activity
20152026
most citedQuantum algorithms and the finite element method

192 citations · 479 across the 11 of their papers we have counts for

collaborators

12 papers

quant-ph2026

Compiling the 2D Fermi-Hubbard ground-state energy estimation algorithm for active volume quantum architectures

Harriet Apel, Athena Caesura, Carys Harvey +11

As quantum computing enters the early fault-tolerant era, circuit compilation choices will increasingly depend on details of the underlying architecture rather than solely optimizi…

quant-ph2025★ 1 cited

A comprehensive framework to simulate real-time chemical dynamics on a fault-tolerant quantum computer

Felipe H. da Jornada, Matteo Lostaglio, Sam Pallister +2

We present a comprehensive end-to-end framework for simulating the real-time dynamics of chemical systems on a fault-tolerant quantum computer, incorporating both electronic and nu…

quant-ph2024

Quantum sampling algorithms for quantum state preparation and matrix block-encoding

Jessica Lemieux, Matteo Lostaglio, Sam Pallister +4

The problems of quantum state preparation and matrix block-encoding are ubiquitous in quantum computing: they are crucial parts of various quantum algorithms for the purpose for in…

quant-ph2023★ 5 cited

The cost of solving linear differential equations on a quantum computer: fast-forwarding to explicit resource counts

David Jennings, Matteo Lostaglio, Robert B. Lowrie +2

How well can quantum computers simulate classical dynamical systems? There is increasing effort in developing quantum algorithms to efficiently simulate dynamics beyond Hamiltonian…

quant-ph2023

Fidelity estimation of quantum states on a silicon photonic chip

Sabine Wollmann, Xiaogang Qiang, Sam Pallister +3

As a measure of the 'closeness' of two quantum states, fidelity plays a fundamental role in quantum information theory. Fidelity estimation protocols try to strike a balance betwee…

quant-ph2023

Randomized adiabatic quantum linear solver algorithm with optimal complexity scaling and detailed running costs

David Jennings, Matteo Lostaglio, Sam Pallister +2

Solving linear systems of equations is a fundamental problem with a wide variety of applications across many fields of science, and there is increasing effort to develop quantum li…

quant-ph2021★ 101 cited

Fault-tolerant resource estimate for quantum chemical simulations: Case study on Li-ion battery electrolyte molecules

Isaac H. Kim, Eunseok Lee, Ye-Hua Liu +3

We estimate the resources required in the fusion-based quantum computing scheme to simulate electrolyte molecules in Li-ion batteries on a fault-tolerant, photonic quantum computer…

quant-ph2020★ 1 cited

A Jordan-Wigner gadget that reduces T count by more than 6x for quantum chemistry applications

Sam Pallister

Quantum computers have the potential to be a profoundly transformative technology, particularly in the context of quantum chemistry. However, running a chemistry application that i…

quant-ph2017★ 127 cited

Optimal verification of entangled states with local measurements

Sam Pallister, Noah Linden, Ashley Montanaro

Consider the task of verifying that a given quantum device, designed to produce a particular entangled state, does indeed produce that state. One natural approach would be to chara…

astro-ph.IM2016★ 17 cited

A blueprint for a simultaneous test of quantum mechanics and general relativity in a space-based quantum optics experiment

Sam Pallister, Simon Coop, Valerio Formichella +12

In this paper we propose an experiment designed to observe a general-relativistic effect on single photon interference. The experiment consists of a folded Mach-Zehnder interferome…

quant-ph2015★ 35 cited

Advances in quantum machine learning

Jeremy Adcock, Euan Allen, Matthew Day +7

Here we discuss advances in the field of quantum machine learning. The following document offers a hybrid discussion; both reviewing the field as it is currently, and suggesting di…

quant-ph2015★ 192 cited

Quantum algorithms and the finite element method

Ashley Montanaro, Sam Pallister

The finite element method is used to approximately solve boundary value problems for differential equations. The method discretises the parameter space and finds an approximate sol…