QC Ware

Description

The Staff Scientist will drive and oversee efforts to discover, implement, test, and deploy hybrid quantum/classical algorithms for the prediction of chemical properties on near-term quantum circuit hardware. The Staff Scientist will actively work with QC Ware clients (industrial customers and US federal funding agencies) to develop end-to-end workflows for the quantum solution of real-world chemistry problems. The overarching goal of this role is for the Staff Scientist to find ways to solve serious chemistry problems on NISQ hardware, where the quantum computing solution offers a performance advantage.

Responsibilities

Role

Requirements

Experience with quantum circuit algorithms and programming, e.g., within Cirq, Qiskit, PyQuil, or similar
Demonstrated experience in solving industrial chemistry problems using classical theoretical chemistry methodology (e.g., in pharmaceuticals or materials)
Experience mentoring team members

Experience with quantum circuit algorithms and programming, e.g., within Cirq, Qiskit, PyQuil, or similar
Demonstrated experience in solving industrial chemistry problems using classical theoretical chemistry methodology (e.g., in pharmaceuticals or materials)
Experience mentoring team members

Preferred Qualifications

Experience with quantum circuit algorithms and programming, e.g., within Cirq, Qiskit, PyQuil, or similar
Demonstrated experience in solving industrial chemistry problems using classical theoretical chemistry methodology (e.g., in pharmaceuticals or materials)
Experience mentoring team members

Minimum Qualifications

Ph.D. in Computational Chemistry, Computational Condensed Matter Physics or similar field
One or more postdoctoral appointments, or industry experience beyond the Ph.D.
Fluency with C++, CUDA, or other high-performance classical programming languages
Demonstrated contributions to one or more major classical electronic structure codes.
Demonstrated publication record in quantum chemistry applications and/or methods development projects
Fluency with Python
Zeal to learn new skills as-needed in mathematics, physics, computer science, and engineering to deliver high-quality technical solutions
Ability to work independently and within a larger team

Compensation

Attractive cash salary and stock option package
Health/vision/dental coverage for employees and dependents
Option to work and publish with top-quality university and national lab collaborators.

About You

About QC Ware

QC Ware has an established and growing portfolio of near-term quantum algorithms for quantum chemistry applications. We particularly focus on hybrid quantum/classical algorithms that provide an end-to-end prediction of useful chemical properties such as excitation energies and molecular interaction energies that use extensive classical pre-computation to compress the “hard kernel” of the problem to the minimal-sized qubit problem. More details can be found in some of our recent papers:

MC-VQE+AIEM – arXiv:1901.01234 – Extension of the variational quantum eigensolver (VQE) to even-handed treatment of excited states and transition properties, introduction of the ab initio exciton model (AIEM) as a means to treat systems with thousands of atoms with a few dozen qubits for suitable photochemistry problems.
MC-VQE+AIEM Gradients – arXiv:1906.08728 – Application of the Lagrangian formalism of derivative theory in classical electronic structure to efficiently compute analytic nuclear gradients of MC-VQE+AIEM energies.
QFD – arXiv:1909.08925 – Method between VQE and QPE that uses more parallel measurements to solve the electronic structure problem via a subspace ansatz of quantum basis functions that are classically diagonalized in postprocessing.
Quantum SAPT - arXiv:2110.01589 - Use of symmetry-adapted perturbation theory (SAPT) as a simple and efficient means to compute interaction energies between large molecular systems with a hybrid method combing NISQ-era quantum and classical computers.

We are part of a large collaborative research endeavor in quantum algorithms for photochemistry sponsored by the US Department of Energy (DoE), and involving collaborators at SLAC National Accelerator Laboratory, Oak Ridge National Laboratory, University of Pennsylvania, and Columbia University. We also deliver quantum algorithms solutions for quantum chemistry for a number of industrial and government clients, including Covestro and Boehringer Ingleheim.

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Staff Scientist in Chemistry