Papers

Alessandro Rudi, Leonard Wossnig, C. Ciliberto +3 more·Apr 6, 2018

Simulating the time-evolution of quantum mechanical systems is BQP-hard and expected to be one of the foremost applications of quantum computers. We consider classical algorithms for the approximation of Hamiltonian dynamics using subsampling methods...

G. Siopsis·Apr 5, 2018

I introduce a continuous-variable quantum topological data algorithm. The goal of the quantum algorithm is to calculate the Betti numbers in persistent homology which are the dimensions of the kernel of the combinatorial Laplacian. I accomplish this ...

Shantanav Chakraborty, András Gilyén, S. Jeffery·Apr 5, 2018

We apply the framework of block-encodings, introduced by Low and Chuang (under the name standard-form), to the study of quantum machine learning algorithms and derive general results that are applicable to a variety of input models, including sparse ...

Michael G. Bayne, A. Chakraborty·Apr 4, 2018

In this work, the composite control-variate stratified sampling (CCSS) method is presented for calculation of MO integrals without transformation of AO integrals. The central idea of this approach is to obtain the 2-electron MO integrals by direct in...

A. Roggero, J. Carlson·Apr 4, 2018

The dynamic linear response of a quantum system is critical for understanding both the structure and dynamics of strongly interacting quantum systems, including neutron scattering from materials, photon and electron scattering from atomic systems, an...

A. B. Özgüler, R. Joynt, M. Vavilov·Apr 3, 2018

A general time-dependent quantum system can be driven fast from its initial ground state to its final ground state without generating transitions by adding a steering term to the Hamiltonian. We show how this technique can be modified to improve on t...

A. Sabry, Benoît Valiron, J. K. Vizzotto·Apr 3, 2018

One perspective on quantum algorithms is that they are classical algorithms having access to a special kind of memory with exotic properties. This perspective suggests that, even in the case of quantum algorithms, the control flow notions of sequenci...

J. P. Lee, J. P. Lee, L. Wells +10 more·Apr 2, 2018

Photonic time bin qubits are well suited to transmission via optical fibres and waveguide circuits. The states take the form $\frac{1}{\sqrt{2}}(\alpha \ket{0} + e^{i\phi}\beta \ket{1})$, with $\ket{0}$ and $\ket{1}$ referring to the early and late t...

Kazuto Oshima·Apr 1, 2018

We study channel fidelity for the high-fidelity approach in the Knill-Laflamme-Milburn (KLM) scheme. We examine an optimal channel fidelity f_{opt} and identify the corresponding KLM ancilla state. In the limit of large n, where 2n is the number of t...

Zhikuan Zhao, V. Dunjko, Jack K. Fitzsimons +2 more·Apr 1, 2018

The intersection between the fields of machine learning and quantum information processing is proving to be a fruitful field for the discovery of new quantum algorithms, which potentially offer an exponential speed-up over their classical counterpart...

Changpeng Shao·Mar 31, 2018

HHL algorithm \cite{harrow} to solve linear system is a powerful and efficient quantum technique to deal with many matrix operations (such as matrix multiplication, powers and inversion). It inspires many applications in quantum machine learning \cit...

Vasileios Mavroeidis, Kamer Vishi, Mateusz Zych +1 more·Mar 31, 2018

The aim of this paper is to elucidate the implications of quantum computing in present cryptography and to introduce the reader to basic post-quantum algorithms. In particular the reader can delve into the following subjects: present cryptographic sc...

H. Krovi·Mar 30, 2018

The Schur transform is a unitary operator that block diagonalizes the action of the symmetric and unitary groups on an n fold tensor product V⊗n of a vector space V of dimension d. Bacon, Chuang and Harrow [5] gave a quantum algorithm for this transf...

W. Huggins, P. Patil, B. Mitchell +2 more·Mar 30, 2018

Machine learning is a promising application of quantum computing, but challenges remain for implementation today because near-term devices have a limited number of physical qubits and high error rates. Motivated by the usefulness of tensor networks f...

S. Pakin·Mar 30, 2018

Abstract A quantum annealer exploits quantum effects to solve a particular type of optimization problem. The advantage of this specialized hardware is that it effectively considers all possible solutions in parallel, thereby potentially outperforming...

J. McClean, S. Boixo, V. Smelyanskiy +2 more·Mar 29, 2018

Many experimental proposals for noisy intermediate scale quantum devices involve training a parameterized quantum circuit with a classical optimization loop. Such hybrid quantum-classical algorithms are popular for applications in quantum simulation,...

Weng-Long Chang, Qi Yu, Zhaokai Li +3 more·Mar 29, 2018

The maximal clique problem, to find the maximally sized clique in a given graph, is classically an NP-complete computational problem, which has potential applications ranging from electrical engineering, computational chemistry, bioinformatics to soc...

J. Arrazola, T. Bromley·Mar 28, 2018

Boson sampling devices are a prime candidate for exhibiting quantum supremacy, yet their application for solving problems of practical interest is less well understood. Here we show that Gaussian boson sampling (GBS) can be used for dense subgraph id...

Zhikuan Zhao, Jack K. Fitzsimons, Michael A. Osborne +2 more·Mar 28, 2018

Gaussian processes (GPs) are important models in supervised machine learning. Training in Gaussian processes refers to selecting the covariance functions and the associated parameters in order to improve the outcome of predictions, the core of which ...

A. Day, M. Bukov, P. Weinberg +2 more·Mar 28, 2018

We study the problem of preparing a quantum many-body system from an initial to a target state by optimizing the fidelity over the family of bang-bang protocols. We present compelling numerical evidence for a universal spin-glasslike transition contr...