ORCID Profile
0000-0002-5459-4313
Current Organisations
Tsinghua University
,
National University of Singapore
,
Nanyang Technological University
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Publisher: American Physical Society (APS)
Date: 11-09-2013
Publisher: American Physical Society (APS)
Date: 13-02-2019
Publisher: Springer Science and Business Media LLC
Date: 24-02-2022
DOI: 10.1038/S41467-022-28702-0
Abstract: Large-scale, highly integrated and low-power-consuming hardware is becoming progressively more important for realizing optical neural networks (ONNs) capable of advanced optical computing. Traditional experimental implementations need N 2 units such as Mach-Zehnder interferometers (MZIs) for an input dimension N to realize typical computing operations (convolutions and matrix multiplication), resulting in limited scalability and consuming excessive power. Here, we propose the integrated diffractive optical network for implementing parallel Fourier transforms, convolution operations and application-specific optical computing using two ultracompact diffractive cells (Fourier transform operation) and only N MZIs. The footprint and energy consumption scales linearly with the input data dimension, instead of the quadratic scaling in the traditional ONN framework. A ~10-fold reduction in both footprint and energy consumption, as well as equal high accuracy with previous MZI-based ONNs was experimentally achieved for computations performed on the MNIST and Fashion-MNIST datasets. The integrated diffractive optical network (IDNN) chip demonstrates a promising avenue towards scalable and low-power-consumption optical computational chips for optical-artificial-intelligence.
Publisher: American Physical Society (APS)
Date: 06-06-2013
Publisher: Springer Science and Business Media LLC
Date: 09-2014
Publisher: American Physical Society (APS)
Date: 06-05-2022
Publisher: The Royal Society
Date: 03-10-2012
Abstract: Physical systems are often simulated using a stochastic computation where different final states result from identical initial states. Here, we derive the minimum energy cost of simulating a data sequence of a general physical system by stochastic computation. We show that the cost is proportional to the difference between two information-theoretic measures of complexity of the data—the statistical complexity and the predictive information . We derive the difference as the amount of information erased during the computation. Finally, we illustrate the physics of information by implementing the stochastic computation as a Gedanken experiment with a Szilard-type engine. The results create a new link between thermodynamics, information theory and complexity.
Publisher: Springer Science and Business Media LLC
Date: 12-01-2016
Publisher: American Physical Society (APS)
Date: 21-09-2007
Publisher: IOP Publishing
Date: 17-09-2021
Abstract: In the setting of regression, the standard formulation of gradient boosting generates a sequence of improvements to a constant model. In this paper, we reformulate gradient boosting such that it is able to generate a sequence of improvements to a nonconstant model, which may contain prior knowledge or physical insight about the data generating process. Moreover, we introduce a simple variant of multi-target stacking that extends our approach to the setting of multi-target regression. An experiment on a real-world superconducting quantum device calibration dataset demonstrates that our approach outperforms the state-of-the-art calibration model even though it only receives a paucity of training ex les. Further, it significantly outperforms a well-known gradient boosting algorithm, known as LightGBM, as well as an entirely data-driven reimplementation of the calibration model, which suggests the viability of our approach.
Publisher: American Physical Society (APS)
Date: 24-04-2017
Publisher: OSA
Date: 2011
Publisher: Elsevier BV
Date: 11-2014
Publisher: IOP Publishing
Date: 05-01-2018
Publisher: American Physical Society (APS)
Date: 23-06-2020
Publisher: Springer Science and Business Media LLC
Date: 19-01-2021
DOI: 10.1038/S41467-020-20719-7
Abstract: Complex-valued neural networks have many advantages over their real-valued counterparts. Conventional digital electronic computing platforms are incapable of executing truly complex-valued representations and operations. In contrast, optical computing platforms that encode information in both phase and magnitude can execute complex arithmetic by optical interference, offering significantly enhanced computational speed and energy efficiency. However, to date, most demonstrations of optical neural networks still only utilize conventional real-valued frameworks that are designed for digital computers, forfeiting many of the advantages of optical computing such as efficient complex-valued operations. In this article, we highlight an optical neural chip (ONC) that implements truly complex-valued neural networks. We benchmark the performance of our complex-valued ONC in four settings: simple Boolean tasks, species classification of an Iris dataset, classifying nonlinear datasets (Circle and Spiral), and handwriting recognition. Strong learning capabilities (i.e., high accuracy, fast convergence and the capability to construct nonlinear decision boundaries) are achieved by our complex-valued ONC compared to its real-valued counterpart.
