment correlations included. I also introduce a weak-correlation expansion that allows to perform systematic perturbative approximations. This expansion provides approximate master equations which can feature advantages over existing weak-coupling techniques in the sense that they are more accurate or as accurate as weak-coupling master equations.

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Recent experimental advances have made record-breaking cooling possible in ultracold gases. And yet, state-of-the-art thermometric techniques fall short of the challenge of measuring their temperatures with high accuracy in the subnanokelvin range. The limitations are not just technical—there are stringent fundamental bounds on the precision of low-temperature thermometry. To approach them as closely as possible, it becomes necessary to revise every step in the estimation process: from the design of the probe, to the choice of temperature estimator, and the physical modelling of probe and sample, which dictates how the measured data must be processed into a temperature estimate. The emerging field of quantum thermometry aims at answering these questions, by blending quantum parameter estimation with the theory of open quantum systems. In this talk I will give an overview of the field and focus on applications to impurity thermometry in ultracold atomic gases.

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The observation of many-body localization is a paradigmatic example of the amount of time an idea takes to get mature enough, and the numerical and experimental methods to sufficiently develop, in order to settle its existence. After the original study of Philip Anderson in 1958, demonstrating localization of non-interacting quantum particles in disordered settings, a natural question is on the resulting effects of the inter-particle interactions on this phenomenon. Only after 50 years, substantial theoretical progress was made in solving this puzzle and, in 2016 the first experimental observation of this phenomenon was realized. The advent of platforms involving ultracold atoms trapped by optical lattices allowed the inspection of an inherently dynamical quantum phase transition, that goes beyond the standard ground-state classification of the quantum matter, and its associated low-lying excitations. Instead, it is described by a high-energy phase transition, inherently manifested via the unitary dynamics of an isolated quantum system, wherein by tuning the strength of disorder, one is able to halt the onset of ergodic behavior and thermalization. In this talk, after introducing the general conditions where it occurs, and review the experiments tackling it so far, I will show numerical and experimental results using quantum circuits of superconducting qubits that shed light on yet another highly debated aspect: the possible existence of many-body mobility edges.
Reference: arXiv preprint, arXiv:1912.02818, “Observation of energy resolved many-body localization”, Qiujiang Guo, Chen Cheng, Zheng-Hang Sun, Zixuan Song, Hekang Li, Zhen Wang, Wenhui Ren, Hang Dong, Dongning Zheng, Yu-Ran Zhang, Rubem Mondaini, Heng Fan, H. Wang. — Nature Physics (in press)

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The rates of chemical reactions are predicted to be increased rapidly at very low temperatures where the quantum mechanical treatments start dominating the classical processes. In such an interaction regime, one can get precise control over the chemical processes by finding a full understanding from the dynamics of involved parameters acting on the reactions, including but not limited to initial quantum states of the reactants and applied external fields. The system under investigation can be even more complicated if reactants are molecules with microscopic degrees of freedom. In this work, we show how such a system can be simulated via a hybrid atom-ion system in which the dynamics and evolution of a single molecular ion inside a bath of ultracold neutral Rb atoms are investigated at very low collision energies of a few mK.

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Solving linear systems of equations is essential for many problems in science and technology, including problems in machine learning. Existing quantum algorithms have demonstrated the potential for large speedups, but the required quantum resources are not immediately available on near-term quantum devices. In this work, we study near-term quantum algorithms for linear systems of equations of the form Ax = b. We investigate the use of variational algorithms and analyze their optimization landscapes. There exist types of linear systems for which variational algorithms designed to avoid barren plateaus, such as properly initialized imaginary time evolution and adiabatic-inspired optimization, suffer from a different plateau problem. To circumvent this issue, we design near-term algorithms based on a core idea: the classical combination of variational quantum states (CQS). We exhibit several provable guarantees for these algorithms, supported by the representation of the linear system on a so-called Ansatz tree. The CQS approach and the Ansatz tree also admit the systematic application of heuristic approaches, including a gradient-based search. We have conducted numerical experiments solving linear systems as large as 2 300 × 2 300 by considering cases where we can simulate the quantum algorithm efficiently on a classical computer. These experiments demonstrate the algorithms’ ability to scale to system sizes within reach in near-term quantum devices of about 100-300 qubits.

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In recent years, propelled by the progress in the field of quantum simulations with ultracold atoms, there has been an increasing interest of the condensed matter community in what is generally called quasicrystal lattices, long-range ordered but non-periodic structures. Besides retaining intrinsic relevant questions that range from the stability of tiled structures at zero temperature to their relation to fractal lattices, quasicrystals have also shown to support quantum phases of matter such as superconductors and Bose-Einstein condensates. Nonetheless, in spite of important works that address the emergence of quasicrystalline order in classical systems, a deeper understanding of the role of quantum fluctuations in these structures still lacks. Here we present our proposal to realize quasi-crystalline states in ultra cold setups with non-local interactions.

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In the classical world, there is a powerful interplay between security and machine learning deployed in a network, like on the modern internet. What happens when the learning algorithms and the network itself can be quantum? What are the new problems that can arise and can quantum resources offer advantages to their classical counterparts?
We explore these questions in a new area called adversarial quantum learning, that combines the area of adversarial machine learning, which investigates security questions in machine learning, and quantum information.
For the first part of the talk, I’ll introduce adversarial machine learning and some exciting potential prospects for contributions from quantum information and computation. For the second part of the talk, I’ll present two recent works on adversarial quantum learning. Here we are able to quantify the vulnerability of quantum algorithms for classification against adversaries and learn how to leverage quantum noise to improve its robustness against attacks.

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HiQ is Huawei’s high-performance cloud platform for large-scale quantum circuit simulations based on HUAWEI CLOUD’s powerful computing and storage infrastructure. It plays an important role in quantum hardware design and verification, quantum algorithm research and quantum software design. The world largest VQE simulation has been carried out on HiQ (in terms of number of orbitals). In this talk, I’ll introduce the basic functionalities of HiQ and its application scenarios. I’ll also briefly introduce our recent research on quantum chemistry VQE and quantum optimization.

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Floquet engineering is a powerful tool to customize quantum states and Hamiltonians via time-periodic fields. On the other hand, the optical lattice clock (OLC) is among the most accurate precision measurement devices. Considering the long life time of the clock state, the OLC becomes an ideal candidate for Floquet engineering the internal (atomic) energy distribution. In this talk, I will introduce the theoretical and experimental research on this project. Our work aims to design of a new generation of devices for sensing, metrology and simulating exotic spin-orbit Hamiltonian.

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