Solve binary optimization problems with QPUs
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"])Used and published on real quantum hardware
“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
The Quest for Quantum Advantage in Combinatorial Optimization: End-to-end Benchmarking of Quantum Solvers vs. Multi-core Classical Solvers
Read the paper
“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
Quantum Optimization Benchmark Library - The Intractable Decathlon
Read the paper
“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
Bias-Field Digitized Counterdiabatic Quantum Algorithm for Higher-Order Binary Optimization
Read the paper
“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
Large-scale portfolio optimization on a trapped-ion quantum computer
Read the paper
“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
Protein folding on a 64-qubit trapped-ion hardware via counterdiabatic quantum optimization
Read the paper
“I tested a larger problem and it works. Now I'm pushing to even larger instances.”
Optimization researcher
University research group · vehicle routing
Quantum Feature Selection with Higher-Order Binary Optimization on Trapped-Ion Hardware
Read the paper
“With the recommended settings, our circuit dropped from 1901 to 1041 layers and 11,919 to 5,112 gates. Very useful.”
Quantum researcher
University research group · biomedical HUBO
Quantum Feature Selection for Biomedical Data Analysis
Read the paper
Scaling advantage with quantum-enhanced memetic tabu search for LABS
Read the paper
Protein folding with an all-to-all trapped-ion quantum computer
Read the paper
Runtime Quantum Advantage with Digital Quantum Optimization
Read the paper
Constant Depth Digital-Analog Counterdiabatic Quantum Computing
Read the paper
Quantum Combinatorial Reasoning for Large Language Models
Read the paper
Hybrid Sequential Quantum Computing
Read the paper
Sequential Quantum Computing
Read the paper
Branch-and-bound digitized counterdiabatic quantum optimization
Read the paper
Efficient DCQO Algorithm within the Impulse Regime for Portfolio Optimization
Read the paper
Digitized-Counterdiabatic Quantum Optimization
Read the paper
Digitized-counterdiabatic quantum approximate optimization algorithm
Read the paper
“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
Quantum Optimization Benchmark Library - The Intractable Decathlon
Read the paper
“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
Large-scale portfolio optimization on a trapped-ion quantum computer
Read the paper
“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
Quantum Feature Selection with Higher-Order Binary Optimization on Trapped-Ion Hardware
Read the paper
“With the recommended settings, our circuit dropped from 1901 to 1041 layers and 11,919 to 5,112 gates. Very useful.”
Quantum researcher
University research group · biomedical HUBO
Scaling advantage with quantum-enhanced memetic tabu search for LABS
Read the paper
Runtime Quantum Advantage with Digital Quantum Optimization
Read the paper
Quantum Combinatorial Reasoning for Large Language Models
Read the paper
Sequential Quantum Computing
Read the paper
Efficient DCQO Algorithm within the Impulse Regime for Portfolio Optimization
Read the paper
Digitized-counterdiabatic quantum approximate optimization algorithm
Read the paper
The Quest for Quantum Advantage in Combinatorial Optimization: End-to-end Benchmarking of Quantum Solvers vs. Multi-core Classical Solvers
Read the paper
“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
Bias-Field Digitized Counterdiabatic Quantum Algorithm for Higher-Order Binary Optimization
Read the paper
“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
Protein folding on a 64-qubit trapped-ion hardware via counterdiabatic quantum optimization
Read the paper
“I tested a larger problem and it works. Now I'm pushing to even larger instances.”
Optimization researcher
University research group · vehicle routing
Quantum Feature Selection for Biomedical Data Analysis
Read the paper
Protein folding with an all-to-all trapped-ion quantum computer
Read the paper
Constant Depth Digital-Analog Counterdiabatic Quantum Computing
Read the paper
Hybrid Sequential Quantum Computing
Read the paper
Branch-and-bound digitized counterdiabatic quantum optimization
Read the paper
Digitized-Counterdiabatic Quantum Optimization
Read the paper
Quantum Optimization Benchmark Library - The Intractable Decathlon
Read the paper
Runtime Quantum Advantage with Digital Quantum Optimization
Read the paper
“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
“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
Protein folding on a 64-qubit trapped-ion hardware via counterdiabatic quantum optimization
Read the paper
Constant Depth Digital-Analog Counterdiabatic Quantum Computing
Read the paper
Branch-and-bound digitized counterdiabatic quantum optimization
Read the paper
Efficient DCQO Algorithm within the Impulse Regime for Portfolio Optimization
Read the paper
The Quest for Quantum Advantage in Combinatorial Optimization: End-to-end Benchmarking of Quantum Solvers vs. Multi-core Classical Solvers
Read the paper
Scaling advantage with quantum-enhanced memetic tabu search for LABS
Read the paper
“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
“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
“With the recommended settings, our circuit dropped from 1901 to 1041 layers and 11,919 to 5,112 gates. Very useful.”
Quantum researcher
University research group · biomedical HUBO
Protein folding with an all-to-all trapped-ion quantum computer
Read the paper
Hybrid Sequential Quantum Computing
Read the paper
Sequential Quantum Computing
Read the paper
Digitized-counterdiabatic quantum approximate optimization algorithm
Read the paper
Bias-Field Digitized Counterdiabatic Quantum Algorithm for Higher-Order Binary Optimization
Read the paper
Large-scale portfolio optimization on a trapped-ion quantum computer
Read the paper
“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
“I tested a larger problem and it works. Now I'm pushing to even larger instances.”
Optimization researcher
University research group · vehicle routing
Quantum Feature Selection with Higher-Order Binary Optimization on Trapped-Ion Hardware
Read the paper
Quantum Feature Selection for Biomedical Data Analysis
Read the paper
Quantum Combinatorial Reasoning for Large Language Models
Read the paper
Digitized-Counterdiabatic Quantum Optimization
Read the paper