Publisher: Springer Science and Business Media LLC
Date: 05-2012
DOI: 10.1038/NCOMMS1809
Abstract: The observation that concepts from quantum information has generated many alternative indicators of quantum phase transitions hints that quantum phase transitions possess operational significance with respect to the processing of quantum information. Yet, studies on whether such transitions lead to quantum phases that differ in their capacity to process information remain limited. Here we show that there exist quantum phase transitions that cause a distinct qualitative change in our ability to simulate certain quantum systems under perturbation of an external field by local operations and classical communication. In particular, we show that in certain quantum phases of the XY model, adiabatic perturbations of the external magnetic field can be simulated by local spin operations, whereas the resulting effect within other phases results in coherent non-local interactions. We discuss the potential implications to adiabatic quantum computation, where a computational advantage exists only when adiabatic perturbation results in coherent multi-body interactions.
Publisher: American Physical Society (APS)
Date: 30-04-2021
Publisher: IEEE
Date: 08-2011
Publisher: AIP Publishing LLC
Date: 2014
DOI: 10.1063/1.4903110
Publisher: Oxford University Press (OUP)
Date: 07-2019
DOI: 10.1093/NSR/NWZ081
Publisher: American Physical Society (APS)
Date: 23-04-2014
Publisher: Springer Science and Business Media LLC
Date: 09-04-2019
DOI: 10.1038/S41467-019-08951-2
Abstract: Simulations of stochastic processes play an important role in the quantitative sciences, enabling the characterisation of complex systems. Recent work has established a quantum advantage in stochastic simulation, leading to quantum devices that execute a simulation using less memory than possible by classical means. To realise this advantage it is essential that the memory register remains coherent, and coherently interacts with the processor, allowing the simulator to operate over many time steps. Here we report a multi-time-step experimental simulation of a stochastic process using less memory than the classical limit. A key feature of the photonic quantum information processor is that it creates a quantum superposition of all possible future trajectories that the system can evolve into. This superposition allows us to introduce, and demonstrate, the idea of comparing statistical futures of two classical processes via quantum interference. We demonstrate interference of two 16-dimensional quantum states, representing statistical futures of our process, with a visibility of 0.96 ± 0.02.
Publisher: American Physical Society (APS)
Date: 03-05-2016
Publisher: American Physical Society (APS)
Date: 17-10-2019
Publisher: American Physical Society (APS)
Date: 12-02-2016
Publisher: Springer Science and Business Media LLC
Date: 16-07-2020
DOI: 10.1038/S41534-020-00292-Z
Abstract: Experimentally achieving the precision that standard quantum metrology schemes promise is always challenging. Recently, additional controls were applied to design feasible quantum metrology schemes. However, these approaches generally does not consider ease of implementation, raising technological barriers impeding its realization. In this paper, we circumvent this problem by applying closed-loop learning control to propose a practical controlled sequential scheme for quantum metrology. Purity loss of the probe state, which relates to quantum Fisher information, is measured efficiently as the fitness to guide the learning loop. We confirm its feasibility and certain superiorities over standard quantum metrology schemes by numerical analysis and proof-of-principle experiments in a nuclear magnetic resonance system.
Publisher: IOP Publishing
Date: 28-01-2019
Publisher: American Physical Society (APS)
Date: 22-04-2016
Publisher: American Vacuum Society
Date: 09-2023
DOI: 10.1116/5.0139099
Publisher: IOP Publishing
Date: 08-2022
Abstract: Adaptive systems—such as a biological organism gaining survival advantage, an autonomous robot executing a functional task, or a motor protein transporting intracellular nutrients—must somehow embody relevant regularities and stochasticity in their environments to take full advantage of thermodynamic resources. Analogously, but in a purely computational realm, machine learning algorithms estimate models to capture predictable structure and identify irrelevant noise in training data. This happens through optimization of performance metrics, such as model likelihood. If such learning is physically implemented, is there a sense in which computational models estimated through machine learning are physically preferred? We introduce the thermodynamic principle that work production is the most relevant performance measure for an adaptive physical agent and compare the results to the maximum-likelihood principle that guides machine learning. Within the class of physical agents that most efficiently harvest energy from their environment, we demonstrate that an efficient agent’s model explicitly determines its architecture and how much useful work it harvests from the environment. We then show that selecting the maximum-work agent for given environmental data corresponds to finding the maximum-likelihood model. This establishes an equivalence between nonequilibrium thermodynamics and dynamic learning. In this way, work maximization emerges as an organizing principle that underlies learning in adaptive thermodynamic systems.
