A5026258482- Quantum Computing Algorithms and Architecture
- Quantum Information and Cryptography
- Quantum and electron transport phenomena
- Aluminum Alloys Composites Properties
- Microstructure and mechanical properties
- Aluminum Alloy Microstructure Properties
- Quantum-Dot Cellular Automata
- Quantum many-body systems
- DNA and Biological Computing
- Neural Networks and Reservoir Computing
- Advancements in Semiconductor Devices and Circuit Design
- Cloud Computing and Resource Management
- Ferroelectric and Piezoelectric Materials
- Molecular Junctions and Nanostructures
- Quantum Mechanics and Applications
- Multiferroics and related materials
- Stochastic Gradient Optimization Techniques
- Advanced Sensor and Energy Harvesting Materials
- Advanced Data Storage Technologies
- Computability, Logic, AI Algorithms
- Metal Alloys Wear and Properties
- Advanced materials and composites
- Scientific Computing and Data Management
- Computational Drug Discovery Methods
- Parallel Computing and Optimization Techniques
State Key Laboratory of Remote Sensing Science
2024
Zapata (United States)
2019-2023
University of Science and Technology Beijing
2018-2022
Fudan University
2021
Harvard University
2017-2021
Purdue University West Lafayette
2012-2020
Novartis (China)
2018
Qatar Foundation
2015-2017
Santa Fe Institute
2017
Boston University
2004
Quantum simulation of chemistry and materials is predicted to be an important application for both near-term fault-tolerant quantum devices. However, at present, developing studying algorithms these problems can difficult due the prohibitive amount domain knowledge required in area algorithms. To help bridge this gap open field more researchers, we have developed OpenFermion software package (www.openfermion.org). open-source library written largely Python under Apache 2.0 license, aimed...
Quantum computing has rapidly advanced in recent years due to substantial development both hardware and algorithms. These advances are carrying quantum computers closer their impending commercial utility. Drug discovery is a promising area of application that will find number uses for these new machines. As prominent example, simulation enable faster more accurate characterizations molecular systems than existing chemistry methods. Furthermore, algorithmic developments machine learning offer...
Even the most sophisticated artificial neural networks are built by aggregating substantially identical units called neurons. A neuron receives multiple signals, internally combines them, and applies a non-linear function to resulting weighted sum. Several attempts generalize neurons quantum regime have been proposed, but all proposals collided with difficulty of implementing activation functions, which is essential for classical neurons, due linear nature mechanics. Here we propose solution...
Quantum computers have the potential of solving certain problems exponentially faster than classical computers. Recently, Harrow, Hassidim and Lloyd proposed a quantum algorithm for linear systems equations: given an $N\times{N}$ matrix $A$ vector $\vec b$, find x$ that satisfies $A\vec x = \vec b$. It has been shown using one could obtain solution encoded in state $|x$ $O(\log{N})$ operations, while algorithms require at least O(N) steps. If is not interested $\vec{x}$ itself but...
De novo drug design with desired biological activities is crucial for developing novel therapeutics patients. The development process time- and resource-consuming, it has a low probability of success. Recent advances in machine learning deep technology have reduced the time cost discovery therefore, improved pharmaceutical research development. In this paper, we explore combination two rapidly fields lead candidate process. First, artificial intelligence already been demonstrated to...
We introduce a quantum–classical generative model for small-molecule design, specifically targeting KRAS inhibitors cancer therapy. apply the method to select and synthesize 15 proposed molecules that could notably engage with therapy, two holding promise future development as inhibitors. This work showcases potential of quantum computing generate experimentally validated hits compare favorably against classical models. A hybrid combines approaches compounds protein.
The Poisson equation occurs in many areas of science and engineering. Here we focus on its numerical solution for an d dimensions. In particular present a quantum algorithm scalable circuit design which approximates the grid with error \varepsilon. We assume are given supersposition function evaluations right hand side equation. produces state encoding solution. number operations qubits used by is almost linear polylog \varepsilon^{-1}. modules together performance guarantees can be also...
