Search Results for author: Liang Jiang

Found 23 papers, 0 papers with code

Dynamical phase transition in quantum neural networks with large depth

no code implementations29 Nov 2023 Bingzhi Zhang, Junyu Liu, Xiao-Chuan Wu, Liang Jiang, Quntao Zhuang

Via mapping the Hessian of the training dynamics to a Hamiltonian in the imaginary time, we reveal the nature of the phase transition to be second-order with the exponent $\nu=1$, where scale invariance and closing gap are observed at critical point.

Tight bounds on Pauli channel learning without entanglement

no code implementations23 Sep 2023 Senrui Chen, Changhun Oh, Sisi Zhou, Hsin-Yuan Huang, Liang Jiang

In this work, we consider learning algorithms without entanglement to be those that only utilize separable states, measurements, and operations between the main system of interest and an ancillary system.

Quantum Data Center: Perspectives

no code implementations12 Sep 2023 Junyu Liu, Liang Jiang

A quantum version of data centers might be significant in the quantum era.

Fundamental causal bounds of quantum random access memories

no code implementations25 Jul 2023 Yunfei Wang, Yuri Alexeev, Liang Jiang, Frederic T. Chong, Junyu Liu

Quantum random access memory (QRAM), a fundamental component of many essential quantum algorithms for tasks such as linear algebra, data search, and machine learning, is often proposed to offer $\mathcal{O}(\log N)$ circuit depth for $\mathcal{O}(N)$ data size, given $N$ qubits.

Adjustment with Many Regressors Under Covariate-Adaptive Randomizations

no code implementations17 Apr 2023 Liang Jiang, Liyao Li, Ke Miao, Yichong Zhang

On the other hand, RAs can degrade estimation efficiency due to their estimation errors, which are not asymptotically negligible when the number of regressors is of the same order as the sample size.

Causal Inference regression

Towards provably efficient quantum algorithms for large-scale machine-learning models

no code implementations6 Mar 2023 Junyu Liu, Minzhao Liu, Jin-Peng Liu, Ziyu Ye, Yunfei Wang, Yuri Alexeev, Jens Eisert, Liang Jiang

Large machine learning models are revolutionary technologies of artificial intelligence whose bottlenecks include huge computational expenses, power, and time used both in the pre-training and fine-tuning process.

Covariate Adjustment in Experiments with Matched Pairs

no code implementations9 Feb 2023 Yuehao Bai, Liang Jiang, Joseph P. Romano, Azeem M. Shaikh, Yichong Zhang

This paper studies inference on the average treatment effect in experiments in which treatment status is determined according to "matched pairs" and it is additionally desired to adjust for observed, baseline covariates to gain further precision.

Stochastic noise can be helpful for variational quantum algorithms

no code implementations13 Oct 2022 Junyu Liu, Frederik Wilde, Antonio Anna Mele, Liang Jiang, Jens Eisert

Saddle points constitute a crucial challenge for first-order gradient descent algorithms.

Robust Domain Adaptation for Machine Reading Comprehension

no code implementations23 Sep 2022 Liang Jiang, Zhenyu Huang, Jia Liu, Zujie Wen, Xi Peng

Such a process will inevitably introduce mismatched pairs (i. e., noisy correspondence) due to i) the unavailable QA pairs in target documents, and ii) the domain shift during applying the QA construction model to the target domain.

Domain Adaptation Machine Reading Comprehension

Data centers with quantum random access memory and quantum networks

no code implementations28 Jul 2022 Junyu Liu, Connor T. Hann, Liang Jiang

In this paper, we propose the Quantum Data Center (QDC), an architecture combining Quantum Random Access Memory (QRAM) and quantum networks.

Data Compression

Laziness, Barren Plateau, and Noise in Machine Learning

no code implementations19 Jun 2022 Junyu Liu, Zexi Lin, Liang Jiang

We discuss the difference between laziness and \emph{barren plateau} in quantum machine learning created by quantum physicists in \cite{mcclean2018barren} for the flatness of the loss function landscape during gradient descent.

