arXiv subjects

Yiqing Zhou

Publications and source records attributed to Yiqing Zhou.

29 records · Page 2Linked to original sources

arXiv2023

Quantum Phases of Transition Metal Dichalcogenide Moiré Systems

Moiré systems provide a rich platform for studies of strong correlation physics. Recent experiments on hetero-bilayer transition metal dichalcogenide (TMD) Moiré systems are exciting in that they manifest a relatively simple model system of an extended Hubbard model on a triangular lattice. Inspired by the prospect of the hetero-TMD Moiré system's potential as a solid-state-based quantum simulator, we explore the extended Hubbard model on the triangular lattice using the density matrix renormalization group (DMRG). Specifically, we explore the two-dimensional phase space of the kinetic energy relative to the interaction strength $t/U$ and the further-range interaction strength $V_1/U$. We find competition between Fermi fluid, chiral spin liquid, spin density wave, and charge density wave. In particular, our finding of the optimal further-range interaction for the chiral correlation presents a tantalizing possibility.

cond-mat.str-el↗

arXiv2022

PyQMC: an all-Python real-space quantum Monte Carlo module in PySCF

We describe a new open-source Python-based package for high accuracy correlated electron calculations using quantum Monte Carlo (QMC) in real space: PyQMC. PyQMC implements modern versions of QMC algorithms in an accessible format, enabling algorithmic development and easy implementation of complex workflows. Tight integration with the PySCF environment allows for simple comparison between QMC calculations and other many-body wave function techniques, as well as access to high accuracy trial wave functions.

cond-mat.mtrl-sci↗

arXiv2022

A density-matrix renormalization group algorithm for simulating quantum circuits with a finite fidelity

We develop a density-matrix renormalization group (DMRG) algorithm for the simulation of quantum circuits. This algorithm can be seen as the extension of time-dependent DMRG from the usual situation of hermitian Hamiltonian matrices to quantum circuits defined by unitary matrices. For small circuit depths, the technique is exact and equivalent to other matrix product state (MPS) based techniques. For larger depths, it becomes approximate in exchange for an exponential speed up in computational time. Like an actual quantum computer, the quality of the DMRG results is characterized by a finite fidelity. However, unlike a quantum computer, the fidelity depends strongly on the quantum circuit considered. For the most difficult possible circuit for this technique, the so-called "quantum supremacy" benchmark of Google Inc. , we find that the DMRG algorithm can generate bit strings of the same quality as the seminal Google experiment on a single computing core. For a more structured circuit used for combinatorial optimization (Quantum Approximate Optimization Algorithm or QAOA), we find a drastic improvement of the DMRG results with error rates dropping by a factor of 100 compared with random quantum circuits. Our results suggest that the current bottleneck of quantum computers is their fidelities rather than the number of qubits.

quant-ph↗

arXiv2020

Efficient 2D Tensor Network Simulation of Quantum Systems

Simulation of quantum systems is challenging due to the exponential size of the state space. Tensor networks provide a systematically improvable approximation for quantum states. 2D tensor networks such as Projected Entangled Pair States (PEPS) are well-suited for key classes of physical systems and quantum circuits. However, direct contraction of PEPS networks has exponential cost, while approximate algorithms require computations with large tensors. We propose new scalable algorithms and software abstractions for PEPS-based methods, accelerating the bottleneck operation of contraction and refactorization of a tensor subnetwork. We employ randomized SVD with an implicit matrix to reduce cost and memory footprint asymptotically. Further, we develop a distributed-memory PEPS library and study accuracy and efficiency of alternative algorithms for PEPS contraction and evolution on the Stampede2 supercomputer. We also simulate a popular near-term quantum algorithm, the Variational Quantum Eigensolver (VQE), and benchmark Imaginary Time Evolution (ITE), which compute ground states of Hamiltonians.

cs.DC↗

arXiv2020

Interference Cancellation Based Channel Estimation for Massive MIMO Systems with Time Shifted Pilots

