Quantum Characterization, Verification, and Validation Engineer

PsiQuantum•Milpitas, CA

1d•$88,600 - $148,700

About The Position

PsiQuantum is seeking a researcher to develop methods for quantum characterization, verification, and validation (QCVV) of large-scale quantum systems. The role focuses on reliably characterizing quantum channels and system-level behavior where direct reconstruction is impossible, using scalable protocols, statistical analysis, and simulation methods to turn experimental data into actionable conclusions about quantum performance. Large-scale simulation is a key part of the work, including characterizing quantum channels, validating QCVV protocols, understanding error mechanisms, and predicting how characterization methods perform on large systems. Relevant approaches may include tensor networks, GPU-accelerated simulation, Monte Carlo methods, stabilizer simulation, and other techniques for modeling large quantum circuits and noisy quantum processes. PsiQuantum builds photonic quantum computers. Experience with photonic quantum computing is valuable but not required; candidates should be eager to learn relevant photonic concepts and apply their QCVV, modeling, and simulation expertise to a photonic fault-tolerant platform. This hands-on technical role is suited to someone comfortable owning research projects end to end, working across disciplines, communicating uncertainty clearly, and translating technical results into decisions that improve system characterization and performance.

Requirements

Ph.D. or equivalent experience in Physics, Electrical Engineering, Applied Mathematics, Computer Science, or a related field.
Experience characterizing or modeling quantum channels, noisy quantum circuits, quantum processes, or quantum error mechanisms.
Experience developing numerical simulations for quantum systems, quantum circuits, or noisy quantum processes.
Programming experience for scientific computing and data analysis, preferably in Python.
Ability to reason quantitatively about uncertainty, noise, finite statistics, model mismatch, and scalability.
Ability to work independently on open-ended technical problems while collaborating effectively with specialists across multiple disciplines.
Strong written and verbal communication skills, including explaining technical results, assumptions, limitations, and risks clearly.

Nice To Haves

Experience with QCVV techniques such as randomized benchmarking, gate set tomography, cycle benchmarking, process tomography, shadow tomography, Hamiltonian learning, or related methods.
Experience with scalable characterization methods for systems where full state or process reconstruction is not possible.
Experience with tensor network methods for simulating quantum circuits, quantum channels, or noisy quantum processes.
Experience running large-scale simulations on GPUs, HPC clusters, or distributed computing infrastructure.
Experience with simulation tools and frameworks such as PyTorch, JAX, TensorFlow, CUDA, CuPy, NumPy/SciPy, ITensor, Stim, or related tools.
Familiarity with quantum error correction, fault tolerance, logical error rates, decoder performance, Pauli errors, erasure errors, leakage, or correlated noise.
Experience analyzing data from experimental quantum hardware platforms.
Experience with photonic quantum computing, quantum optics, integrated photonics, measurement-based quantum computation, fusion-based quantum computation, or cluster-state models.
Experience collaborating across theory, software, and experimental teams.

Responsibilities

Develop scalable QCVV methods for characterizing large quantum systems, quantum channels, and system-level quantum behavior.
Develop statistical and numerical methods to extract meaningful performance information from limited, noisy, and indirect measurement data.
Build models connecting observed experimental data to underlying quantum channels, error mechanisms, and system-level performance metrics.
Use large-scale quantum circuit simulation to evaluate characterization protocols, diagnose failure modes, and understand noisy quantum channels.
Apply simulation methods such as tensor networks, GPU-accelerated computation, stabilizer methods, and Monte Carlo techniques where appropriate.
Work with experimental, architecture, software, and systems teams to compare measured behavior against theoretical and numerical models.
Contribute to defining diagnostic observables and data analysis pipelines for reliable system characterization.
Document methods, assumptions, limitations, and results clearly.
Communicate progress, challenges, uncertainties, and conclusions to technical and cross-functional stakeholders.
Own research tasks from problem definition through implementation, validation, analysis, and communication.