Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets
The experimental optimization of Hamiltonian problems with up to six qubits and more than one hundred Pauli terms is demonstrated, determining the ground-state energy for molecules of increasing size, up to BeH2.
Supervised learning with quantum-enhanced feature spaces
Two classification algorithms that use the quantum state space to produce feature maps are demonstrated on a superconducting processor, enabling the solution of problems when the feature space is large and the kernel functions are computationally expensive to estimate.
Error Mitigation for Short-Depth Quantum Circuits.
Two schemes are presented that mitigate the effect of errors and decoherence in short-depth quantum circuits by resampling randomized circuits according to a quasiprobability distribution.
Error mitigation extends the computational reach of a noisy quantum processor
- A. Kandala, K. Temme, A. Córcoles, A. Mezzacapo, J. Chow, J. Gambetta
- Physics, Computer ScienceNature
- 1 March 2019
This work applies the error mitigation protocol to mitigate errors in canonical single- and two-qubit experiments and extends its application to the variational optimization of Hamiltonians for quantum chemistry and magnetism.
Tapering off qubits to simulate fermionic Hamiltonians
It is shown that encodings with a given filling fraction $\nu=N/M$ and a qubit-per-mode ratio $\eta=Q/M<1$ can be constructed from efficiently decodable classical LDPC codes with the relative distance $2\nu$ and the encoding rate $1-\eta$.
Quantum optimization using variational algorithms on near-term quantum devices
The quantum volume as a metric to compare the power of near-term quantum devices is discussed and simple error-mitigation schemes are introduced that could improve the accuracy of determining ground-state energies.
A rigorous and robust quantum speed-up in supervised machine learning
A rigorous quantum speed-up for supervised classification using a quantum learning algorithm that only requires classical access to data and achieves high accuracy, robust against additive errors in the kernel entries that arise from finite sampling statistics.
Quantum Metropolis sampling
This paper demonstrates how to implement a quantum version of the Metropolis algorithm, a method that has basically acquired a monopoly on the simulation of interacting particles and permits sampling directly from the eigenstates of the Hamiltonian, and thus avoids the sign problem present in classical simulations.
Quantum logarithmic Sobolev inequalities and rapid mixing
A family of logarithmic Sobolev inequalities on finite dimensional quantum state spaces is introduced. The framework of non-commutative Lp-spaces is reviewed and the relationship between quantum…
The χ2-divergence and mixing times of quantum Markov processes
We introduce quantum versions of the χ2-divergence, provide a detailed analysis of their properties, and apply them in the investigation of mixing times of quantum Markov processes. An approach…