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Found 47 papers, 10 papers with code
Scalable Thermodynamic Second-order Optimization
Many hardware proposals have aimed to accelerate inference in AI workloads.
Thermodynamic Bayesian Inference
Thermodynamic computing has emerged as a paradigm for accelerating operations used in machine learning, such as matrix inversion, and is based on the mapping of Langevin equations to the dynamics of noisy physical systems.
Thermodynamic Natural Gradient Descent
Second-order training methods have better convergence properties than gradient descent but are rarely used in practice for large-scale training due to their computational overhead.
A Review of Barren Plateaus in Variational Quantum Computing
Variational quantum computing offers a flexible computational paradigm with applications in diverse areas.
Thermodynamic Computing System for AI Applications
Recent breakthroughs in artificial intelligence (AI) algorithms have highlighted the need for novel computing hardware in order to truly unlock the potential for AI.
The power and limitations of learning quantum dynamics incoherently
Quantum process learning is emerging as an important tool to study quantum systems.
Challenges and Opportunities in Quantum Machine Learning
At the intersection of machine learning and quantum computing, Quantum Machine Learning (QML) has the potential of accelerating data analysis, especially for quantum data, with applications for quantum materials, biochemistry, and high-energy physics.
Thermodynamic AI and the fluctuation frontier
Hence, we propose a novel computing paradigm, where software and hardware become inseparable.
Resource frugal optimizer for quantum machine learning
In this work, we advocate for simultaneous random sampling over both the dataset as well as the measurement operators that define the loss function.
Theory for Equivariant Quantum Neural Networks
Inspired by a similar problem, recent breakthroughs in machine learning address this challenge by creating models encoding the symmetries of the learning task.
Representation Theory for Geometric Quantum Machine Learning
Recent advances in classical machine learning have shown that creating models with inductive biases encoding the symmetries of a problem can greatly improve performance.
Practical Black Box Hamiltonian Learning
We study the problem of learning the parameters for the Hamiltonian of a quantum many-body system, given limited access to the system.
Inference-Based Quantum Sensing
We show that, for a general class of unitary families of encoding, $\mathcal{R}(\theta)$ can be fully characterized by only measuring the system response at $2n+1$ parameters.
Group-Invariant Quantum Machine Learning
We present theoretical results underpinning the design of $\mathfrak{G}$-invariant models, and exemplify their application through several paradigmatic QML classification tasks including cases when $\mathfrak{G}$ is a continuous Lie group and also when it is a discrete symmetry group.
Out-of-distribution generalization for learning quantum dynamics
However, there are currently no results on out-of-distribution generalization in QML, where we require a trained model to perform well even on data drawn from a different distribution to the training distribution.
Dynamical simulation via quantum machine learning with provable generalization
Much attention has been paid to dynamical simulation and quantum machine learning (QML) independently as applications for quantum advantage, while the possibility of using QML to enhance dynamical simulations has not been thoroughly investigated.
Covariance matrix preparation for quantum principal component analysis
We also argue that PCA on quantum datasets is natural and meaningful, and we numerically implement our method for molecular ground-state datasets.
The quantum low-rank approximation problem
We consider a quantum version of the famous low-rank approximation problem.
Generalization in quantum machine learning from few training data
Modern quantum machine learning (QML) methods involve variationally optimizing a parameterized quantum circuit on a training data set, and subsequently making predictions on a testing data set (i. e., generalizing).
Subtleties in the trainability of quantum machine learning models
In this work we bridge the two frameworks and show that gradient scaling results for VQAs can also be applied to study the gradient scaling of QML models.
Theory of overparametrization in quantum neural networks
The prospect of achieving quantum advantage with Quantum Neural Networks (QNNs) is exciting.
Entangled Datasets for Quantum Machine Learning
For this purpose, we introduce the NTangled dataset composed of quantum states with different amounts and types of multipartite entanglement.
Can Error Mitigation Improve Trainability of Noisy Variational Quantum Algorithms?
On the other hand, our positive results for CDR highlight the possibility of engineering error mitigation methods to improve trainability.
Adaptive shot allocation for fast convergence in variational quantum algorithms
Variational Quantum Algorithms (VQAs) are a promising approach for practical applications like chemistry and materials science on near-term quantum computers as they typically reduce quantum resource requirements.
Equivalence of quantum barren plateaus to cost concentration and narrow gorges
Optimizing parameterized quantum circuits (PQCs) is the leading approach to make use of near-term quantum computers.
A semi-agnostic ansatz with variable structure for quantum machine learning
Our approach, called VAns (Variable Ansatz), applies a set of rules to both grow and (crucially) remove quantum gates in an informed manner during the optimization.
Qubit-efficient exponential suppression of errors
Here we present an alternative method, the Resource-Efficient Quantum Error Suppression Technique (REQUEST), that adapts this breakthrough to much fewer qubits by making use of active qubit resets, a feature now available on commercial platforms.
