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Found 15 papers, 7 papers with code
On the average-case complexity of learning output distributions of quantum circuits
In this work, we show that learning the output distributions of brickwork random quantum circuits is average-case hard in the statistical query model.
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.
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.
Symmetry-resolved entanglement detection using partial transpose moments
We propose an ordered set of experimentally accessible conditions for detecting entanglement in mixed states.
Quantum Physics Statistical Mechanics
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.
Stochastic Quantum Circuit Simulation Using Decision Diagrams
Recent years have seen unprecedented advance in the design and control of quantum computers.
Quantum Physics
As Accurate as Needed, as Efficient as Possible: Approximations in DD-based Quantum Circuit Simulation
Quantum computers promise to solve important problems faster than conventional computers.
Quantum Physics
Characteristics of Reversible Circuits for Error Detection
In this work, we consider error detection via simulation for reversible circuit architectures.
Hardware Architecture Emerging Technologies
Random Stimuli Generation for the Verification of Quantum Circuits
Verification of quantum circuits is essential for guaranteeing correctness of quantum algorithms and/or quantum descriptions across various levels of abstraction.
Quantum Physics Emerging Technologies
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
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.
Recovering quantum gates from few average gate fidelities
For the important case of characterising multi-qubit unitary gates, we provide a rigorously guaranteed and practical reconstruction method that works with an essentially optimal number of average gate fidelities measured respect to random Clifford unitaries.
Quantum Physics Information Theory Information Theory
A unifying framework for relaxations of the causal assumptions in Bell's theorem
Bell's Theorem shows that quantum mechanical correlations can violate the constraints that the causal structure of certain experiments impose on any classical explanation.
