1 code implementation • 29 Jan 2022 • Mohammad Hassan Khatami, Udson C. Mendes, Nathan Wiebe, Philip M. Kim
In our quantum algorithms, we use custom pair-wise energy tables consisting of eight different amino acids.
1 code implementation • 15 Dec 2021 • Brian Flynn, Antonio Andreas Gentile, Nathan Wiebe, Raffaele Santagati, Anthony Laing
Accurate models of real quantum systems are important for investigating their behaviour, yet are difficult to distill empirically.
no code implementations • 6 Nov 2020 • Joonho Lee, Dominic Berry, Craig Gidney, William J. Huggins, Jarrod R. McClean, Nathan Wiebe, Ryan Babbush
We describe quantum circuits with only $\widetilde{\cal O}(N)$ Toffoli complexity that block encode the spectra of quantum chemistry Hamiltonians in a basis of $N$ arbitrary (e. g., molecular) orbitals.
Quantum Physics Chemical Physics
no code implementations • 29 Oct 2020 • Carlos Ortiz Marrero, Mária Kieferová, Nathan Wiebe
In particular, we show that quantum neural networks that satisfy a volume-law in the entanglement entropy will give rise to models not suitable for learning with high probability.
1 code implementation • 14 Feb 2020 • Antonio A. Gentile, Brian Flynn, Sebastian Knauer, Nathan Wiebe, Stefano Paesani, Christopher E. Granade, John G. Rarity, Raffaele Santagati, Anthony Laing
An isolated system of interacting quantum particles is described by a Hamiltonian operator.
2 code implementations • 26 Jul 2019 • Guang Hao Low, Vadym Kliuchnikov, Nathan Wiebe
We introduce well-conditioned multiproduct formulas, which are a linear combination of product formulas, where a single step has polynomial cost $\mathcal{O}(m^2\log{(m)})$ and succeeds with probability $\Omega(1/\operatorname{log}^2{(m)})$.
Quantum Physics Computational Physics
no code implementations • 23 May 2019 • Nathan Wiebe, Leonard Wossnig
In this article we provide a method for fully quantum generative training of quantum Boltzmann machines with both visible and hidden units while using quantum relative entropy as an objective.
no code implementations • 1 Apr 2019 • Guang Hao Low, Nicholas P. Bauman, Christopher E. Granade, Bo Peng, Nathan Wiebe, Eric J. Bylaska, Dave Wecker, Sriram Krishnamoorthy, Martin Roetteler, Karol Kowalski, Matthias Troyer, Nathan A. Baker
Fault-tolerant quantum computation promises to solve outstanding problems in quantum chemistry within the next decade.
Quantum Physics Emerging Technologies Chemical Physics Computational Physics
no code implementations • 5 Feb 2019 • Nicholas P. Bauman, Eric J. Bylaska, Sriram Krishnamoorthy, Guang Hao Low, Nathan Wiebe, Karol Kowalski
In analogy to the standard single-reference SES-CC formalism, its unitary CC extension allows one to include the dynamical (outside the active space) correlation effects in an SES induced complete active space (CAS) effective Hamiltonian.
Quantum Physics
3 code implementations • 2 Apr 2018 • Maria Schuld, Alex Bocharov, Krysta Svore, Nathan Wiebe
In this paper, we propose a low-depth variational quantum algorithm for supervised learning.
Quantum Physics
no code implementations • 20 Dec 2017 • Alessandro Lumino, Emanuele Polino, Adil S. Rab, Giorgio Milani, Nicolò Spagnolo, Nathan Wiebe, Fabio Sciarrino
Phase estimation protocols provide a fundamental benchmark for the field of quantum metrology.
no code implementations • 19 Dec 2017 • Iris Agresti, Niko Viggianiello, Fulvio Flamini, Nicolò Spagnolo, Andrea Crespi, Roberto Osellame, Nathan Wiebe, Fabio Sciarrino
The difficulty of validating large-scale quantum devices, such as Boson Samplers, poses a major challenge for any research program that aims to show quantum advantages over classical hardware.
no code implementations • 17 Nov 2017 • Nathan Wiebe, Ram Shankar Siva Kumar
Finally, we provide a private form of $k$--means clustering that can be used to prevent an all powerful adversary from learning more than a small fraction of a bit from any user.
no code implementations • 1 Nov 2017 • András Gilyén, Srinivasan Arunachalam, Nathan Wiebe
We also show that in a continuous phase-query model, our gradient computation algorithm has optimal query complexity up to poly-logarithmic factors, for a particular class of smooth functions.
Quantum Physics Computational Complexity
no code implementations • 28 Nov 2016 • Jacob Biamonte, Peter Wittek, Nicola Pancotti, Patrick Rebentrost, Nathan Wiebe, Seth Lloyd
Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data.
no code implementations • 11 May 2016 • Markus Reiher, Nathan Wiebe, Krysta M. Svore, Dave Wecker, Matthias Troyer
We show how a quantum computer can be employed to elucidate reaction mechanisms in complex chemical systems, using the open problem of biological nitrogen fixation in nitrogenase as an example.
Quantum Physics
no code implementations • NeurIPS 2016 • Nathan Wiebe, Ashish Kapoor, Krysta M. Svore
We demonstrate how quantum computation can provide non-trivial improvements in the computational and statistical complexity of the perceptron model.
no code implementations • 9 Dec 2015 • Ilia Zintchenko, Matthew Hastings, Nathan Wiebe, Ethan Brown, Matthias Troyer
Heuristic optimisers which search for an optimal configuration of variables relative to an objective function often get stuck in local optima where the algorithm is unable to find further improvement.
no code implementations • 20 Nov 2015 • Nathan Wiebe, Christopher Granade, Ashish Kapoor, Krysta M. Svore
We provide a method for approximating Bayesian inference using rejection sampling.
no code implementations • 9 Jul 2015 • Nathan Wiebe, Ashish Kapoor, Christopher Granade, Krysta M. Svore
We present an efficient classical algorithm for training deep Boltzmann machines (DBMs) that uses rejection sampling in concert with variational approximations to estimate the gradients of the training objective function.
no code implementations • 10 Dec 2014 • Nathan Wiebe, Ashish Kapoor, Krysta M. Svore
In recent years, deep learning has had a profound impact on machine learning and artificial intelligence.
2 code implementations • 9 Jan 2014 • Nathan Wiebe, Ashish Kapoor, Krysta Svore
In the worst case, our quantum algorithms lead to polynomial reductions in query complexity relative to the corresponding classical algorithm.
Quantum Physics
1 code implementation • 27 Feb 2012 • Andrew M. Childs, Nathan Wiebe
We present a new approach to simulating Hamiltonian dynamics based on implementing linear combinations of unitary operations rather than products of unitary operations.
Quantum Physics
no code implementations • 22 Aug 2011 • Sadegh Raeisi, Nathan Wiebe, Barry C. Sanders
We construct an efficient autonomous quantum-circuit design algorithm for creating efficient quantum circuits to simulate Hamiltonian many-body quantum dynamics for arbitrary input states.
Quantum Physics