Publisher: IEEE
Date: 05-2013
Publisher: American Physical Society (APS)
Date: 03-12-2018
Publisher: American Physical Society (APS)
Date: 12-11-2018
Publisher: Springer Science and Business Media LLC
Date: 24-11-2015
DOI: 10.1038/NPJQI.2015.7
Abstract: In general relativity, closed timelike curves can break causality with remarkable and unsettling consequences. At the classical level, they induce causal paradoxes disturbing enough to motivate conjectures that explicitly prevent their existence. At the quantum level such problems can be resolved through the Deutschian formalism, however this induces radical benefits—from cloning unknown quantum states to solving problems intractable to quantum computers. Instinctively, one expects these benefits to vanish if causality is respected. Here we show that in harnessing entanglement, we can efficiently solve NP-complete problems and clone arbitrary quantum states—even when all time-travelling systems are completely isolated from the past. Thus, the many defining benefits of Deutschian closed timelike curves can still be harnessed, even when causality is preserved. Our results unveil a subtle interplay between entanglement and general relativity, and significantly improve the potential of probing the radical effects that may exist at the interface between relativity and quantum theory.
Publisher: American Physical Society (APS)
Date: 02-08-2018
Publisher: Springer Science and Business Media LLC
Date: 11-01-2021
DOI: 10.1038/S41467-020-20330-W
Abstract: Realizing a long coherence time quantum memory is a major challenge of current quantum technology. Until now, the longest coherence-time of a single qubit was reported as 660 s in a single 171 Yb + ion-qubit through the technical developments of sympathetic cooling and dynamical decoupling pulses, which addressed heating-induced detection inefficiency and magnetic field fluctuations. However, it was not clear what prohibited further enhancement. Here, we identify and suppress the limiting factors, which are the remaining magnetic-field fluctuations, frequency instability and leakage of the microwave reference-oscillator. Then, we observe the coherence time of around 5500 s for the 171 Yb + ion-qubit, which is the time constant of the exponential decay fit from the measurements up to 960 s. We also systematically study the decoherence process of the quantum memory by using quantum process tomography and analyze the results by applying recently developed resource theories of quantum memory and coherence. Our experimental demonstration will accelerate practical applications of quantum memories for various quantum information processing, especially in the noisy-intermediate-scale quantum regime.
Publisher: Springer Science and Business Media LLC
Date: 17-02-2020
Publisher: American Physical Society (APS)
Date: 22-12-2020
Publisher: Verein zur Forderung des Open Access Publizierens in den Quantenwissenschaften
Date: 11-08-2017
Abstract: The minimal memory required to model a given stochastic process - known as the statistical complexity - is a widely adopted quantifier of structure in complexity science. Here, we ask if quantum mechanics can fundamentally change the qualitative behaviour of this measure. We study this question in the context of the classical Ising spin chain. In this system, the statistical complexity is known to grow monotonically with temperature. We evaluate the spin chain's quantum mechanical statistical complexity by explicitly constructing its provably simplest quantum model, and demonstrate that this measure exhibits drastically different behaviour: it rises to a maximum at some finite temperature then tends back towards zero for higher temperatures. This demonstrates how complexity, as captured by the amount of memory required to model a process, can exhibit radically different behaviour when quantum processing is allowed.
