Quantum computing is a transformative technology with the potential to enhance operations in the space industry through the acceleration of optimization and machine learning processes. Machine learning processes enable automated image classification in geospatial data. New quantum algorithms provide novel approaches for solving these problems and a potential future advantage over current, classical techniques. Universal Quantum Computers, currently under development by Rigetti Computing and other providers, enable fully general quantum algorithms to be executed, with theoretically proven speed-up over classical algorithms in certain cases. This paper describes an approach to satellite image classification using a universal quantum enhancement to convolutional neural networks: the quanvolutional neural network. Using a refined method, we found a performance improvement over previous quantum efforts in this domain and identified potential refinements that could lead to an eventual quantum advantage. We benchmark these networks using the SAT-4 satellite imagery data set in order to demonstrate the utility of machine learning techniques in the space industry and the potential advantages that quantum machine learning can offer.
In this paper we discuss analogue computers based on quantum optical systems accelerating dynamic programming for some computational problems. These computers, at least in principle, can be realized by actually existing devices. We estimate an acceleration in resolving of some NP-hard problems that can be obtained in such a way versus deterministic computers
Variational quantum algorithms (VQAs) provide a promising approach to achieve quantum advantage in the noisy intermediate-scale quantum era. In this era, quantum computers experience high error rates and quantum error detection and correction is not feasible. VQAs can utilize noisy qubits in tandem with classical optimization algorithms to solve hard problems. However, VQAs are still slow relative to their classical counterparts. Hence, improving the performance of VQAs will be necessary to make them competitive. While VQAs are expected perform better as the problem sizes increase, increasing …
Accelerating Inference for Multilayer Neural Networks with Quantum Computers
Department of Materials, University of Oxford（牛津大学材料系）；Mathematical Institute, University of Oxford（牛津大学数学研究所）；Quantum Motion（Quantum Motion公司）；Centre for Quantum Technologies, National University of Singapore（新加坡国立大学量子中心）；Department of Computer Science, National University of Singapore（新加坡国立大学计算机科学系）
Quantum computers have been widely speculated to offer significant advantages in obtaining the ground state of difficult Hamiltonian in chemistry and physics. In this work, we first propose a Lyapunov control-inspired strategy to accelerate the well-established imaginary-time method for ground-state preparation. We also dig for the source of acceleration of the imaginary-time process under Lyapunov control with theoretical understanding and dynamic process visualization. To make the method accessible in the noisy intermediate-scale quantum era, we further propose a variational form of the algo…
Quantum computing for the biological sciences is an area of rapidly growing interest, but specific industrial applications remain elusive. Quantum Markov chain Monte Carlo has been proposed as a method for accelerating a broad class of computational problems, including problems of pharmaceutical interest. Here we investigate the prospects of quantum advantage via this approach, by applying it to the problem of modelling antibody structure, a crucial task in drug development. To minimize the resources required while maintaining pharmaceutical-level accuracy, we propose a specific encoding of mo…
Recently it has been shown that time-optimal quantum computation is attained by using the Cartan decomposition of a unitary matrix. We extend this approach by noting that the unitary group is compact. This allows us to reduce the execution time of a quantum algorithm $U_{\rm alg}$ further by adding an extra gate $W$ to it. This gate $W$ sends $U_{\rm alg}$ to another algorithm $WU_{\rm alg}$ which is executable in a shorter time than $U_{\rm alg}$. We call this technique warp-drive. Here we show both theoretically and experimentally that the execution time of Grover's algorithm is reduced in t…
Arbitrary parallel entangling gates with independent calibration on a trapped ion quantum computer
Joint Quantum Institute, University of Maryland（马里兰大学联合量子研究所）；National Quantum Laboratory (QLab), University of Maryland（马里兰大学国家量子实验室）；Department of Physics, University of Maryland（马里兰大学物理系）；Duke Quantum Center and Department of Physics, Duke University（杜克大学量子中心及物理系）
Most research in quantum computing today is performed against simulations of quantum computers rather than true quantum computers. Simulating a quantum computer entails implementing all of the unitary operators corresponding to the quantum gates as tensors. For high numbers of qubits, performing tensor multiplications for these simulations becomes quite expensive, since $N$-qubit gates correspond to $2^{N}$-dimensional tensors. One way to accelerate such a simulation is to use field programmable gate array (FPGA) hardware to efficiently compute the matrix multiplications. Though FPGAs can effi…
University of Vienna（维也纳大学）；Austrian Academy of Sciences（奥地利科学院）；University of Toronto（多伦多大学）；Vector Institute for Artificial Intelligence（向量人工智能研究所）
The design of new devices and experiments in science and engineering has historically relied on the intuitions of human experts. This credo, however, has changed. In many disciplines, computer-inspired design processes, also known as inverse-design, have augmented the capability of scientists. Here we visit different fields of physics in which computer-inspired designs are applied. We will meet vastly diverse computational approaches based on topological optimization, evolutionary strategies, deep learning, reinforcement learning or automated reasoning. Then we draw our attention specifically …
Quantum Transformer: Accelerating model inference via quantum linear algebra
Centre for Quantum Technologies, National University of Singapore（新加坡国立大学量子技术中心）；Department of Computer Science, University of Toronto（多伦多大学计算机科学系）；Vector Institute for Artificial Intelligence（人工智能向量研究所）；School of Computing, National University of Singapore（新加坡国立大学计算机学院）；Department of Mathematics, National University of Singapore（新加坡国立大学数学系）；NVIDIA Corporation（NVIDIA公司）；Department of Chemistry, University of Toronto（多伦多大学化学系）；Research Center for Emerging Computing Technologies, National Institute of Advanced Industrial Science and Technology (AIST)（新兴计算技术研究中心，日本国家先进工业科学与技术研究院）；Quantum Computing Center, Keio University（Keio大学量子计算中心）；Department of Materials Science & Engineering, University of Toronto（多伦多大学材料科学与工程系）；Department of Chemical Engineering & Applied Chemistry, University of Toronto（多伦多大学化学工程与应用化学系）；Lebovic Fellow, Canadian Institute for Advanced Research（加拿大高级研究研究院Lebovic研究员）
