Search Results for author:
Found 24 papers, 7 papers with code
Quantum-circuit design for efficient simulations of many-body quantum dynamics
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
Hamiltonian Simulation Using Linear Combinations of Unitary Operations
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
Quantum Algorithms for Nearest-Neighbor Methods for Supervised and Unsupervised Learning
In the worst case, our quantum algorithms lead to polynomial reductions in query complexity relative to the corresponding classical algorithm.
Quantum Physics
Quantum Deep Learning
In recent years, deep learning has had a profound impact on machine learning and artificial intelligence.
Quantum Inspired Training for Boltzmann Machines
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.
Bayesian inference via rejection filtering
We provide a method for approximating Bayesian inference using rejection sampling.
Partial Reinitialisation for Optimisers
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.
Quantum Perceptron Models
We demonstrate how quantum computation can provide non-trivial improvements in the computational and statistical complexity of the perceptron model.
Elucidating Reaction Mechanisms on Quantum Computers
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
Quantum Machine Learning
Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data.
Optimizing quantum optimization algorithms via faster quantum gradient computation
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
Hardening Quantum Machine Learning Against Adversaries
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.
Pattern recognition techniques for Boson Sampling validation
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.
Experimental Phase Estimation Enhanced By Machine Learning
Phase estimation protocols provide a fundamental benchmark for the field of quantum metrology.
Circuit-centric quantum classifiers
In this paper, we propose a low-depth variational quantum algorithm for supervised learning.
Quantum Physics
Downfolding of many-body Hamiltonians using active-space models: extension of the sub-system embedding sub-algebras approach to unitary coupled cluster formalisms
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
Q# and NWChem: Tools for Scalable Quantum Chemistry on Quantum Computers
Fault-tolerant quantum computation promises to solve outstanding problems in quantum chemistry within the next decade.
Quantum Physics Emerging Technologies Chemical Physics Computational Physics
Generative training of quantum Boltzmann machines with hidden units
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.
Well-conditioned multiproduct Hamiltonian simulation
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
Learning models of quantum systems from experiments
An isolated system of interacting quantum particles is described by a Hamiltonian operator.
Entanglement Induced Barren Plateaus
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.
Even more efficient quantum computations of chemistry through tensor hypercontraction
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
Quantum Model Learning Agent: characterisation of quantum systems through machine learning
Accurate models of real quantum systems are important for investigating their behaviour, yet are difficult to distill empirically.
Gate-based Quantum Computing for Protein Design
In our quantum algorithms, we use custom pair-wise energy tables consisting of eight different amino acids.
