Quantum Computing – Senior Applied Research Scientist - Tensor Networks, Research & Delivery _Products & Platforms

Deloitte • Tampa, FL

18d • $134,500 - $265,100

About The Position

The Senior Applied Research Scientist – Tensor Networks joins Deloitte Consulting’s Quantum Computing team within the Research & Delivery workstream. The team develops emerging-technology capabilities and solutions, with the goal of applying quantum technologies across industries and establishing Deloitte as a thought leader in transformative quantum technology.

In this role, you will research and develop advanced computational methods for national security applications, investigating how tensor networks can support modeling, simulation, uncertainty quantification, and decision-making for complex systems. You will work with researchers, engineers, and national security specialists to turn mission needs into mathematical problems, develop algorithms, and determine where the methods provide measurable gains in accuracy, cost, or scale.

You will identify and formulate mission-relevant problems, optimize algorithms, and produce rigorous, reproducible benchmarks. You will contribute technical expertise to government proposals and translate validated research into prototypes, reusable software, technical roadmaps, and client recommendations. The role also involves mentoring colleagues and promoting high standards for technical rigor, reproducibility, software quality, and communication. Candidates must possess or be willing to seek, obtain, and maintain a U.S. government security clearance, as required for the work. Travel may be required up to 10% based on business needs.

Requirements

• Doctoral degree in Physics, Applied Mathematics, Computer Science, Quantum Information Science, Electrical Engineering, Operations Research, or a related field.
• Possess or be willing to seek, obtain, and maintain a U.S. government security clearance.
• At least 5 years of relevant research or development experience, including independently leading technically complex work from problem framing through analysis and communication of conclusions.
• Expertise in tensor network methods such as MPS, DMRG, TEBD, PEPS, tree tensor networks, or tensor train decompositions, including bond-dimension control, truncation-error analysis, and contraction-path optimization.
• Strong foundation in numerical methods, optimization, randomized numerical linear algebra, and quantum-inspired methods for machine learning.
• Ability to formulate real-world problems and assess whether structural characteristics such as low entanglement, low rank, sparsity, or operator locality make them suitable for quantum-inspired approaches.
• Demonstrated ability to design fair and rigorous benchmarks using strong baselines, appropriate tuning, controlled compute budgets, transparent metrics, and honest reporting of positive and negative results.
• Strong Python skills, with experience implementing, reproducing, or critically evaluating algorithms using scientific and quantum software such as NumPy, SciPy, JAX, PyTorch, Quimb, ITensor, Cotengra, Qiskit, Cirq, or PennyLane.
• Ability to translate technical research into client-relevant insights, business value, prototypes, roadmaps, or offering priorities.
• Evidence of technical contributions in quantum-inspired computing, tensor networks, classical simulation, or optimization, such as publications, preprints, open-source contributions, community benchmarks, or conference presentations.
• Strong written and verbal communication skills, including the ability to explain complex technical findings, uncertainty, tradeoffs, and recommendations to technical and business audiences.
• Demonstrated ability to guide collaborators, provide constructive technical feedback, and work across research, engineering, product, and business teams.
• Ability to travel up to 10%, based on business needs and the clients and industries served.
• Must be legally authorized to work in the United States without the need for employer sponsorship, now or at any time in the future.

Nice To Haves

• Research extending tensor networks beyond quantum many-body physics to applications such as uncertainty quantification, numerical integration, probabilistic graphical models, stochastic processes, differential equations, optimization, or machine learning.
• Experience developing new tensor representations, network architectures, contraction algorithms, or methods for controlling approximation error.
• Expertise in complementary areas such as probability and statistics, stochastic modeling, dynamical systems, optimal control, numerical analysis, or computational physics.
• Experience with GPU acceleration, parallel computing, or distributed scientific workloads using JAX, PyTorch, CUDA, cuTensorNet, MPI, or related technologies.
• Experience with scientific tensor software such as Quimb, ITensor, Cotengra, or comparable libraries.
• Experience applying computational methods to national security problems, including infrastructure resilience, logistics and supply chains, sensing, materials, or modeling complex systems under uncertainty.
• Experience collaborating with government agencies, national laboratories, academic researchers, or multidisciplinary research teams.
• Experience developing research proposals, helping define a research agenda, or transitioning research software to broader use.
• Familiarity with quantum algorithms or quantum simulation where relevant to the research.

Responsibilities

• Work with national security and domain specialists to translate mission challenges into mathematical models and computational problems, potentially in uncertainty quantification, rare-event analysis, resilient infrastructure and supply chains, probabilistic inference, and dynamical systems.
• Assess how dependencies, correlations, network topology, and tensor representations affect computational cost and accuracy, including identifying the promise and limitations of tensor network approaches.
• Design, implement, and optimize quantum-inspired algorithms, including tensor network, Pauli-propagation, dequantized, Ising, and QUBO-based methods, using high-performance computing and GPU resources where appropriate.
• Collaborate with engineers to improve performance on CPU, GPU, and high-performance computing platforms.
• Contribute technical expertise to government proposals and collaborations.
• Transition validated research from proof of concept to prototypes and reusable product assets, such as solver and simulator libraries, experiment frameworks, benchmarks, and engineering requirements, in partnership with product and engineering teams.
• Serve as a technical subject-matter expert in client discussions, executive briefings, proposals, demonstrations, and ecosystem collaborations; clearly communicate results, limitations, uncertainty, and risks.
• Mentor researchers and engineers, provide constructive technical review, and establish standards for research rigor, reproducibility, software quality, and communication.
• Support work with government agencies, national laboratories, academic groups, vendors, and other clearance-sensitive stakeholders in accordance with applicable clearance and information-handling requirements.

Benefits

• Deloitte offers employees a broad range of benefits.
• Opportunities to build new skills, take on leadership opportunities, and connect and grow through mentorship.
• On-the-job learning experiences and formal development programs.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Ph.D. or professional degree