In massive multiple-input multiple-output (MIMO) systems with time shifted pilot (TSP) schemes, the inter-group interference caused by the pilot contamination can be eliminated when the number of base station (BS) antennas M approaches infinity. However, M is finite in practice and the effectiveness of the TSP is limited by channel estimation errors. In this paper, it is analytically shown that the mean square channel estimation error (MSCEE) of the TSP is dominated by the intergroup data interference. To reduce the MSCEE in the finite antenna massive MIMO systems, an interference cancellation based channel estimation for the TSP (IC-TSP) is proposed, where the dominant inter-group data interference is canceled based on BS cooperation. To show the advantage of the IC-TSP, the additional overhead of IC-TSP is evaluated by considering different M and the coherence time of BS-BS channels. Furthermore, the impact of sectorization and compressed sensing based BS-BS channel estimation are also discussed. We show that when 128 < M < 2048, with the inter-group data interference from the nearest two cell layers being canceled, the IC-TSP achieves a spectral efficiency gain of more than 1.2 bps/Hz over the TSP.

eess.SP↗

arXiv2020

What limits the simulation of quantum computers?

It is imperative that useful quantum computers be very difficult to simulate classically; otherwise classical computers could be used for the applications envisioned for the quantum ones. Perfect quantum computers are unarguably exponentially difficult to simulate: the classical resources required grow exponentially with the number of qubits $N$ or the depth $D$ of the circuit. Real quantum computing devices, however, are characterized by an exponentially decaying fidelity $\mathcal{F} \sim (1-ε)^{ND}$ with an error rate $ε$ per operation as small as $\approx 1\%$ for current devices. In this work, we demonstrate that real quantum computers can be simulated at a tiny fraction of the cost that would be needed for a perfect quantum computer. Our algorithms compress the representations of quantum wavefunctions using matrix product states (MPS), which capture states with low to moderate entanglement very accurately. This compression introduces a finite error rate $ε$ so that the algorithms closely mimic the behavior of real quantum computing devices. The computing time of our algorithm increases only linearly with $N$ and $D$. We illustrate our algorithms with simulations of random circuits for qubits connected in both one and two dimensional lattices. We find that $ε$ can be decreased at a polynomial cost in computing power down to a minimum error $ε_\infty$. Getting below $ε_\infty$ requires computing resources that increase exponentially with $ε_\infty/ε$. For a two dimensional array of $N=54$ qubits and a circuit with Control-Z gates, error rates better than state-of-the-art devices can be obtained on a laptop in a few hours. For more complex gates such as a swap gate followed by a controlled rotation, the error rate increases by a factor three for similar computing time.

quant-ph↗

arXiv2017

A Framework for Transceiver Designs for Multi-Hop Communications with Covariance Shaping Constraints

For multiple-input multiple-output (MIMO) transceiver designs, sum power constraint is an elegant and ideal model. When various practical limitations are taken into account e.g., peak power constraints, per-antenna power constraints, etc., covariance shaping constraints will act as an effective and reasonable model. In this paper, we develop a framework for transceiver designs for multi-hop communications under covariance shaping constraints. Particularly, we focus on multi-hop amplify-and-forward (AF) MIMO relaying communications which are recognized as a key enabling technology for device-to-device (D2D) communications for next generation wireless systems such as 5G. The proposed framework includes a broad range of various linear and nonlinear transceiver designs as its special cases. It reveals an interesting fact that the relaying operation in each hop can be understood as a matrix version weighting operation. Furthermore, the nonlinear operations of Tomolision-Harashima Precoding (THP) and Decision Feedback Equalizer (DFE) also belong to the category of this kind of matrix version weighting operation. Furthermore, for both the cases with only pure shaping constraints or joint power constraints, the closed-form optimal solutions have been derived. At the end of this paper, the performance of the various designs is assessed by simulations.

