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Found 14 papers, 7 papers with code
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.
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.
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.
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.
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.
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.
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.
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.
Cellular-automaton decoders with provable thresholds for topological codes
We propose a new cellular automaton (CA), the Sweep Rule, which generalizes Toom's rule to any locally Euclidean lattice.
Quantum Physics Disordered Systems and Neural Networks Statistical Mechanics
Quantum Computing in the NISQ era and beyond
Noisy Intermediate-Scale Quantum (NISQ) technology will be available in the near future.
Quantum Physics Strongly Correlated Electrons
Combining dynamical decoupling with fault-tolerant quantum computation
We study how dynamical decoupling (DD) pulse sequences can improve the reliability of quantum computers.
Quantum Physics Mesoscale and Nanoscale Physics
Topological entanglement entropy
We formulate a universal characterization of the many-particle quantum entanglement in the ground state of a topologically ordered two-dimensional medium with a mass gap.
High Energy Physics - Theory Strongly Correlated Electrons Quantum Physics
Simple Proof of Security of the BB84 Quantum Key Distribution Protocol
We prove the security of the 1984 protocol of Bennett and Brassard (BB84) for quantum key distribution.
Quantum Physics
Fault-tolerant quantum computation
The discovery of quantum error correction has greatly improved the long-term prospects for quantum computing technology.
Quantum Physics
