Step-1
Define the Challenge/Research Thesis
We start with a simple question - what’s worth solving? Together, we shape your ideas into focused, high impact challenges aligned with your goals.
Step-2
Expert teams, built just for you
Technology is often interdisciplinary. Using WISER’s global network of scientists, engineers, and domain experts, we assemble a curated team of experts with exactly the skills your project needs.
Step-3
Work in Fast, Measurable Sprints
Each project runs on clear milestones, managed by a dedicated principal investigator. We handle planning, documentation, compute setup, and delivery - so you can focus on results.
Step-4
From prototype to impact
We deliver tangible, IP-protected solutions including working code, benchmarks, reports, and demos ready to showcase. After delivery, we help you scale, implement, and grow your solution.
< 5 steps >
How to get started
With WISER’s Solutions Launchpad, you don’t have to wait years for university partnerships or academic approvals to take shape. We move fast - helping you scope your problem, define the work plan, and begin research in as little as three months.
Each project is time-boxed, low-risk, and fully managed by a custom team of experts chosen for your goals. You can test new ideas, validate what works, and turn science into real-world applications - all while seeing measurable results without long-term commitments. With WISER, you focus on industry impact, not just fundamental research.
Taking you from "What if" to "Here's what we built".
< what's slowing your simulation? >
Our Projects
Development of Advanced Quantum Algorithms
Verifying quantum speedup in coupled harmonic oscillator systems through practical implementations using Classiq; with concrete efforts on clarifying the resource requirements of the algorithm and provide concrete pathways toward practical quantum advantage.
Why quantum now?
Hitting a compute bottleneck? Tell us what’s slowing your simulations or models.
We'll help you see if quantum can speed things up.
Confident in your classical setup?
Share your best in-class approach and we'll show you how to start building its quantum-ready version.
Already developing quantum solutions?
Let's benchmark them together and make sure they match global standards and performance.
Just getting started?
If you're exploring quantum for the first time, we'll help you map a simple, low-risk path forward.
< case studies >
Our Projects
Development of Advanced Quantum Algorithms
Verifying quantum speedup in coupled harmonic oscillator systems through practical implementations using Classiq; with concrete efforts on clarifying the resource requirements of the algorithm and provide concrete pathways toward practical quantum advantage.
Quantum PDE Solvers for CFD
Developing quantum circuits to solve nonlinear fluid equations using QTN and HSE frameworks, offering new paths to simulate shockwaves in fluid flows. By compressing state spaces and reducing circuit depth, benchmarking quantum solvers on the Burgers’ equation, laying the groundwork for scalable, noise-resilient quantum CFD.
Quantum Computing for Anti Money Laundering
AML is a large-scale and complex problem involving detection of suspicious activity using transaction data, customer behavior and various parameters resulting in slower pipelines. Quantum feature maps and kernels may offer richer representations and Scaling costs for traditional methods necessitate the need for a Quantum/ Hybrid Approach not replacing classical AML, but extending it where it strains.
Quantum Computing for Portfolio Optimization
Sampling-based quantum optimization for portfolio optimization for Financial use cases. Using binary decision variables and quadratic objectives to simulate realistic trading scenarios, and not only achieving computational gains but also maintaining interpretability, robustness, and alignment with investment principles.
Energy Demand Forecasting using Quantum Machine Learning
Using Quantum Gaussian Regression and Quantum Reservoir Computing to improve energy supply and demand forecasting for electric grids. Accurate time-series forecasting is critical for modern energy systems, where nonlinear dynamics, multi-scale seasonality, and strong inter-variable dependencies challenge conventional machine learning approaches
Development of Advanced Quantum Algorithms
Verifying quantum speedup in coupled harmonic oscillator systems through practical implementations using Classiq; with concrete efforts on clarifying the resource requirements of the algorithm and provide concrete pathways toward practical quantum advantage.
Quantum Machine Learning for Defect Detection
Practical applications of Quantum Neural Networks for automated anomaly detection in aerospace manufacturing processes, validated on inspection and sensor data from synthetic and real-world aircraft production datasets.
