Advancing Quantum Frontiers: Solutions for Tomorrow’s Challenges
We are company focused on building software platforms to enable users to take advantage of the rapidly expanding capabilities of quantum hardware.
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Quantum Machine Learning Platform
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Quantum Machine Learning Platform
Let’s Build Quantum Foundation Models
Broadly applicable models that can be adapted to various tasks trained on domain specific datasets
Parameters of models trained today can be used to initialize parameters of larger future models
Parameter transfer enables value creation on quantum computers starting today
Quantum Machine Learning Platform
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Build generative and predictive models on real datasets
Benchmark models, estimate resources, and project performance vs. classical
Optimize model implementations for execution on current hardware with 50–100 qubits
Research on abstractions
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Research Themes
How many qubits does a machine learning problem require?
What if you could examine a dataset and quickly calculate how many qubits you’d need to model it on a quantum computer? For quantum machine learning, that kind of clarity has been missing. And as a result, researchers and companies have been left guessing about when...
A Hierarchy of Contexts
In this post, we describe an important aspect of constructing and compiling complex quantum operators—specifically, how large operators built from many smaller pieces can be represented and resolved. Let’s say we want to create a unitary operator acting on a large...
Quantum Software for Accelerating Application Design
In this post, we'll walk through our architectural approach — a structure designed to make quantum computing more accessible, modular, and scalable. Building for the Quantum User At the top of the stack, we find the hardware-independent layers. These are the tools and...
Founder Profile
Dr. Sonika Johri is Founder & CEO of Coherent Computing, a company focused on building software platforms to enable users to take advantage of the rapidly expanding capabilities of quantum hardware. As a Principal Researcher at IonQ, she led a team working on quantum applications R&D for clients including Hyundai, Fidelity Center for Applied Technology, GE Research, Goldman Sachs, Airbus, and Thompson Machinery.
She has authored 35+ publications, recently focusing on cutting‐edge quantum algorithm demonstrations in the areas of generative and discriminative machine learning, quantum chemistry, optimization, and condensed matter physics across a variety of quantum hardware platforms, as well as developing frameworks for quantum benchmarking.
In the quantum arena, principal interests are algorithms and software, resource estimation, applications roadmaps, projection and analysis of quantum advantage, hiring and training quantum teams.
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