Industries
Quantum software
for the computations
that matter
Noise still limits real quantum workloads, even as quantum systems advance. QESEM helps improve signal fidelity and validate larger computations across chemistry and pharma, materials and automotive, finance, government and national research, and HPC and national labs.
Chemistry & Pharma
Use quantum hardware to investigate molecular systems
Quantum chemistry workloads are highly sensitive to errors in state preparation, circuit execution, and measurement. On noisy hardware, these errors can obscure the properties researchers are trying to calculate.
QESEM enables teams to:
- Improve the accuracy of molecular-energy and expectation-value calculations
- Use hardware-aware execution for quantum chemistry circuits
- Manage the resource requirements of high-precision results
- Validate computations against exact or trusted reference results where available
- Build workflows that can evolve with improving quantum hardware
Featured work
Qedma is collaborating with the Technical University of Denmark and the University of Copenhagen on a quantum chemistry project supported by the… Eureka grant program. The work explores the potential energy surface of the water molecule using IBM quantum hardware and QESEM, while investigating the hardware capabilities required for quantum advantage in chemistry.
JSR researchers used QESEM to study point-group symmetries in many-electron molecular wavefunctions on IBM quantum hardware. In experiments using up to… 32 qubits, QESEM produced the closest agreement with exact results, with a maximum discrepancy of only a few percent.
Materials & Automotive
Quantum computation for complex materials and mobility problems
Materials and automotive organizations are exploring quantum methods for molecular simulation, materials discovery, optimization, and computational fluid dynamics. To make these efforts meaningful, teams need workflows that can operate on real hardware and produce results they can evaluate.
QESEM enables teams to:
- Execute larger quantum workloads on available processors
- Improve the signal fidelity of simulation and optimization outputs
- Apply hardware-specific noise analysis to the computation
- Integrate quantum execution into research and engineering workflows
- Assess the accuracy and correctness of results with quantitative metrics
Featured work
Qedma is working with Hyundai and academic collaborators on a proof of concept for quantum computational fluid dynamics. The project is developing… and benchmarking a variational quantum linear-solver workflow for standard CFD problems, with the goal of designing a realistic pipeline for near-term hardware using QESEM.
JSR’s work with QESEM applies quantum computing to many-electron molecular wavefunctions, connecting quantum error mitigation with materials… and chemistry research.
Finance
More reliable quantum workflows for finance
Financial institutions are exploring quantum methods for optimization, risk analysis, machine learning, and simulation. These workloads often require repeated execution, precise expectation values, and a clear understanding of uncertainty.
QESEM enables teams to:
- Execute larger or deeper circuits on current quantum hardware
- Control the tradeoff between result accuracy and QPU resources
- Improve the fidelity of outputs used in optimization and machine-learning workflows
- Track error bars, precision, and execution metrics
- Compare and validate results across supported hardware platforms
Potential applications include portfolio and risk optimization, quantum machine learning, and other hybrid quantum-classical workflows.
Government & National Research
Support for national-scale quantum research
Government research agencies and national research programs need transparent, reproducible, and technically defensible quantum results. Their teams may work across different hardware platforms, algorithms, and classical computing environments.
QESEM enables teams to:
- Analyze hardware-specific noise and error dynamics
- Execute and validate larger quantum computations
- Maintain consistency across different quantum platforms
- Quantify uncertainty and resource requirements
- Connect quantum processors with classical and HPC infrastructure
Featured work
DESY researchers, in collaboration with Qedma, developed and tested a method for extracting statistical properties from amplitude-encoded… velocity fields in fluid-dynamics simulations. Using QESEM on IBM’s ibm_fez processor, the researchers demonstrated that central moments and structure functions can be recovered on current noisy hardware.
DESY researchers have also used QESEM in work on quantum simulation of lattice quantum electrodynamics in 2+1 dimensions, with experiments conducted on real quantum hardware.
Qedma is collaborating with CTU on quantum phase-estimation research, including iterative and robust phase estimation, variational state preparation… , and more efficient decompositions of resource-intensive controlled operations. The work examines how QESEM can improve the reliability and practical performance of phase-estimation algorithms on noisy quantum hardware.
HPC & National Labs
Integrate quantum processors into serious computing infrastructure
Quantum processors are most useful when they work as part of a larger computational system. HPC centers and national labs need quantum workflows that fit their existing infrastructure, make resource requirements visible, and support repeatable experimentation.
QESEM enables teams to:
- Connect QPU execution with classical and HPC workflows
- Estimate QPU time and other resources before execution
- Run multi-observable and hybrid quantum-classical workloads
- Apply adaptive, hardware-aware error management
- Return results with uncertainty and execution metadata
- Build a consistent software layer across quantum platforms
Featured work
DESY’s fluid-dynamics work demonstrates how quantum hardware can be used within a broader scientific-computing workflow to recover statistical… properties of velocity fields using QESEM.
Qedma and LTM are collaborating on QuEST, the Quantum Error Mitigated Statistical Training Library for High Impact Applications, with support from… the India Israel Innovation Fund. The project is developing a modular quantum machine-learning toolkit with built-in error mitigation for more dependable performance on current quantum hardware.
Other industries
A software layer that keeps pace with your hardware
The computational questions differ by industry, but the technical requirements are shared: understand the noise, manage its effect on the output, execute at the required scale, and validate the result.
QESEM enables teams to:
- Application agnostic
- Hardware and platform agnostic
- Accessible through APIs
- Designed for cloud and on-premises environments
- Built to support current and future quantum architectures
Get more out of yourquantum hardware
Bring your workload, your target accuracy, and the QPU you want to use. Qedma can help you understand the noise that matters, improve signal fidelity, and validate the results.