Publisher: American Physical Society (APS)
Date: 08-02-2022
Publisher: American Physical Society (APS)
Date: 13-07-2012
Publisher: IOP Publishing
Date: 18-04-2016
Publisher: American Physical Society (APS)
Date: 23-11-2020
Publisher: American Physical Society (APS)
Date: 19-03-2020
Publisher: American Physical Society (APS)
Date: 22-11-2021
Publisher: Springer Science and Business Media LLC
Date: 16-10-2019
DOI: 10.1038/S41467-019-12643-2
Abstract: Modern computation relies crucially on modular architectures, breaking a complex algorithm into self-contained subroutines. A client can then call upon a remote server to implement parts of the computation independently via an application programming interface (API). Present APIs relay only classical information. Here we implement a quantum API that enables a client to estimate the absolute value of the trace of a server-provided unitary operation $$U$$ U . We demonstrate that the algorithm functions correctly irrespective of what unitary $$U$$ U the server implements or how the server specifically realizes $$U$$ U . Our experiment involves pioneering techniques to coherently swap qubits encoded within the motional states of a trapped $${}^{171}{{\\rm{Yb}}}^{+}\\,$$ 171 Yb + ion, controlled on its hyperfine state. This constitutes the first demonstration of modular computation in the quantum regime, providing a step towards scalable, parallelization of quantum computation.
Publisher: IOP Publishing
Date: 12-10-2017
Publisher: American Physical Society (APS)
Date: 13-11-2014
Publisher: American Physical Society (APS)
Date: 07-11-2016
Publisher: Springer Science and Business Media LLC
Date: 05-08-2012
DOI: 10.1038/NPHYS2376
Publisher: American Physical Society (APS)
Date: 22-10-2003
Publisher: Optica Publishing Group
Date: 06-07-2020
Abstract: In quantum illumination (QI), a signal beam initially entangled with an idler beam held at the receiver interrogates a target region bathed in thermal background light. The returned beam is measured jointly with the idler in order to determine whether a weakly reflecting target is present. Using tools from quantum information theory, we derive lower bounds on the average error probability of detecting both specular and fading targets and on the mean squared error of estimating the reflectance of a detected target, which are obeyed by any QI transmitter satisfying a signal energy constraint. For bright thermal backgrounds, we show that the QI system using multiple copies of low-brightness two-mode squeezed vacuum states is nearly optimal. More generally, our results place limits on the best possible performance achievable using QI systems at all wavelengths, and at all signal and background noise levels.
Publisher: American Physical Society (APS)
Date: 23-11-2016
Publisher: American Physical Society (APS)
Date: 12-06-2023
Publisher: American Physical Society (APS)
Date: 18-07-2018
Publisher: American Physical Society (APS)
Date: 09-05-2019
Publisher: American Physical Society (APS)
Date: 11-06-2018
Publisher: Springer Science and Business Media LLC
Date: 03-2018
DOI: 10.1038/S41534-018-0064-4
Abstract: Continuous-time stochastic processes pervade everyday experience, and the simulation of models of these processes is of great utility. Classical models of systems operating in continuous-time must typically track an unbounded amount of information about past behaviour, even for relatively simple models, enforcing limits on precision due to the finite memory of the machine. However, quantum machines can require less information about the past than even their optimal classical counterparts to simulate the future of discrete-time processes, and we demonstrate that this advantage extends to the continuous-time regime. Moreover, we show that this reduction in the memory requirement can be unboundedly large, allowing for arbitrary precision even with a finite quantum memory. We provide a systematic method for finding superior quantum constructions, and a protocol for analogue simulation of continuous-time renewal processes with a quantum machine.
Publisher: American Physical Society (APS)
Date: 23-02-2017
Publisher: Springer Science and Business Media LLC
Date: 06-05-2023
DOI: 10.1038/S41467-023-37555-0
Abstract: Complex systems are embedded in our everyday experience. Stochastic modelling enables us to understand and predict the behaviour of such systems, cementing its utility across the quantitative sciences. Accurate models of highly non-Markovian processes – where the future behaviour depends on events that happened far in the past – must track copious amounts of information about past observations, requiring high-dimensional memories. Quantum technologies can ameliorate this cost, allowing models of the same processes with lower memory dimension than corresponding classical models. Here we implement such memory-efficient quantum models for a family of non-Markovian processes using a photonic setup. We show that with a single qubit of memory our implemented quantum models can attain higher precision than possible with any classical model of the same memory dimension. This heralds a key step towards applying quantum technologies in complex systems modelling.
