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Found 35 papers, 3 papers with code
Hybrid Quantum-Classical Scheduling for Accelerating Neural Network Training with Newton's Gradient Descent
Nevertheless, one of its major bottlenecks is matrix inversion, which is notably time-consuming in $O(N^3)$ time with weak scalability.
Feint in Multi-Player Games
This paper introduces the first formalization, implementation and quantitative evaluation of Feint in Multi-Player Games.
A comprehensive review of Quantum Machine Learning: from NISQ to Fault Tolerance
Quantum machine learning, which involves running machine learning algorithms on quantum devices, has garnered significant attention in both academic and business circles.
Energy-Efficient Power Control for Multiple-Task Split Inference in UAVs: A Tiny Learning-Based Approach
Specifically, we replace the optimization of transmit power with that of transmission time to decrease the computational complexity of OP since we reveal that energy consumption monotonically decreases with increasing transmission time.
Dynamical phase transition in quantum neural networks with large depth
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.
UAV Swarm Deployment and Trajectory for 3D Area Coverage via Reinforcement Learning
Unmanned aerial vehicles (UAVs) are recognized as promising technologies for area coverage due to the flexibility and adaptability.
Quantum Data Center: Perspectives
A quantum version of data centers might be significant in the quantum era.
Beyond MD17: the reactive xxMD dataset
We show that the xxMD dataset involves diverse geometries which represent chemical reactions.
Potential Energy Advantage of Quantum Economy
Energy cost is increasingly crucial in the modern computing industry with the wide deployment of large-scale machine learning models and language models.
Fundamental causal bounds of quantum random access memories
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.
Towards provably efficient quantum algorithms for large-scale machine-learning models
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.
Catapult Dynamics and Phase Transitions in Quadratic Nets
To do this, we show that for a certain range of learning rates the weight norm decreases whenever the loss becomes large.
Estimating truncation effects of quantum bosonic systems using sampling algorithms
To simulate bosons on a qubit- or qudit-based quantum computer, one has to regularize the theory by truncating infinite-dimensional local Hilbert spaces to finite dimensions.
SnCQA: A hardware-efficient equivariant quantum convolutional circuit architecture
We propose SnCQA, a set of hardware-efficient variational circuits of equivariant quantum convolutional circuits respective to permutation symmetries and spatial lattice symmetries with the number of qubits $n$.
Unifying O(3) Equivariant Neural Networks Design with Tensor-Network Formalism
Equivariant graph neural networks are a standard approach to such problems, with one of the most successful methods employing tensor products between various tensors that transform under the spatial group.
Quantum Power Flows: From Theory to Practice
Climate change is becoming one of the greatest challenges to the sustainable development of modern society.
Stochastic noise can be helpful for variational quantum algorithms
Saddle points constitute a crucial challenge for first-order gradient descent algorithms.
Quantum Computing Methods for Supply Chain Management
We focus on applying quantum computing to operations management problems in industry, and in particular, supply chain management.
Symmetric Pruning in Quantum Neural Networks
To fill this knowledge gap, here we propose the effective quantum neural tangent kernel (EQNTK) and connect this concept with over-parameterization theory to quantify the convergence of QNNs towards the global optima.
Data centers with quantum random access memory and quantum networks
In this paper, we propose the Quantum Data Center (QDC), an architecture combining Quantum Random Access Memory (QRAM) and quantum networks.
On the Super-exponential Quantum Speedup of Equivariant Quantum Machine Learning Algorithms with SU($d$) Symmetry
We introduce a framework of the equivariant convolutional algorithms which is tailored for a number of machine-learning tasks on physical systems with arbitrary SU($d$) symmetries.
Laziness, Barren Plateau, and Noise in Machine Learning
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.
Quantum Kerr Learning
Quantum machine learning is a rapidly evolving field of research that could facilitate important applications for quantum computing and also significantly impact data-driven sciences.
Estimating the randomness of quantum circuit ensembles up to 50 qubits
Random quantum circuits have been utilized in the contexts of quantum supremacy demonstrations, variational quantum algorithms for chemistry and machine learning, and blackhole information.
Geometry-Based Stochastic Probability Models for the LoS and NLoS Paths of A2G Channels under Urban Scenario
Path probability prediction is essential to describe the dynamic birth and death of propagation paths, and build the accurate channel model for air-to-ground (A2G) communications.
Analytic theory for the dynamics of wide quantum neural networks
We define wide quantum neural networks as parameterized quantum circuits in the limit of a large number of qubits and variational parameters.
Speeding up Learning Quantum States through Group Equivariant Convolutional Quantum Ansätze
We develop a theoretical framework for $S_n$-equivariant convolutional quantum circuits with SU$(d)$-symmetry, building on and significantly generalizing Jordan's Permutational Quantum Computing (PQC) formalism based on Schur-Weyl duality connecting both SU$(d)$ and $S_n$ actions on qudits.
Representation Learning via Quantum Neural Tangent Kernels
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.
Towards a variational Jordan-Lee-Preskill quantum algorithm
Rapid developments of quantum information technology show promising opportunities for simulating quantum field theory in near-term quantum devices.
Toward simulating Superstring/M-theory on a quantum computer
Furthermore, for certain states in the Berenstein-Maldacena-Nastase (BMN) matrix model, several supersymmetric quantum field theories dual to superstring/M-theory can be realized on a quantum device.
High Energy Physics - Theory High Energy Physics - Lattice Quantum Physics
Quantum simulation of gauge theory via orbifold lattice
We propose a new framework for simulating $\text{U}(k)$ Yang-Mills theory on a universal quantum computer.
High Energy Physics - Theory High Energy Physics - Lattice Quantum Physics
RODE-Net: Learning Ordinary Differential Equations with Randomness from Data
To train the RODE-Net, we first estimate the parameters of the unknown RODE using the symbolic networks \cite{long2019pde} by solving a set of deterministic inverse problems based on the measured data, and use a generative adversarial network (GAN) to estimate the true distribution of the RODE's parameters.
Prediction stability as a criterion in active learning
Recent breakthroughs made by deep learning rely heavily on large number of annotated samples.
A Locating Model for Pulmonary Tuberculosis Diagnosis in Radiographs
Significance: We first make full use of the feature extraction ability of CNNs in TB diagnostic field and make exploration in localization of TB, when the previous works focus on the weaker task of healthy-sick subject classification.
PartsNet: A Unified Deep Network for Automotive Engine Precision Parts Defect Detection
Defect detection is a basic and essential task in automatic parts production, especially for automotive engine precision parts.
