Quantum Computing Intern
BQP
This job at a glance
- Who can apply
- New grads and up to 1 year
- Must know
- Data structures & algorithmsMachine learningDeep learningCircuit designPython
- Work
- On-site in Syracuse · internship
- Pay
- Not stated by the employer.
- Skill demand
- Machine learning33%Data structures & algorithms21%Circuit design11%Deep learning7%of 99 similar entry-level jobs posted in the US this week
- Listed
- Oct 27, 2026 · still open
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About the Role
We are seeking a Quantum Computing Intern to join our research team and contribute to the development, benchmarking, and improvement of cutting-edge quantum algorithms. The intern will work at the intersection of variational quantum computing, quantum machine learning, and scientific computing — tackling challenges such as barren plateaus, hardware execution, and algorithmic efficiency. This is a hands-on research role where you will help push the boundaries of what is possible on near-term quantum hardware.
Key Responsibilities
- Design, implement, and improve variational quantum algorithms, with a focus on the Variational Quantum Linear Solver (VQLS) and related solvers.
- Develop and benchmark Quantum Machine Learning (QML) models, including Quantum-Assisted Physics-Informed Neural Networks (PINN / QAPINN) and Physics-Informed Neural Operators (PINO / QAPINO).
- Work on quantum optimization algorithms such as the Quantum Approximate Optimization Algorithm (QAOA), including ansatz design, parameter initialization, and performance analysis.
- Investigate and apply techniques to mitigate barren plateaus in variational quantum circuits (e.g., layer-wise training, smart initialization, problem-inspired ansätze, symmetry-preserving circuits).
- Execute and benchmark algorithms on real quantum hardware, manage noise, and analyze hardware-specific performance trade-offs.
- Document results clearly through technical reports, internal presentations, and contributions to publications.
Requirements & Tech Stack
- Demonstrated experience improving variational quantum algorithms such as VQLS.
- Hands-on experience with Quantum Machine Learning (QML), including PINN / QAPINN and PINO / QAPINO architectures.
- Practical experience with quantum optimization algorithms, particularly QAOA.
- Working knowledge of barren plateau phenomena and strategies for handling them.
- Experience executing quantum algorithms on real quantum hardware (e.g., IBM Quantum, IonQ, Quantinuum, Rigetti, or similar).
- Proficiency in Python and at least one quantum SDK (e.g., Qiskit, PennyLane, Cirq, or Braket).
- Strong foundation in linear algebra, probability, and numerical methods.
Good to Have
- Experience with Quantum Monte Carlo (QMC) methods.
- Familiarity with uncertainty quantification techniques.
- Upper-level / advanced linear algebra (tensor methods, spectral analysis, matrix decompositions, Krylov methods).
- Background in Computational Fluid Dynamics (CFD) and partial differential equations (PDEs).
- Knowledge of quantum error correction (QEC) and error mitigation techniques.
- Experience with quantum data loading / state preparation methods (e.g., amplitude encoding, QRAM-style approaches).
Along with your resume, please submit copies of any posters and papers you have authored or co-authored. Preference will be given to candidates with demonstrable research output, even if preliminary or unpublished.