Iskay Quantum Optimizer
A QUBO and HUBO solver by Kipu Quantum that can outperform classical solvers. It runs on IBM Quantum hardware and takes only your objective function as input.
Drop it into your Qiskit workflow
Iskay is a first-class Qiskit Function on IBM Quantum, catalogued as kipu-quantum/iskay-quantum-optimizer. Load it, pass your objective, read the solution. Pre- and post-processing are managed.
from qiskit_ibm_catalog import QiskitFunctionsCatalog # pip install qiskit-ibm-catalog
catalog = QiskitFunctionsCatalog(
token=IBM_TOKEN, channel="ibm_quantum_platform", instance=INSTANCE_CRN,
)
iskay = catalog.load("kipu-quantum/iskay-quantum-optimizer")
# Objective as a QUBO/HUBO. Higher-order terms are native.
hubo = {
"()": 0.0, # constant
"(0,)": -1.0, # linear
"(1,)": -1.0,
"(0, 1)": 2.0, # quadratic (QUBO)
"(0, 1, 2)": -1.5, # cubic (HUBO), no extra qubits
}
job = iskay.run(
problem=hubo,
problem_type="binary",
instance=INSTANCE_CRN,
backend_name="ibm_fez",
options={"shots": 100, "num_iterations": 3},
)
result = job.result()
print(result["solution_info"]["cost"], result["solution"])Join the Iskay Community
Already running Iskay as a Qiskit Function? Connect directly with Kipu’s engineering team and fellow practitioners solving real problems on IBM quantum computers.
Launching soon. Free for Iskay users; founding membership pending interest.
Developer check-ins
Dedicated time for problem encoding, shot budgets and tuning.
Early access
Preview features before they reach the Qiskit Functions Catalog.
Best practices
HUBO/QUBO formulation patterns and benchmarks that work at scale.
Shape the roadmap
A direct channel that puts your requests at the front of the queue.
What users have to say
At the IBM Quantum Developer Conference
“Kipu's Qiskit Function radically shortened the path from QUBO/HUBO formulation to execution on real 156-qubit hardware, freeing me to focus on tuning the quantum algorithm itself.”
Khanyisa Noganta
Principal Data Scientist · Market Split winner
“Khanyisa won by tuning the parameters. It's proof that Kipu's Iskay Qiskit function works in practice. The new features allowed participants and other users to cater the function to their needs.”
Junye Huang
IBM Quantum · ran the QDC25 challenge
In the field
“The results are excellent: a perfect 222-edge cut at 100 nodes, and 318 of 336 at 150.”
Quantum team
Financial services · Max-Cut benchmark
“The function has been working well. We're scaling from 20 to 150 qubits on S&P 500 data.”
Data-science team
Global IT consultancy · portfolio optimization
“I tested a larger problem and it works. Now I'm pushing to even larger instances.”
Optimization researcher
University research group · vehicle routing
Tune it to your needs
“With the recommended settings, our circuit dropped from 1901 to 1041 layers. Very useful.”
Quantum researcher
University research group · biomedical HUBO
At the Berlin Quantum Hackathon
“We're probing quantum utility by extracting combinatorially hard scheduling subproblems and encoding them to fit NISQ-sized QPUs while keeping circuits as shallow as possible. With Kipu's proven Iskay Optimizer, we can study solution-quality distributions in a hardware-realistic setting.”
The Depth Compressors
P. Kulkarni & A. Atre · BVG Optimization Challenge
“BVG's use case represents a highly complex combinatorial optimization challenge. It was inspiring to see the energy it generated across the teams, combining advanced classical and quantum approaches.”
Alejandro Gomez Cadavid
Quantum Optimization Lead, Kipu Quantum
Performant now. Better later.
Iskay matches the best classical solvers on real IBM hardware today, and its advantage compounds as problems scale beyond what classical and QUBO methods can reach.
Qubits required to encode a drone vehicle-routing problem, log scale. Lower is better.
| Problem size (delivery sites) | QUBO qubits (QAOA, others) | HUBO qubits (native, Kipu) |
|---|---|---|
| 10 sites, 2 drones | 55 | 20 |
| 50 sites, 5 drones | 77K | 250 |
| 100 sites, 10 drones | 5M | 1K |
| 500 sites, 20 drones | 625M | 5K |
~300x
fewer qubits at 50 sites 5 drones
~5,000x
fewer qubits at 100 sites 10 drones
~125,000x
fewer qubits at 500 sites 20 drones
In the most comprehensive benchmark to date, a single IBM Heron r3 QPU matched classical solvers running on 128 vCPUs or 8 NVIDIA A100 GPUs, reaching ground-state solutions in 14 of 20 instances in under a second. A collaboration with IBM and the Zuse Institute Berlin.
arXiv 2603.13607The runtime edge becomes more pronounced as system size grows (arXiv 2505.08663). This sparse cubic VRP is expected to surpass classical runtime near 275 HUBO qubits (roughly 50 delivery sites with a 5-drone fleet), denser problems near 85, and richer constraints such as time windows lower it to 50–60 (Kipu Complexity Index). See the demo.
The research behind it
Peer-reviewed and preprint work laying the groundwork for Kipu’s quantum optimization.
Questions, answered
What algorithm powers Iskay?
Bias-field digitized counterdiabatic quantum optimization (BF-DCQO). It is non-variational, so it runs as a purely iterative loop with no classical parameter training to get stuck in. Counterdiabatic protocols compress circuit depth to fit today's gate-based devices, and each round updates bias fields from the lowest-energy samples to steer the next evolution toward better solutions.
What problems can Iskay solve?
Any unconstrained binary optimization problem, both QUBO (quadratic) and HUBO (higher-order), with higher-order terms run natively. Demonstrated applications include portfolio selection in finance on a 250-asset S&P 500 universe, optical-fibre routing and wavelength assignment in telecommunications validated in a live operator pilot, supply chain, fleet routing and job-shop scheduling in logistics, and protein folding and dense spin-glass models in materials and life sciences.
Why solve HUBO natively instead of converting to QUBO?
Forcing a higher-order problem into a quadratic QUBO form requires auxiliary qubits, and the qubit count explodes as the problem grows. A 100-site routing problem needs roughly five million QUBO qubits but only about a thousand native HUBO qubits. Iskay encodes HUBO directly, so real problems stay within today's hardware budget and the advantage compounds with problem size.
What hardware does Iskay run on?
Iskay runs on IBM Quantum hardware through the Qiskit Functions Catalog, catalogued as kipu-quantum/iskay-quantum-optimizer. The same BF-DCQO engine is available as Miray on the Kipu Quantum Hub, where it also runs on IonQ, Rigetti, QuEra and more.
How do I get started?
Load Iskay from the IBM Qiskit Functions Catalog and pass your objective function, or run Miray on the Kipu Quantum Hub. Pre- and post-processing are managed, so you only provide the problem. See the documentation for setup and the Hub pricing for plans.