cs.IT↗

arXiv2016

On Weighted MSE Model for MIMO Transceiver Optimization

Mean-squared-error (MSE) is one of the most widely used performance metrics for the designs and analysis of multi-input-multiple-output (MIMO) communications. Weighted MSE minimization, a more general formulation of MSE minimization, plays an important role in MIMO transceiver optimization. While this topic has a long history and has been extensively studied, existing treatments on the methods in solving the weighted MSE optimization are more or less sporadic and non-systematic. In this paper, we firstly review the two major methodologies, Lagrange multiplier method and majorization theory based method, and their common procedures in solving the weighted MSE minimization. Then some problems and limitations of the methods that were usually neglected or glossed over in existing literature are provided. These problems are fundamental and of critical importance for the corresponding MIMO transceiver optimizations. In addition, a new extended matrix-field weighted MSE model is proposed. Its solutions and applications are discussed in details. Compared with existing models, this new model has wider applications, e.g., nonlinear MIMO transceiver designs and capacity-maximization transceiver designs for general MIMO networks.

cs.IT↗

arXiv2015

Transceiver designs with matrix-version water-filling architecture under mixed power constraints

In this paper, we investigate the multiple-input multiple-output (MIMO) transceiver design under an interesting power model named mixed power constraints. In the considered power model, several antenna subsets are constrained by sum power constraints while the other antennas are subject to per-antenna power constraints. This kind of transceiver designs includes both the transceiver designs under sum power constraint and per-antenna power constraint as its special cases. This kind of designs is of critical importance for distributed antenna systems (DASs) with heterogeneous remote radio heads (RRHs) such as cloud radio access networks (C-RANs). In our work, we try to solve the optimization problem in an analytical way instead of using some famous software packages e.g., CVX or SeDuMi. In our work, to strike tradeoffs between performance and complexity, both iterative and non-iterative solutions are proposed. Interestingly the non-iterative solution can be interpreted as a matrix version water-filling solution extended from the well-known and extensively studied vector version. Finally, simulation results demonstrate the accuracy of our theoretical results.

cs.IT↗

arXiv2014

Matrix-Monotonic Optimization for MIMO Systems

For MIMO systems, due to the deployment of multiple antennas at both the transmitter and the receiver, the design variables e.g., precoders, equalizers, training sequences, etc. are usually matrices. It is well known that matrix operations are usually more complicated compared to their vector counterparts. In order to overcome the high complexity resulting from matrix variables, in this paper we investigate a class of elegant multi-objective optimization problems, namely matrix-monotonic optimization problems (MMOPs). In our work, various representative MIMO optimization problems are unified into a framework of matrix-monotonic optimization, which includes linear transceiver design, nonlinear transceiver design, training sequence design, radar waveform optimization, the corresponding robust design and so on as its special cases. Then exploiting the framework of matrix-monotonic optimization the optimal structures of the considered matrix variables can be derived first. Based on the optimal structure, the matrix-variate optimization problems can be greatly simplified into the ones with only vector variables. In particular, the dimension of the new vector variable is equal to the minimum number of columns and rows of the original matrix variable. Finally, we also extend our work to some more general cases with multiple matrix variables.

cs.IT↗

arXiv2012

Distributed Resource Allocation Algorithm Design for Multi-Cell Networks Based on Advanced Decomposition Theory

In this letter, we investigate the resource allocation for downlink multi-cell coordinated OFDMA wireless networks, in which power allocation and subcarrier scheduling are jointly optimized. Aiming at maximizing the weighted sum of the minimal user rates (WSMR) of coordinated cells under individual power constraints at each base station, an effective distributed resource allocation algorithm using a modified decomposition method is proposed, which is suitable by practical implementation due to its low complexity and fast convergence speed. Simulation results demonstrate that the proposed decentralized algorithm provides substantial throughput gains with lower computational cost compared to existing schemes.

cs.IT↗