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Boris Kiefer

Boris Kiefer

Professor of Physics · New Mexico State University

Computational materials science, quantum information science and engineering, quantum photonics, and solution-architecture-oriented research software. Current work emphasizes translating complex scientific and quantum technologies into bounded workflows, transparent simulations, reproducible proofs of concept, and decision-support tools.

Gardiner Hall 354 · 575-646-1932 · bkiefer@nmsu.edu

Simulators & Research Software Resume Education & Professional Experience GitHub

Research

Computational Materials Science

Density functional theory, classical potentials, electronic structure, magnetism, defects, phase stability, low-dimensional materials, and structure-property relationships.

Quantum Information Science & Engineering

Quantum computing, quantum photonics, many-body physics, tensor networks, continuous-variable quantum information, quantum networking, and quantum measurement.

Data Science & Materials Informatics

Materials analytics, data mining, materials design, database comparison, AI-assisted scientific workflows, and computational materials discovery.

Selected Computational Materials Research

  • Electronic structure and magnetism.
  • Physical and chemical properties of defects.
  • Topological materials and quantum computing.
  • The effect of intercrystalline water on material properties.
  • Low-dimensional materials: clusters, wires, surfaces, interfaces, and bulk solids.

Mahatara, S. and Kiefer, B., “Layer dependent magnetism and topology in monolayer and bilayers ReX3 (X = Br, I),” Journal of Physics: Condensed Matter, DOI: 10.1088/1361-648X/ac1c2e (2021).

Mahatara, S. and Kiefer, B., “Does beta-PbO2 Harbor Topological States?”, Journal of Physics: Condensed Matter, DOI: 10.1088/1361-648X/ab79fb (2020).

Steciuk, G., Kiefer, B., Hornfeck, W., Kasatkin, A. V., and Plášil, J., “Molybdenum Disorder in Hydrated Sedovite, Ideally U(MoO4)2, a Microporous Nanocrystalline Mineral Characterized by Three-Dimensional Electron Diffraction, Density-Functional-Theory Computations, and Complexity Analysis,” Inorganic Chemistry, DOI: 10.1021/acs.inorgchem.1c01506 (2021).

Plasil, J., Ghazisaeed, S., Kiefer, B., and Philippo, S., “Hydrogen bonding in the crystal structure of phurcalite, Ca2[(UO2)3O2(PO4)2]·7H2O: Single-crystal X-ray study and Torque calculations,” Acta Crystallographica B, DOI: 10.1107/S2052520620005739 (2020).

Ghazisaeed, S., Md, M., Nakotte, H., and Kiefer, B., “DFT predicted symmetry lowering from cubic to tetragonal in Nickel Hexacyanoferrate,” Journal of Applied Crystallography, DOI: 10.1107/S1600576719016492 (2020).

Marquez Chavez, J. and Kiefer, B., “MATCOR, a program for the cross validation of material properties between databases,” Computational Materials Science, DOI: 10.1016/j.commatsci.2020.110103 (2020). MATCOR software.

Quantum Solutions Architecture

From scientific question to decision-ready workflow

My solution-architecture work focuses on translating complex quantum and scientific capabilities into usable technical workflows: clarifying the problem, identifying constraints, bounding the solution space, building transparent proofs of concept, validating outputs, and communicating tradeoffs to technical and non-technical stakeholders.

This approach combines more than two decades of computational materials science with quantum computing, quantum photonics, benchmarking, scientific software, and human-guided AI workflows. The goal is not to recommend quantum by default, but to identify where quantum, classical, or classical-plus-AI methods provide the most credible path.

Solutions Architecture Resume View Public Simulator Portfolio

Simulators and Research Software

Open-source physics and quantum-science tools

Browse interactive simulators and computational tools for quantum foundations, state preparation, quantum optics and photonics, quantum networking, fault tolerance, materials physics, and applied scientific computing. Many repositories include technical briefs, guided exercises, or other supporting resources.

Browse Simulators & Research Software

Teaching & Learning Resources

A simple picture of quantum computing: A quantum computer works like a carefully tuned system of waves. With n qubits, it carries amplitudes across 2n possible states simultaneously. A quantum algorithm adjusts the phases of these amplitudes so that interference tends to cancel paths leading to unwanted outcomes and reinforce paths leading to useful ones. Measurement then reveals one outcome, with the algorithm designed to make the desired result much more likely.
Quantum communication: Quantum communication uses quantum waves, often carried by single photons or entangled pairs, to connect distant locations. Their amplitudes and phases can encode quantum information and produce correlations with no classical counterpart. Because measuring an unknown quantum state generally disturbs it, interception can leave detectable traces, enabling applications such as secure key distribution and verification of quantum links.
Quantum sensing: Quantum sensing uses the extreme sensitivity of quantum waves to their surroundings. Small changes in magnetic fields, electric fields, motion, time, or other physical quantities shift the phase of a quantum state, and interference converts these tiny phase changes into measurable signals. Specially prepared quantum states, such as squeezed or entangled states, can reduce measurement uncertainty beyond what comparable classical states allow, making even smaller effects detectable.

Mathematics Primers

Quantum Information Science Primers

Writing & Computing

Other Physics Primers

Quantum Information Science & Engineering

Topics include many-body physics, tensor networks, continuous-variable measurement-based quantum computing, quantum computing, quantum photonics, and quantum information science.

Quantum Computing Projects & Lecture Notes

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Undergraduate Students

Graduate Students