Quantum Computing Intern

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.

Roles and 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.

Required Qualifications -

• 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.

Company Description -

BQP is a pioneering company advancing quantum-inspired simulation technologies under its platform BQPhy®, designed to tackle the most complex computational problems in aerospace, space, and defense industries. Leveraging quantum-inspired algorithms and high-performance computing, BQPhy® delivers significantly higher speed and accuracy compared to existing GPU-based solutions, empowering organizations with accelerated innovation and sustainable competitive advantages.