Quantum simulation of chemistry and materials is predicted to be an important application for both near-term fault-tolerant quantum devices. However, at present, developing studying algorithms these problems can difficult due the prohibitive amount domain knowledge required in area algorithms. To help bridge this gap open field more researchers, we have developed OpenFermion software package (www.openfermion.org). open-source library written largely Python under Apache 2.0 license, aimed...
In the near-term, hybrid quantum-classical algorithms hold great potential for outperforming classical approaches. Understanding how these two computing paradigms work in tandem is critical identifying areas where such could provide a quantum advantage. this work, we study QAOA-based optimization algorithm by implementing Variational Quantum Factoring (VQF) algorithm. We execute experimental demonstrations using superconducting processor and investigate trade-off between resources (number of...
Classical simulation of quantum computation is necessary for studying the numerical behavior algorithms, as there does not yet exist a large viable computer on which to perform tests. Tensor network (TN) contraction an algorithmic method that can efficiently simulate some circuits, often greatly reducing computational cost over methods full Hilbert space. In this study we implement tensor program simulating circuits using multi-core compute nodes. We show results Max-Cut problem 3- through...
The discovery of small molecules with therapeutic potential is a long-standing challenge in chemistry and biology. Researchers have increasingly leveraged novel computational techniques to streamline the drug development process increase hit rates reduce costs associated bringing market. To this end, we introduce quantum-classical generative model that seamlessly integrates power quantum algorithms trained on 16-qubit IBM computer established reliability classical methods for designing...
The number of measurements demanded by hybrid quantum-classical algorithms such as the variational quantum eigensolver (VQE) is prohibitively high for many problems practical value. For problems, realizing advantage will require methods that dramatically reduce this cost. Previous measurement cost (e.g., amplitude and phase estimation) error rates are too low near-term implementation. Here we propose take available coherence to maximally enhance power sampling on noisy devices, reducing...
Application of the adiabatic model quantum computation requires efficient encoding solution to computational problems into lowest eigenstate a Hamiltonian that supports universal computation. Experimental systems are typically limited restricted forms 2-body interactions. Therefore, method for approximating many-body Hamiltonians up arbitrary spectral error using at most gadgets, introduced around decade ago, offer only current means address this requirement. Although applications gadgets...
Projective adaptive resonance theory (PART) neural network developed by Cao and Wu recently has been shown to be very effective in clustering data sets high dimensional spaces. The PART algorithm is based on the assumptions that model equations of (a large scale singularly perturbed system differential coupled with a reset mechanism) have quite regular computational performance. This paper provides rigorous proof these dynamics when signal functions are special step functions, additional...
We investigate several aspects of realizing quantum computation using entangled polar molecules in pendular states. Quantum algorithms typically start from a product state |00⋯0⟩ and we show that up to negligible error, the ground states molecule arrays can be considered as unentangled qubit basis . This prepared by simply allowing system reach thermal equilibrium at low temperature (<1 mK). also evaluate entanglement, characterized concurrence qubits dipole governed external electric field,...
Implementing a scalable quantum information processor using polar molecules in optical lattices requires precise control over the long-range dipole–dipole interaction between selected lattice sites. We present here scheme trapped open-shell that allows dipolar exchange processes nearest and next-nearest neighbors to be controlled order construct generalized transverse Ising spin Hamiltonian with tunable XX, YY XY couplings rotating frame of driving lasers. The moderately strong bias magnetic...
It is expected that the simulation of correlated fermions in chemistry and material science will be one first practical applications quantum processors. Given rapid evolution hardware, it increasingly important to develop robust benchmarking techniques gauge capacity hardware specifically for purpose fermionic simulation. Here we propose using one-dimensional Fermi-Hubbard model as an application benchmark variational simulations on near-term devices. Since Hubbard both strongly exactly...