The Qatalyst Core

Quantum-ready, cloud-based complex optimizations

Qatalyst delivers state-of-the-art constrained optimization for both classic and quantum computers. Solve complex computational problems using quantum-ready techniques on classic systems to discover best-fit solutions to your most vexing constrained optimization problems.

Quantum-ready decisions, Right now.

Featuring state-of-the-art quantum-ready computational engines, the Core accelerates your time-to-results while delivering more and better solutions for your optimization problems.

The Core integrates specialized mathematical operations with complex constrained optimization techniques to deliver fast, excellent results.

It automatically transforms today’s real-world problems into quantum-ready requests and processes those requests on classical and/or quantum processors. No programming required.

You stay focused on solving your problems, not learning a complicated new programming paradigm and complex mathematical operations.

State-of-the-art computational optimization

Qatalyst features a variety of complex mathematical modules to prepare, optimize, iterate, and solve complex computations. For example:

LaGrange multipliers

Advanced mathematics compress and simplify the problem prior to QUBO application. The Core applies these advanced mathematic techniques, based on the type of problem and processing required.  

Once requests are submitted for processing, results are iteratively optimized using advanced mathematics as part of the quantum processing flow. When the best results are identified, the Core returns the results in the desired format.

Sampling Quadratic Unconstrained Binary Optimization Algorith (QUBO)

The Core automatically maps real-world problems into
physics-based problems in a multi-dimensional space that quantum computers require for processing. QUBOs represent the quantum problem, which is a result of the transformation of binary requests into the multi-dimensional space of quantum physics. QUBOs are the native format used by annealing quantum computers, such as D-Wave.

Qubit control for diverse QPU access

Qatalyst controls both the physical and logical qubits, maintaining strict chains of computation. These controls apply to three different computing paradigms; gate models, annealers, and classic computers. As a result, Qatalyst automates and can eliminate the hardware limitations often caused by physical to logical mapping of qubits. It also eliminates the need for low-level hardware programming and reprogramming, accelerating time to results while dramatically reducing overall application development and lifecycle costs.

Sampling QUBOs with the Quantum Approximate Optimization Algorithm (QAOA)

Qatalyst automatically applies the QAOA algorithm to previously defined QUBOs.

This algorithm optimizes problems for submission to gate model quantum computers, such as Rigetti, IBM, and IonQ.

Seamless optimization

Any problem request that your SME, application, or workflow defines can be processed on classic or quantum computers, seamlessly. No new programming, no coding for different hardware.

Qatalyst's powerful engines will submit problems to whichever processor, or processors, you define in the simple Q API call. Run on multiple systems to benchmark results, learn about differences in problem tuning for best results, and more.

Technical specifications


  • Uses familiar SME constructs to create problems and submit to Qatalyst
  •  Six API calls, three constrained-optimization and three graph-analytic, access all Qatalyst functions.
  • Users can redirect to classical, quantum, or hybrid solver by one parameter on each API call;  e.g., sampler='braket_ionq' for an IonQ QPU running in AWS’ Braket quantum cloud service.
  • Users with existing QUBO formulations can call the QUBO-tailored entry point in the API.
  • Results always come back in the same terms as input; i.e., a constrained-optimization problem returns objective-function values and solutions based on the input variables while graph-analytic functions return graph-relevant results such as partition assignments for partitioning.

Q Graph

  • Accepts graph models as problems for computational optimization.
  • Supports common compute-intense (NP-hard) NetworkX graph functions including Clique-Cover, Partitioning, and Community Detection.
  • Q Graph effectively samples near-optimal results from graphs up to 70,000 vertices.
  • The Q Detect community-detection capability implements a recent algorithm from Mniszewski et al. from Los Alamos National Lab.  In addition, Q Detect implements extensions for bipartite graphs, which consist of 2 types of vertices, such as patients and medical variables, and multiple techniques for projecting the accumulated behavior of a set of vertices (say, patients) back to a single patient.

The Qatalyst Core

  • Quantum-ready constrained optimization engine for both classic and quantum processing
  • Exploits concepts from algebraic topology to capture the essence of the input problem in a form that enables the QPU to contribute its unique characteristics to hybrid quantum/classical sampling.  
  • Leverages QUBO and modified VQE-based algorithms for quantum optimization and submission
  • Classical sampler uses raised temperature (conceptually, in the sense of simulated annealing) for global optimization and an advanced tabu solver for local optimization
  • Qatalyst Core has demonstrated strong performance on constrained-optimization problems up to 110,000 variables and 8,000 constraints.


  • Microservices manage request flow to and from CPU or QPUs, tailored to the specific Qatalyst entry point called, relieving app developers from this tedious low-level task
  • Portal offers easy access to all admin and user functions. Can be integrated with current management systems.

Learn What Quantum Can Do For You

Explore our QikStart program, including access to Qatalyst, Quantum experts and resources to create your first quantum-assisted results. All on us.

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