A5062047279- Quantum Computing Algorithms and Architecture
- Quantum Information and Cryptography
- Quantum and electron transport phenomena
- Data Visualization and Analytics
- Computability, Logic, AI Algorithms
- Neural Networks and Reservoir Computing
- Quantum-Dot Cellular Automata
- Advanced Multi-Objective Optimization Algorithms
- Stochastic Gradient Optimization Techniques
- Neural Networks and Applications
- Quantum Mechanics and Applications
- Conferences and Exhibitions Management
- Evolutionary Algorithms and Applications
Leiden University
2018-2024
Delft University of Technology
2019-2020
QuTech
2019-2020
Quantum simulation of chemistry and materials is predicted to be an important application for both near-term fault-tolerant quantum devices. However, at present, developing studying algorithms these problems can difficult due the prohibitive amount domain knowledge required in area algorithms. To help bridge this gap open field more researchers, we have developed OpenFermion software package (www.openfermion.org). open-source library written largely Python under Apache 2.0 license, aimed...
We investigate the performance of error mitigation via measurement conserved symmetries on near-term devices. present two protocols to measure during bulk an experiment, and develop a third, zero-cost, post-processing protocol which is equivalent variant quantum subspace expansion. methods for inserting global local into algorithms, adjusting natural problem boost errors produced by different noise channels. demonstrate these techniques two- four-qubit simulations hydrogen molecule (using...
Variational quantum algorithms (VQAs) offer a promising path toward using near-term hardware for applications in academic and industrial research. These aim to find approximate solutions problems by optimizing parametrized circuit classical optimization algorithm. A successful VQA requires fast reliable algorithms. Understanding how off-the-shelf methods perform this context is important the future of field. In work, we study performance four commonly used gradient-free methods: SLSQP,...
Variational quantum eigensolvers offer a small-scale testbed to demonstrate the performance of error mitigation techniques with low experimental overhead. We present successful by applying recently proposed symmetry verification technique estimation ground-state energy and ground state hydrogen molecule. A finely adjustable exchange interaction between two qubits in circuit QED processor efficiently prepares variational ansatz states single-excitation subspace respecting parity qubit-mapped...
Many applications of quantum simulation require to prepare and then characterize states by performing an efficient partial tomography estimate observables corresponding $k$-body reduced density matrices ($k$-RDMs). For instance, variational algorithms for the chemistry usually that one measure fermionic 2-RDM. While such marginals provide a tractable description from which many important properties can be computed, their determination often requires prohibitively large number circuit...
Modeling chemical reactions and complicated molecular systems has been proposed as the `killer application' of a future quantum computer. Accurate calculations derivatives eigenenergies are essential towards this end, allowing for geometry optimization, transition state searches, predictions response to an applied electric or magnetic field, dynamics simulations. In work, we survey methods calculate energy derivatives, present two new methods: one based on phase estimation, other low-order...
The key challenge in the noisy intermediate-scale quantum era is finding useful circuits compatible with current device limitations. Variational algorithms (VQAs) offer a potential solution by fixing circuit architecture and optimizing individual gate parameters an external loop. However, parameter optimization can become intractable, overall performance of algorithm depends heavily on initially chosen architecture. Several search (QAS) have been developed to design architectures...
The most scalable proposed methods of simulating lattice fermions on noisy quantum computers employ encodings that eliminate nonlocal operators using a constant factor more qubits and nontrivial stabilizer group. In this work, we investigated the straightforward error mitigation strategy group, postselection, is very natural to setting fermionic simulation. We numerically investigate performance range systems containing up 42 number fundamental simulation tasks including non-equilibrium...
We present a metric, average randomness, that predicts the compatibility of set quantum states with Haar-random distribution, by matching statistical moments, through known observable. show Haar-randomness is connected to Dirichlet and provide closed-form expression, simple bounds moments. generalize this metric permutation- unitary-equivalent observables, ensuring if extended randomness compatible then approximately Haar-random.
The Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) is one of the most advanced algorithms in numerical black-box optimization. For noisy objective functions, several approaches were proposed to mitigate noise, e.g., re-evaluations same solution or adapting population size. In this paper, we devise a novel method adaptively choose optimal re-evaluation number for function values corrupted by additive Gaussian white noise. We derive theoretical lower bound expected improvement...
Quantum machine learning (QML) has surged as a prominent area of research with the objective to go beyond capabilities classical models. A critical aspect any task is process data embedding, which directly impacts model performance. Poorly designed data-embedding strategies can significantly impact success task. Despite its importance, rigorous analyses effects are limited, leaving many cases without effective assessment methods. In this work, we introduce metric for binary classification...
The parameters of the quantum circuit in a variational algorithm induce landscape that contains relevant information regarding its optimization hardness. In this work we investigate such landscapes through lens content, measure variability between points parameter space. Our major contribution connects content to average norm gradient, for which provide robust analytical bounds on estimators. This result holds any (classical or quantum) landscape. We validate understating by numerically...
In the noisy near-term quantum computing era variational algorithms are promising to explore boundaries of advantage. One prevalent instance is eigensolver (VQE) employed in chemistry approximate ground-state energies molecules. The performance VQE depends on design parametrized circuit, as well optimization algorithm used optimize those parameters. Circuit methods have been developed generate circuits reach accurate approximations ground-states. this work, we conduct an empirical comparison...
With the lockdowns caused by COVID-19 pandemic, researchers turn to online conferencing. While posing new challenges, this format also brings multiple advantages. We argue that virtual conferences will become part of our regular scientific communication and invite community members join movement.