A publication of the Universities Space Research Association                                November 2019

Featured ArXiv Papers: October 15th - November 14th, 2019

The noisy-intermediate-scale-quantum (NISQ) Computing newsletter is a curated ArXiv Digest published once a month by the USRA Research Institute for Advanced Computer Science. We feature exclusively experiments and algorithms that advance the field of applied quantum computing in existing hardware. The team of editors collaborates with the NASA Ames Quantum Artificial Intelligence Laboratory (QuAIL) to produce the content.

To submit a paper for consideration please visit the NISQC newsletter page at http://riacs.usra.edu/quantum/nisqc-nl.

NISQ Experiments

Experiment: Superconducting

Crossing a topological phase transition with a quantum computer

Quantum Algorithms for Mixed Binary Optimization applied to Transaction Settlement

An Improved Implementation Approach for Quantum Phase Estimation on Quantum Computers

Quantum Synchronization on the IBM Q System

Cancer Detection Using Quantum Neural Networks: A Demonstration on a Quantum Computer

Experimental Simulation of Hybrid Quantum Systems and Entanglement on a Quantum Computer

Experiment: Analog/Quantum Annealing

Multilevel Combinatorial Optimization Across Quantum Architectures

Fairness evaluation during the conceptual design of heat grids with quantum annealers

Estimating the Density of States of Boolean Satisfiability Problems on Classical and Quantum Computing Platforms

Quantum Computing based Hybrid Solution Strategies for Large-scale Discrete-Continuous Optimization Problems

Scaling advantage in quantum simulation of geometrically frustrated magnets

Comparison of D-Wave Quantum Annealing and Classical Simulated Annealing for Local Minima Determination

Experiment: Photonic

Boson sampling with 20 input photons in 60-mode interferometers at 1014 state spaces

Universal programmable photonic architecture for quantum information processing

Experimental Collision-Free Dominant Boson Sampling

NISQ Algorithms

Algorithm: Machine Learning 

A simple approach to design quantum neural networks and its applications to kernel-learning methods

Quantum Circuit Parameters Learning with Gradient Descent Using Backpropagation

Quantum State Discrimination Using Noisy Quantum Neural Networks

Quantum Algorithms for Deep Convolutional Neural Networks

Hybrid Quantum-Classical Convolutional Neural Networks

Algorithm: Optimization

The Quantum Approximate Optimization Algorithm and the Sherrington-Kirkpatrick Model at Infinite Size

Robust Control Optimization for Quantum Approximate Optimization Algorithm

Mapping NP-Hard Problems to Restricted Adiabatic Quantum Architectures

Solving Multi-Coloring Combinatorial Optimization Problems Using Hybrid Quantum Algorithms

Reinforcement-Learning-Based Variational Quantum Circuits Optimization for Combinatorial Problems

Portfolio rebalancing experiments using the Quantum Alternating Operator Ansatz

Algorithm: Simulation

Noise-Resilient Quantum Dynamics Using Symmetry-Preserving Ansatzes

Projected Cooling Algorithm for Quantum Computation

A Domain-agnostic, Noise-resistant, Hardware-efficient Evolutionary Variational Quantum Eigensolver

Is the Trotterized UCCSD Ansatz usefully well-defined?

Quantum equation of motion for computing molecular excitation energies on a noisy quantum processor

Collective optimization for variational quantum eigensolvers

Quantum simulation by qubitization without Toffoli gates

A Small, Highly Accurate Quantum Processor for Intermediate-Depth Quantum Simulations

Algorithm: Benchmarking, Software Tools, Compilation

Compilation by stochastic Hamiltonian sparsification

An approximate description of quantum states

Using Spectral Graph Theory to Map Qubits onto Connectivity-Limited Devices

Optimization of CNOT circuits on topological superconducting processors

XACC: A System-Level Software Infrastructure for Heterogeneous Quantum-Classical Computing

Algorithm: Other

Sampling-based sublinear low-rank matrix arithmetic framework for dequantizing quantum machine learning

Quantum-Inspired Classical Algorithms for Singular Value Transformation