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Found 30 papers, 10 papers with code
Predicting quantum channels over general product distributions
Previously, Huang, Chen, and Preskill proved a surprising result that even if $E$ is arbitrary, this task can be solved in time roughly $n^{O(\log(1/\epsilon))}$, where $\epsilon$ is the target prediction error.
Certifying almost all quantum states with few single-qubit measurements
Certifying that an n-qubit state synthesized in the lab is close to the target state is a fundamental task in quantum information science.
Learning shallow quantum circuits
Despite fundamental interests in learning quantum circuits, the existence of a computationally efficient algorithm for learning shallow quantum circuits remains an open question.
Learning quantum states and unitaries of bounded gate complexity
While quantum state tomography is notoriously hard, most states hold little interest to practically-minded tomographers.
Tight bounds on Pauli channel learning without entanglement
In this work, we consider learning algorithms without entanglement to be those that only utilize states, measurements, and operations that are separable between the main system of interest and an ancillary system.
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.
Improved machine learning algorithm for predicting ground state properties
Finding the ground state of a quantum many-body system is a fundamental problem in quantum physics.
Hardware-efficient learning of quantum many-body states
Efficient characterization of highly entangled multi-particle systems is an outstanding challenge in quantum science.
Learning to predict arbitrary quantum processes
We present an efficient machine learning (ML) algorithm for predicting any unknown quantum process $\mathcal{E}$ over $n$ qubits.
The Complexity of NISQ
The recent proliferation of NISQ devices has made it imperative to understand their computational power.
Learning many-body Hamiltonians with Heisenberg-limited scaling
In contrast, the best previous algorithms, such as recent works using gradient-based optimization or polynomial interpolation, require a total evolution time of $\mathcal{O}(\epsilon^{-2})$ and $\mathcal{O}(\epsilon^{-2})$ experiments.
Foundations for learning from noisy quantum experiments
When one cannot explore the full state space but all operations are approximately known and noise in Clifford gates is gate-independent, we find an efficient algorithm for learning all operations up to a single unlearnable parameter characterizing the fidelity of the initial state.
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.
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.
Revisiting dequantization and quantum advantage in learning tasks
In this note, we prove that classical algorithms with SQ access can accomplish some learning tasks exponentially faster than quantum algorithms with quantum state inputs.
Quantum advantage in learning from experiments
Quantum technology has the potential to revolutionize how we acquire and process experimental data to learn about the physical world.
A Hierarchy for Replica Quantum Advantage
We prove that given the ability to make entangled measurements on at most $k$ replicas of an $n$-qubit state $\rho$ simultaneously, there is a property of $\rho$ which requires at least order $2^n$ measurements to learn.
Exponential separations between learning with and without quantum memory
We study the power of quantum memory for learning properties of quantum systems and dynamics, which is of great importance in physics and chemistry.
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).
Provably efficient machine learning for quantum many-body problems
In this work, we prove that classical ML algorithms can efficiently predict ground state properties of gapped Hamiltonians in finite spatial dimensions, after learning from data obtained by measuring other Hamiltonians in the same quantum phase of matter.
On Liouville systems at critical parameters, Part 2: Multiple bubbles
In this paper, we continue to consider the generalized Liouville system: $$ \Delta_g u_i+\sum_{j=1}^n a_{ij}\rho_j\left(\frac{h_j e^{u_j}}{\int h_j e^{u_j}}- {1} \right)=0\quad\text{in \,}M,\quad i\in I=\{1,\cdots, n\}, $$ where $(M, g)$ is a Riemann surface $M$ with volume $1$, $h_1,.., h_n$ are positive smooth functions and $\rho_j\in \mathbb R^+$($j\in I$).
Analysis of PDEs Mathematical Physics Mathematical Physics 35J60, 35J55
Information-theoretic bounds on quantum advantage in machine learning
We prove that for any input distribution $\mathcal{D}(x)$, a classical ML model can provide accurate predictions on average by accessing $\mathcal{E}$ a number of times comparable to the optimal quantum ML model.
Power of data in quantum machine learning
These constructions explain numerical results showing that with the help of data, classical machine learning models can be competitive with quantum models even if they are tailored to quantum problems.
Concentration for random product formulas
qDRIFT achieves a gate count that does not explicitly depend on the number of terms in the Hamiltonian, which contrasts with Suzuki formulas.
Quantum Physics Probability
TensorFlow Quantum: A Software Framework for Quantum Machine Learning
We introduce TensorFlow Quantum (TFQ), an open source library for the rapid prototyping of hybrid quantum-classical models for classical or quantum data.
Predicting Many Properties of a Quantum System from Very Few Measurements
This description, called a classical shadow, can be used to predict many different properties: order $\log M$ measurements suffice to accurately predict $M$ different functions of the state with high success probability.
Predicting Features of Quantum Systems from Very Few Measurements
Predicting features of complex, large-scale quantum systems is essential to the characterization and engineering of quantum architectures.
FlowQA: Grasping Flow in History for Conversational Machine Comprehension
Conversational machine comprehension requires the understanding of the conversation history, such as previous question/answer pairs, the document context, and the current question.
Ranked #1 on
Question Answering
on QuAC
FusionNet: Fusing via Fully-Aware Attention with Application to Machine Comprehension
This paper introduces a new neural structure called FusionNet, which extends existing attention approaches from three perspectives.
Ranked #26 on
Question Answering
on SQuAD1.1 dev