BIG-bench Machine Learning Quantum Machine Learning

Estimating the randomness of quantum circuit ensembles up to 50 qubits

no code implementations19 May 2022 Minzhao Liu, Junyu Liu, Yuri Alexeev, Liang Jiang

Random quantum circuits have been utilized in the contexts of quantum supremacy demonstrations, variational quantum algorithms for chemistry and machine learning, and blackhole information.

Quantum Machine Learning Tensor Networks

Analytic theory for the dynamics of wide quantum neural networks

no code implementations30 Mar 2022 Junyu Liu, Khadijeh Najafi, Kunal Sharma, Francesco Tacchino, Liang Jiang, Antonio Mezzacapo

We define wide quantum neural networks as parameterized quantum circuits in the limit of a large number of qubits and variational parameters.

Quantum Machine Learning

Improving Estimation Efficiency via Regression-Adjustment in Covariate-Adaptive Randomizations with Imperfect Compliance

no code implementations31 Jan 2022 Liang Jiang, Oliver B. Linton, Haihan Tang, Yichong Zhang

We investigate how to improve efficiency using regression adjustments with covariates in covariate-adaptive randomizations (CARs) with imperfect subject compliance.


Representation Learning via Quantum Neural Tangent Kernels

no code implementations8 Nov 2021 Junyu Liu, Francesco Tacchino, Jennifer R. Glick, Liang Jiang, Antonio Mezzacapo

We analytically solve the dynamics in the frozen limit, or lazy training regime, where variational angles change slowly and a linear perturbation is good enough.

BIG-bench Machine Learning Quantum Machine Learning +1

Regression-Adjusted Estimation of Quantile Treatment Effects under Covariate-Adaptive Randomizations

no code implementations31 May 2021 Liang Jiang, Peter C. B. Phillips, Yubo Tao, Yichong Zhang

We establish the consistency and limit distribution of the regression-adjusted QTE estimator and prove that the use of multiplier bootstrap inference is non-conservative under CARs.


Classical simulation of bosonic linear-optical random circuits beyond linear light cone

no code implementations19 Feb 2021 Changhun Oh, Youngrong Lim, Bill Fefferman, Liang Jiang

Sampling from probability distributions of quantum circuits is a fundamentally and practically important task which can be used to demonstrate quantum supremacy using noisy intermediate-scale quantum devices.

Quantum Physics

Single-shot number-resolved detection of microwave photons with error mitigation

no code implementations9 Oct 2020 Jacob C. Curtis, Connor T. Hann, Salvatore S. Elder, Christopher S. Wang, Luigi Frunzio, Liang Jiang, Robert J. Schoelkopf

This detector functions by measuring a series of generalized parity operators which make up the bits in the binary decomposition of the photon number.

Quantum Physics

Bootstrap Inference for Quantile Treatment Effects in Randomized Experiments with Matched Pairs

no code implementations25 May 2020 Liang Jiang, Xiaobin Liu, Peter C. B. Phillips, Yichong Zhang

This paper examines methods of inference concerning quantile treatment effects (QTEs) in randomized experiments with matched-pairs designs (MPDs).

Long Short-Term Sample Distillation

no code implementations2 Mar 2020 Liang Jiang, Zujie Wen, Zhongping Liang, Yafang Wang, Gerard de Melo, Zhe Li, Liangzhuang Ma, Jiaxing Zhang, Xiaolong Li, Yuan Qi

The long-term teacher draws on snapshots from several epochs ago in order to provide steadfast guidance and to guarantee teacher--student differences, while the short-term one yields more up-to-date cues with the goal of enabling higher-quality updates.

Enhancing Person-Job Fit for Talent Recruitment: An Ability-aware Neural Network Approach

no code implementations21 Dec 2018 Chuan Qin, HengShu Zhu, Tong Xu, Chen Zhu, Liang Jiang, Enhong Chen, Hui Xiong

The wide spread use of online recruitment services has led to information explosion in the job market.

How Images Inspire Poems: Generating Classical Chinese Poetry from Images with Memory Networks

no code implementations8 Mar 2018 Linli Xu, Liang Jiang, Chuan Qin, Zhe Wang, Dongfang Du

Generating poetry from images is much more challenging than generating poetry from text, since images contain very rich visual information which cannot be described completely using several keywords, and a good poem should convey the image accurately.

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