Quantum Software Engineering · New Mexico, USA

Quantum software
engineered at the
physics layer.

Gate-level algorithm design and hardware-aware compilation for IBM Quantum, IonQ, and Quantinuum processors. Delivered by researchers who published the underlying physics.

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59⁺ Peer-reviewed publications

350⁺ Academic citations

5 Countries of research

The company

Deep physics.
Production software.

Quantum Hardware Software Systems builds quantum software directly against the hardware — gate-level algorithms, hardware-native compilation, and hybrid classical-quantum architectures running on real QPUs.

Founded by a research physicist with 59 peer-reviewed publications and a decade of work on superconducting qubits, Josephson junctions, and semiconductor quantum dot devices. The same expertise that produced the papers produces the code.

US-registered LLC, operating across North America, Europe, and Asia-Pacific.

LLC · New Mexico, USA NISQ-era specialists Global delivery B2B enterprise

Dr. Krzysztof Pomorski

Co-founder & CEO

Quantum physicist. Research spans superconducting qubits, Josephson junctions, and semiconductor quantum dots. Fellow at University of New Mexico. 59 publications, 350+ citations.

Superconducting qubits Josephson junctions Quantum AI

Sebastian Bochenek

Co-founder & Business Development

Software entrepreneur and business builder. Leads strategy, enterprise partnerships, and go-to-market across the US and Europe.

Enterprise software Go-to-market B2B

Services

Six disciplines.
One team.

From qubit-level algorithm design through production integration. Each engagement is scoped to the problem — not a service catalog.

Quantum Algorithm Design

Custom quantum algorithms for your specific computational problem — optimization, simulation, sampling, or search. Designed for NISQ hardware today and fault-tolerant architectures tomorrow.

Hybrid Classical-Quantum Systems

Architecture and delivery of systems that combine classical HPC with quantum processing units. Real quantum advantage, integrated into your existing infrastructure.

Circuit Optimization

Depth reduction, gate decomposition, and noise mitigation tailored to your target QPU. Compatible with IBM Quantum, IonQ, Quantinuum, AWS Braket, and Azure Quantum.

Device Simulation & Modeling

Physics-accurate quantum device simulation — Josephson junction modeling, semiconductor quantum dot dynamics, and quantum state evolution on classical hardware.

Quantum Machine Learning

QML model design and integration into your AI stack. Variational quantum classifiers, quantum neural networks, and quantum-enhanced feature spaces — built to bridge physics and production ML.

R&D Consulting

Strategic guidance for corporate quantum programs. Technology roadmapping, hardware vendor evaluation, team upskilling, and facilitated research partnerships with academic institutions.

Engineering approach

From first principles
to working code.

Gate-level precision

We work at the gate level — decomposing algorithms into hardware-native gate sets while accounting for T1/T2 times, crosstalk, and measurement error rates on your target QPU.

Hardware-aware compilation

Circuit compilation and transpilation pipelines built specifically for the hardware you're running on — not generic transpilers optimized for demo circuits.

Rigorous error mitigation

Zero-noise extrapolation, probabilistic error cancellation, and symmetry verification. We squeeze the most signal out of NISQ devices before fault-tolerance arrives.

Open standards, auditable code

Everything is built on Qiskit, PennyLane, Cirq, and OpenQASM. You own the code, you can audit it, and it stays portable across hardware generations.

vqe_h2_molecule.py

# VQE ground state estimation for H₂
# hardware-efficient ansatz, error mitigated

from qiskit import QuantumCircuit
from qiskit.primitives import Estimator
from qiskit_algorithms import VQE

# Hardware-efficient ansatz
qc = QuantumCircuit(4)
qc.h([0, 1])
qc.cx(0, 1)
qc.ry(θ[0], 2)
qc.cx(2, 3)
qc.rz(θ[1], 3)

# Minimize ⟨ψ(θ)|H|ψ(θ)⟩
solver = VQE(
  ansatz=qc,
  optimizer=COBYLA(maxiter=500),
  estimator=Estimator()
)

result = solver.compute_minimum_eigenvalue(H)
# E₀ = −1.1372 Ha  ✓  (Δ < 1 mHa)
        

Scientific foundation

59 publications.
One engineering firm.

59⁺

Peer-reviewed publications

350⁺

Academic citations

Countries of research activity

NISQ

Hardware-first approach

Dr. Pomorski's research spans superconducting qubits, semiconductor quantum dot devices, Josephson junction physics, and quantum information theory — published across institutions in the US, Poland, Ireland, and Japan.

59 peer-reviewed publications and 350+ citations are not marketing figures. They are the reason the software works correctly on real hardware. The person writing your algorithm is the same person who published the underlying physics.

View research profile

Josephson junctions Semiconductor nanowires Quantum dots Quantum information Quaternionic QM Classical-quantum hybrid

From quantum hardware to quantum AI K. Pomorski · ResearchGate, 2019

Room-temperature emulation of single-electron devices via classical analog electronics K. Pomorski · Semiconductor quantum devices

Quaternionic description of semiconductor quantum dots-based electronics K. Pomorski · Quantum information theory

Josephson energy quantization and validity of quantization approaches K. Pomorski · Superconducting circuits

Industries

One platform.
Every sector.

The physics of optimization, simulation, and sampling is the same across industries. The application layer is where the specificity lives.

Pharmaceuticals

Molecular simulation, protein folding, drug discovery

Finance

Portfolio optimization, Monte Carlo acceleration, risk modeling

Defense & Aerospace

Quantum sensing, secure communications, GPS-independent navigation

Energy & Materials

Materials discovery, grid optimization, catalysis simulation

Logistics

Combinatorial optimization, routing, supply chain scheduling

AI & Machine Learning

Quantum-enhanced models, QML pipelines, variational inference

Research Institutions

Algorithm R&D tooling, simulation infrastructure, academic collaboration

Technology

Quantum-ready software architectures, SDK integration, QPU access

Engagement model

Structured.
Milestone-driven.

Problem mapping

We identify which computational problems map to quantum-tractable problem classes, assess hardware feasibility on your target QPU, and give you a direct technical scope — not a slide deck.

Algorithm design

Research-backed algorithm selection and circuit design. Hardware-agnostic first, then optimized for your target device — with complexity analysis and expected speedup estimates.

Prototype & validate

Classical simulation followed by execution on real QPU hardware. Deliverables include benchmarking reports, noise characterization, and fidelity measurements against theoretical bounds.

Integrate & maintain

Production integration with API wrappers, CI/CD pipelines, and monitoring dashboards. We stay on for performance tracking and can upgrade algorithms as better hardware becomes available.

Get in touch

Describe the problem.
We'll scope the solution.

We respond with a direct technical assessment — what quantum can and cannot do for your specific use case, and what a realistic engagement looks like.

contact@qhssystems.com LinkedIn ResearchGate

Quantum Hardware Software Systems LLC · Entity #0008079083 · New Mexico, USA

Quantum software engineered at the physics layer.

Deep physics.Production software.

Six disciplines.One team.

From first principlesto working code.