Quantum Random Number Generator
Design, Simulations, and Analysis for a Photonics-based Quantum Random Number Generator (QRNG) Chip. Reliable randomness is a foundational requirement for modern cryptography, secure communications, and authentication systems.
Quantum Key Distribution for Satellite Communication
Formulating secure QKD protocols, and designing an optimal photonics-based QKD chip for satellites in space. Industry-standard chip design for space-based QKD communication. As space infrastructure becomes increasingly important for global communications and defense applications, ensuring the security of transmitted data is a critical challenge.
Quantum Computing for Warehouse Inventory Optimization
Developing quantum computing algorithms for warehouse inventory optimization, solving NP-Hard Integer-binary programming model with quantum annealing algorithms. Results show 66% faster order fulfillment than classical methods.
Quantum Computing for Chemical Simulations
Optimizing LCU Decompositions for NISQ quantum chemistry algorithms. Significant cost reductions up to 90% for simulating complex molecules on Quantum Computers, helping bridge the gap between theoretical algorithms and deployable quantum chemistry workflows.
Quantum Walks and Monte Carlo
Using quantum computing for nuclear radiation transport problem through Quantum Walks and Monte Carlo. Developing quantum circuits to simulate complex systems through a Galton Box-style Monte Carlo problem, an approach relevant to high-dimensional challenges like particle transport and quantum systems.
AI for Designing Radiation Resistant Alloys
AI for Designing Radiation Resistant Alloys. Building a pipeline to predict radiation resistant alloys due to high cost of experimental screening. Creating a light-weight AI model with an importance on frugal resources that can be easily modified to design materials for a variety of application parameters including target hardness, ductility etc.
Quantum Benchmarks of Majorana systems
Developing quantum computing benchmarks for electron tight-binding models of Majorana systems to enable utility-scale simulations. Initiative to establish industry-wide standardized benchmarks for quantum computing in chemistry and materials simulation.
Enhancing Corrosion Resistance of Aluminum Alloys Through AI and ML Modeling
Enhancing Corrosion Resistance of Aluminum Alloys Through AI and ML Modeling. Leveraging AI and machine learning models to enable the rapid screening of alloy compositions and processing conditions that enhance durability.
Distributed Quantum Computing
Optimizing Variational Quantum Algorithms (VQAs) through distributed quantum computing techniques, for executing large quantum workloads on current-generation hardware by distributing complex circuits into smaller, executable components.
Modeling and Simulation of Diamond NV Centers
Developing computational models to better understand the physical behavior and operational limits of Nitrogen-Vacancy Center (NV centers) in diamond. Large scale simulations on HPC systems to investigate the fault tolerance and environmental robustness of diamond NV sensors.
Quantum Benchmarking of LMG model
Developing large, utility-scale benchmarks for simulating the Lipkin-Meshkov-Glick model of a nucleus on quantum computers to evaluate their practical performance. Comparative industry-scale benchmarks for the largest experiments to date on Perlmutter supercomputers and IBM quantum computers.
Hardware Design for Portable Quantum Sensors
Design and development of miniaturized quantum sensing hardware tailored for nuclear engineering and monitoring applications. Low-cost and compact hardware architecture capable of supporting scalable manufacturing.
Quantum Computing for Engineering Simulations
The Poisson equation is a fundamental partial differential equation (PDE) that describes equilibrium distributions and appears in many engineering problems. It serves as a canonical "Hello world!" benchmark in computational engineering because of its ubiquity and foundational status in fields ranging from structural analysis to electromagnetics.
< our ecosystem >
Partners
Idaho National Laboratory
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IBM Quantum
Naval Nuclear Laboratory
Classiq
Xanadu
Vanguard
BQPhy
qBraid
E.ON
D-Wave
Pramatra Space
Idaho National Library
NERSC
University of Maryland QLAB
CQ Tech
Strangeworks
Forschungszentrum Jülich