Publisher: American Association for the Advancement of Science (AAAS)
Date: 03-02-2017
Abstract: We simulate a random process using a photonic quantum processor with less memory than an ordinary computer would require.
Publisher: American Physical Society (APS)
Date: 24-01-2018
Publisher: American Physical Society (APS)
Date: 18-06-2019
Publisher: American Physical Society (APS)
Date: 11-01-2022
Publisher: OSA
Date: 2014
Publisher: Springer Science and Business Media LLC
Date: 10-02-2017
DOI: 10.1038/S41534-016-0001-3
Abstract: All natural things process and transform information. They receive environmental information as input, and transform it into appropriate output responses. Much of science is dedicated to building models of such systems—algorithmic abstractions of their input–output behavior that allow us to simulate how such systems can behave in the future, conditioned on what has transpired in the past. Here, we show that classical models cannot avoid inefficiency—storing past information that is unnecessary for correct future simulation. We construct quantum models that mitigate this waste, whenever it is physically possible to do so. This suggests that the complexity of general input–output processes depends fundamentally on what sort of information theory we use to describe them.
Publisher: American Physical Society (APS)
Date: 31-01-2020
Publisher: Elsevier BV
Date: 06-2018
Publisher: Springer Science and Business Media LLC
Date: 27-03-2012
DOI: 10.1038/NCOMMS1761
Abstract: Mathematical models are an essential component of quantitative science. They generate predictions about the future, based on information available in the present. In the spirit of simpler is better should two models make identical predictions, the one that requires less input is preferred. Yet, for almost all stochastic processes, even the provably optimal classical models waste information. The amount of input information they demand exceeds the amount of predictive information they output. Here we show how to systematically construct quantum models that break this classical bound, and that the system of minimal entropy that simulates such processes must necessarily feature quantum dynamics. This indicates that many observed phenomena could be significantly simpler than classically possible should quantum effects be involved.
Publisher: American Physical Society (APS)
Date: 20-04-2016
Publisher: IEEE
Date: 06-2013
Publisher: American Physical Society (APS)
Date: 17-06-2009
Publisher: American Physical Society (APS)
Date: 26-04-2006
Publisher: World Scientific Pub Co Pte Lt
Date: 26-11-2012
DOI: 10.1142/S0217979213450276
Abstract: We examine bipartite and multipartite correlations within the construct of unitary orbits. We show that the set of product states is a very small subset of set of all possible states, while all unitary orbits contain classically correlated states. Using this we give meaning to degeneration of quantum correlations due to a unitary interactions, which we call coherent correlations. The remaining classical correlations are called incoherent correlations and quantified in terms of the distance of the joint probability distributions to its marginals. Finally, we look at how entanglement looks in this picture for the two-qubit case.
Publisher: American Physical Society (APS)
Date: 27-12-2018
Publisher: American Physical Society (APS)
Date: 26-11-2019
Publisher: IOP Publishing
Date: 10-2019
Abstract: We study the task of entanglement distillation in the one-shot setting under different classes of quantum operations which extend the set of local operations and classical communication (LOCC). Establishing a general formalism which allows for a straightforward comparison of their exact achievable performance, we relate the fidelity of distillation under these classes of operations with a family of entanglement monotones and the rates of distillation with a class of smoothed entropic quantities based on the hypothesis testing relative entropy. We then characterise exactly the one-shot distillable entanglement of several classes of quantum states and reveal many simplifications in their manipulation. We show in particular that the ε -error one-shot distillable entanglement of any pure state is the same under all sets of operations ranging from one-way LOCC to separability-preserving operations or operations preserving the set of states with positive partial transpose, and can be computed exactly as a quadratically constrained linear program. We establish similar operational equivalences in the distillation of isotropic and maximally correlated states, reducing the computation of the relevant quantities to linear or semidefinite programs. We also show that all considered sets of operations achieve the same performance in environment-assisted entanglement distillation from any state.
Start Date: 2016
End Date: 2018
Funder: Australian Research Council
View Funded ActivityStart Date: 2015
End Date: 2017
Funder: John Templeton Foundation
View Funded Activity