Your Gateway to the Quantum Age
Pioneering high-performance and user-friendly software platforms that unlock the full potential of quantum hardware.
What we do
Offerings
Benchmarking Quantum Computers
Evaluate performance with tailored or open-source application-oriented benchmarks designed to reflect real-world quantum workloads.
Integration of Quantum Applications with Hardware
Maximize your application’s performance through our custom code compilation services
Quantum Machine Learning Platform
Blog post explaining the paper
Alpha access to platform: Contact us
Papers
Quantum Machine Learning Platform
Quantum Foundation Models for your Data
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
Our platform enables value creation on quantum computers starting today
Quantum Machine Learning Platform
Engage with us
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
Research on abstractions
Posts
Research Themes
Quantum Programming Abstractions
What does it mean to program a quantum computer? Why are we still stuck specifying individual qubit gates by hand? And what would a true quantum programming language look like?
Right now, programming quantum computers is done through quantum circuits—specifying how qubits rotate on their Bloch spheres or how they become entangled with one another. This is an extremely low-level approach, comparable to programming a classical computer by adjusting the voltages across its transistors. Yet, this is the dominant paradigm adopted by widely used quantum software frameworks such as Qiskit, Cirq, CUDA-Q, and PennyLane.
These frameworks sometimes include libraries of reusable circuits, which can make things more manageable, but this is still far from true quantum abstraction. Other approaches attempt to wrap quantum operations in classical compilation infrastructures like LLVM. While useful, these are not quantum-native models of computation—they carry over classical assumptions that are often not very relevant to quantum computing.
The push for higher-level abstractions isn’t just theoretical—it’s driven by pressing practical needs. As qubit counts grow, designing and optimizing quantum circuits by hand quickly becomes infeasible. Add in the complexity of error correction and fault tolerance, and manual circuit construction becomes virtually impossible. And if the only way to express a quantum algorithm is through low-level circuit details, reasoning about algorithms—let alone building robust applications—remains out of reach for most developers.
Clearly, we need a higher-level programming model for quantum computing. But because the field is so new, it’s not yet obvious what form that model should take. What seems likely is that it won’t look much like classical abstractions. We believe that such a model will emerge gradually, shaped by advancing hardware, growing algorithmic sophistication, and repeated patterns in how people interact with quantum systems.
This blog series chronicles our attempt to build just such a high-level quantum programming language—one born from the real, day-to-day challenges of developing a usable quantum computing stack. Our goal is to create something intuitive, powerful, and compatible with a range of underlying qubit technologies. Join us on the journey.
Full Stack Quantum Software
What will it take to make quantum computing truly usable? How can we connect high-level application needs with the gritty realities of quantum hardware?
At Coherent Computing, we believe the answer lies in a layered approach to quantum software — one that bridges abstraction and implementation seamlessly.
Follow us in this blog series as we set out to build a software stack that enables all the stakeholders in the quantum ecosystem — from end users and physicists to software engineers — to collaborate with maximum impact, and remains.
Building Quantum Intelligence
What will it mean to create a new kind of intelligence – one that relies on quantum superposition and entanglement – which can discover patterns beyond the reach of classical logic?
What unique approach will an intelligent quantum system take to problem-solving? Which currently intractable challenges in science, technology, and human society might suddenly become accessible?
How will we harness these systems to realize quantum advantage in practical applications?
Follow us in this blog series as we set out to build quantum intelligence — we will discuss its theoretical foundations, its extraordinary potential, and the sometimes winding road that leads from today’s quantum devices to tomorrow’s quantum AI.
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 ranging from startups to Fortune 500 companies.
She has authored 40+ 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.
She has a PhD in theoretical condensed matter physics from Princeton University.
Videos
Appearances at Q2B
Lectures
