QCB's QUOR program — Quantum Undergraduate Opportunities in Research — connects prepared undergraduates with intensive quantum research. Propose a project or join an existing one.
Propose a ProjectProjects TBD — check back soon.
QCB members have built open-source codebases, technical presentations, posters, and writeups across quantum algorithms, quantum machine learning, optimization, simulation, and visualization.
HEP · Quantum Annealing
Modeled high-energy particle-collision jets as graph nodes, weighted edges by node-attribute differences, and used max-cut / annealing methods to separate target jet signatures from background.
4
benchmarked configs
83.08%
best max accuracy
Quantum Annealing
Used max-cut, QAOA, and D-Wave quantum annealing tools to study data-driven quarantine groupings for campus reopening scenarios.
50%+
infection reduction scenario
~20%
housing scenario reduction
Quantum Machine Learning
Tested quantum neural-network approaches for sentiment classification, including recurrent quantum circuits and sentence-to-circuit workflows.
75.8%
QRNN training accuracy
69.6%
Lambeq embedding result
NISQ algorithms · simulation
Reviewed hybrid VQE methods for molecular Hamiltonians, including resource estimation, Hartree-Fock setup, and simulations for H₂ and H₂O.
2
molecules studied
~20
max simulatable qubits noted
Visualization
Explored a color-based alternative to Bloch-sphere visualization for multi-qubit, mixed-state, and entangled-state representations.
JPM
John Paul Marceaux method
Qudit
color-state visualization
Quantum Search
Implemented a variation of Grover's canonical search algorithm in PyQuil, studying the case where the goal is to identify the target block rather than the exact database item.
PyQuil
implementation