nzflow-qubo

 

Status: Complete as of May 2026

My collaborator A. Lotfi maintains a public GitHub repository containing scripts supporting the paper "A QUBO formulation for nowhere-zero k-flows", which I co-authored with A. Lotfi, A. Carter, M. Meysami, T. Ha, K. Nketia, and S. Shirtliffe. For any loopless multigraph, the code builds a quadratic Hamiltonian over binary variables whose ground-state energy vanishes if and only if the graph admits a nowhere-zero k-flow, in line with Tutte's Equivalence Theorem and Tutte's 5-Flow Conjecture.  The script can be executed on D-Wave's Ocean SDK. The code was written by A. Lotfi. 

The repository is here:

Tags:

  • Nowhere-zero k-flow
  • Solver witness
  • Quadratic unconstrained binary optimization
  • Quadratic Hamiltonian
  • Quantum annealing
  • Classical annealing
  • Simulated annealing
  • One-hot encoding
  • Tutte equivalence
  • Tutte 5-flow conjecture

 

 

QSVMDocking

 

Status: Complete as of Apr 2026

I maintain a public GitHub repository containing the supporting Jupyter notebook and (encoded) data for the paper "Quantum kernel estimation in SVMs for the docking of 9-mer peptides to immune receptors", which I co-authored with L. González Dominguez, Z. Khatooni, H. Wilson, and G. Broderick. The code implements three distinct quantum-enhanced support vector machines (QSVMs) to classify 9-mer peptide epitopes of the Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) into binding affinities categorized as strong or weak based on docking energy profiles for the SLA-1*04:01:01 immune receptor. The code facilitates benchmarking of the three schemes against one another and relative to a classical SVM. The code was written by L. González Dominguez.  The data was supplied by the Vaccine and Infectious Disease Organization (VIDO) at the University of Saskatchewan and is presented in a flattened / encoded form.

The repository is here:

Tags:

  • Quantum machine learning
  • Quantum support vector machine
  • Quantum kernel estimation
  • Quantum variational circuit
  • Peptide docking
  • Immune receptor
  • Epitope identification
  • Binding affinity
  • Vaccine discovery
  • Porcine Reproductive and Respiratory Syndrome Virus (PRRSV)

 

 

HQECC-Threshold

 

Status: Complete as of Mar 2026

My PhD student A. Mahmoud maintains public Zenodo and GitHub repositories containing the supporting Python code for the paper "Systematic approach to hyperbolic quantum error correction codes", which I co-authored with A. Mahmoud and K. Ali. The code applies random Pauli Z errors to physical qubits arranged according to a hyperbolic lattice based on an initial error probability. Subsequently, it initiates error-correcting paths as per the Hyperbolic Cycle Basis algorithm proposed in the paper and then determines whether they locate a logical error. Such errors are incremented accordingly and the logical error probability is updated after a fixed number of trials.  The procedure accommodates varying numbers of Bravais faces. The code was written by A. Mahmoud and K. Ali.

The repositories are here:

Tags:

  • Hyperbolic lattice
  • Quantum computing
  • Quantum error correction
  • Surface code
  • Quantum circuit
  • Simulated error
  • Error correction rate
  • Error correction threshold
  • Logical error probability
  • Fault-tolerant computing

 

 

ShardQ

 

Status: Complete as of Jan 2026

My collaborator Z. Guo maintains a public GitHub repository containing the package of routines announced in the paper "ShardQ: quantum data encoding via circuit partitioning and recomposition", which I co-authored with Z. Guo, J. Balewski, K. Xiao, and Z. Pan. The package comprises a scalable, hardware-aware circuit knitting framework designed for quantum data encoding circuits. It integrates cut selection (SparseCut), approximate compilation, and global reconstruction into a uniform pipeline optimized for NISQ-era superconducting quantum processors at widths beyond 100 physical qubits.  In particular, it can make cut-selection decisions based upon actual calibration data (gate error rates and T1/T2 times).  The Python scripts interface with Qiskit and the Qiskit-Aer simulator. The code was written by Z. Guo. 

The repository is here:

Tags:

  • Quantum data encoding
  • Quantum circuit simulation
  • Quantum circuit cutting
  • Quantum circuit knitting
  • Hardware error rates
  • Approximate compilation
  • Optimization of superconducting quantum processors

 

 

Triple Reduced Product


Status: Complete as of Nov 2016

I maintain a public GitHub repository containing the supporting Mathematica code for the paper "The triple reduced product and Hamiltonian flows", which I co-authored with L. Jeffrey, G. Seal, P. Selick, and J. Weitsman. The code carries out numerical calculations related to the symplectic structure of the triple reduced product and the average Gelfand-Cetlin function. The code was written by P. Selick and then improved and documented by J. Bettencourt.

The repository is here:

Tags:

  • Triple reduced product
  • Average Gelfand-Cetlin function
  • Symplectic geometry
  • Symplectic quotient
  • Coadjoint orbit
  • Moment map
  • Hamiltonian flow
  • Integrable system