Quantum Physics
Long-time simulations with high fidelity on quantum hardware
Specifically, we simulate an XY-model spin chain on the Rigetti and IBM quantum computers, maintaining a fidelity of at least 0. 9 for over 600 time steps.
Connecting ansatz expressibility to gradient magnitudes and barren plateaus
Parameterized quantum circuits serve as ans\"{a}tze for solving variational problems and provide a flexible paradigm for programming near-term quantum computers.
Variational Quantum Algorithms
Applications such as simulating complicated quantum systems or solving large-scale linear algebra problems are very challenging for classical computers due to the extremely high computational cost.
Effect of barren plateaus on gradient-free optimization
We numerically confirm this by training in a barren plateau with several gradient-free optimizers (Nelder-Mead, Powell, and COBYLA algorithms), and show that the numbers of shots required in the optimization grows exponentially with the number of qubits.
Non-trivial symmetries in quantum landscapes and their resilience to quantum noise
(2) We study the resilience of the symmetries under noise, and show that while it is conserved under unital noise, non-unital channels can break these symmetries and lift the degeneracy of minima, leading to multiple new local minima.
Absence of Barren Plateaus in Quantum Convolutional Neural Networks
To derive our results we introduce a novel graph-based method to analyze expectation values over Haar-distributed unitaries, which will likely be useful in other contexts.
Generalized Measure of Quantum Fisher Information
In this work, we present a lower bound on the quantum Fisher information (QFI) which is efficiently computable on near-term quantum devices.
Quantum Physics Mathematical Physics Mathematical Physics Data Analysis, Statistics and Probability
Noise-Induced Barren Plateaus in Variational Quantum Algorithms
Specifically, for the local Pauli noise considered, we prove that the gradient vanishes exponentially in the number of qubits $n$ if the depth of the ansatz grows linearly with $n$.
Reformulation of the No-Free-Lunch Theorem for Entangled Data Sets
With the recent rise of quantum machine learning, it is natural to ask whether there is a quantum analog of the NFL theorem, which would restrict a quantum computer's ability to learn a unitary process (the quantum analog of a function) with quantum training data.
Trainability of Dissipative Perceptron-Based Quantum Neural Networks
Several architectures have been proposed for quantum neural networks (QNNs), with the goal of efficiently performing machine learning tasks on quantum data.
Security proof of practical quantum key distribution with detection-efficiency mismatch
Our method also shows that in the absence of efficiency mismatch in our detector model, the key rate increases if the loss due to detection inefficiency is assumed to be outside of the adversary's control, as compared to the view where for a security proof this loss is attributed to the action of the adversary.
Quantum Physics
Cost Function Dependent Barren Plateaus in Shallow Parametrized Quantum Circuits
Variational quantum algorithms (VQAs) optimize the parameters $\vec{\theta}$ of a parametrized quantum circuit $V(\vec{\theta})$ to minimize a cost function $C$.
Variational Fast Forwarding for Quantum Simulation Beyond the Coherence Time
Finally, we implement VFF on Rigetti's quantum computer to show simulation beyond the coherence time.
Quantum Physics
Variational Quantum Linear Solver
Specifically, we prove that $C \geq \epsilon^2 / \kappa^2$, where $C$ is the VQLS cost function and $\kappa$ is the condition number of $A$.
Quantum Physics
Variational Quantum State Diagonalization
In these algorithms, a quantum computer evaluates the cost of a gate sequence (with speedup over classical cost evaluation), and a classical computer uses this information to adjust the parameters of the gate sequence.
Quantum Physics
Quantum assisted quantum compiling
Our other circuit gives ${\rm Tr}(V^\dagger U)$ and is a generalization of the power-of-one-qubit circuit that we call the power-of-two-qubits.
Quantum Physics
Quantum Algorithm Implementations for Beginners
As quantum computers become available to the general public, the need has arisen to train a cohort of quantum programmers, many of whom have been developing classical computer programs for most of their careers.
Emerging Technologies Quantum Physics
Learning the quantum algorithm for state overlap
Furthermore, we apply our approach to the hardware-specific connectivity and gate alphabets used by Rigetti's and IBM's quantum computers and demonstrate that the shorter algorithms that we derive significantly reduce the error - compared to the Swap Test - on these computers.
Quantum Physics
Reliable numerical key rates for quantum key distribution
In this work, we present a reliable, efficient, and tight numerical method for calculating key rates for finite-dimensional quantum key distribution (QKD) protocols.
Quantum Physics
Sifting attacks in finite-size quantum key distribution
Here we show that this assumption is violated for iterative sifting, a sifting procedure that has been employed in some (but not all) of the recently suggested QKD protocols in order to increase their efficiency.
Quantum Physics
