![]() ![]() Noise-resilient preparation of quantum many-body ground states. ![]() Approximation algorithms for complex-valued ising models on bounded degree graphs. Infinite and Finite Sets, Keszthely ( Hungary) (Citeseer, 1973). In Colloqua Mathematica Societatis Janos Bolyai 10. Problems and results on 3-chromatic hypergraphs and some related questions. Combinatorics and Complexity of Partition Functions Vol. A note on quantum algorithms and the minimal degree of epsilon-error polynomials for symmetric functions. In 40th Annual Symposium on Foundations of Computer Science 358–368 (IEEE, 1999).ĭe Wolf, R. Bounds for small-error and zero-error quantum algorithms. In 2017 IEEE 58th Annual Symposium on Foundations of Computer Science ( FOCS) 427–438 (IEEE, 2017).īuhrman, H., Cleve, R., De Wolf, R. Local Hamiltonians whose ground states are hard to approximate. Simulating quantum computers with probabilistic methods. Complexity-theoretic foundations of quantum supremacy experiments. Establishing the quantum supremacy frontier with a 281 pflop/s simulation. Simulation of low-depth quantum circuits as complex undirected graphical models. Breaking the 49-qubit barrier in the simulation of quantum circuits. Simulating quantum computation by contracting tensor networks. The complexity of approximating complex-valued Ising and Tutte partition functions. Adptive quantum computation, constant depth quantum circuits and Arthur–Merlin games. Extending the computational reach of a noisy superconducting quantum processor. Low-cost error mitigation by symmetry verification. Recovering noise-free quantum observables. Practical quantum error mitigation for near-future applications. #Quantum error correction course u of a simulator#Efficient variational quantum simulator incorporating active error minimization. Error mitigation for short-depth quantum circuits. Supervised learning with quantum-enhanced feature spaces. Quantum machine learning in feature Hilbert spaces. Ground-state energy estimation of the water molecule on a trapped-ion quantum computer. The theory of variational hybrid quantum-classical algorithms. ![]() et al.Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets. A variational eigenvalue solver on a photonic quantum processor. Quantum supremacy using a programmable superconducting processor. IBM’s quantum cloud computer goes commercial. Quantum computing in the NISQ era and beyond. A method for obtaining digital signatures and public-key cryptosystems. Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer. Our results demonstrate that appropriate choices of circuit parameters such as geometric locality and depth are essential to obtain quantum speed-ups based on variational quantum algorithms.įeynman, R. In the special case of geometrically local two-dimensional quantum circuits, the runtime of our algorithm scales linearly with the number of qubits. We develop sub-exponential time classical algorithms for solving the quantum mean value problem for general classes of quantum observables and constant-depth quantum circuits. Here we consider the task of computing the mean values of multi-qubit observables, which is a cornerstone of variational quantum algorithms for optimization, machine learning and the simulation of quantum many-body systems. Variational quantum algorithms are leading candidates in the effort to find shallow-depth quantum algorithms that outperform classical computers. However, due to the lack of fault tolerance, the qubits can be operated for only a few time steps, making the quantum circuits shallow in depth. The latest generation of quantum processors with ~50 qubits are expected to be at the brink of outperforming classical computers. Quantum algorithms hold the promise of solving certain computational problems dramatically faster than their classical counterparts. ![]()
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