Bridging the gap between classical and quantum computing
We accelerate the shift to quantum with physics-inspired and hybrid QUBO solvers—powering next-level optimization, today.
Instance Size
Instance Destiny
Hybrid Approach
Hardware Agnostic
veloxQ
VeloxQ helps enterprises solve complex optimization challenges such as scheduling, logistics, resource allocation, and portfolio decisions. It uses a physics-inspired approach that runs on today’s conventional hardware. This enables organizations to capture quantum-readiness now, without waiting for advanced quantum infrastructure, while staying ready for future hybrid quantum-classical workflows.
⇒ Why VeloxQ?
- Business-ready today: Runs on conventional hardware for immediate deployment.
- Built for scale: Handles large, real-world optimization problems efficiently.
- Future-ready: Designed to support hybrid quantum-classical optimization strategies.
- High performance: Combines strong solution quality with fast execution.
⇒ Technical Capabilities
VeloxQ is built to solve Quadratic Unconstrained Binary Optimization (QUBO) problems at scale, supporting complex variable interactions and large problem instances while remaining topology-agnostic so it’s eliminating the need for hardware-specific graph embedding workflows.
- Scalable QUBO solving for large and complex optimization models
- Topology-agnostic execution with no hardware-specific embedding requirements
- Fast optimization runtime for production-oriented workflows
- Strong solution quality across benchmarked problem classes
- Pipeline compatibility for use as a standalone solver or in heterogeneous architectures
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Logistics
Optimize routing, scheduling, and supply chain decisions with quantum-ready workflows running on today’s infrastructure.
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Finance
Improve portfolio and risk-constrained optimization with a quantum-ready QUBO approach deployable on conventional hardware.
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Drug Discovery
Accelerate drug discovery decisions with QUBO-based optimization across complex R&D workflows on current infrastructure.
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Energy
Optimize grid, storage, and asset planning with quantum-ready QUBO workflows built for today’s energy operations.
Ready to Use Across All Sectors
Whether you’re optimizing job schedules, discovering breakthrough drugs, or managing financial portfolios, our physics-inspired QUBO solvers are designed to empower your innovation.
VeloxQ can support optimization-driven workflows across virtually any industry where decisions must be made under constraints, from manufacturing and logistics to finance, energy, retail, healthcare, telecom, and public-sector operations.
By solving QUBO-formulated problems on conventional hardware, VeloxQ enables organizations to apply advanced optimization methods today (without waiting for specialized quantum infrastructure), while maintaining a future-ready path aligned with hybrid quantum-classical strategies.
In supply chain and logistics, VeloxQ can help improve routing, fleet utilization, warehouse task assignment, and distribution planning by identifying better trade-offs between cost, time, and capacity constraints.
In manufacturing and industrial operations, it can support production scheduling, machine allocation, maintenance planning, and workforce assignment to increase throughput, reduce downtime, and improve resource efficiency.
In finance and insurance, VeloxQ can be used for portfolio construction, risk-aware allocation, and scenario-based decision optimization where many binary choices interact in complex ways.
In energy and utilities, it can assist with grid-related planning, asset scheduling, load-balancing decisions, and infrastructure optimization where scale and interdependency make traditional approaches difficult to maintain.
In retail and commercial operations, VeloxQ can help with assortment decisions, promotion planning, inventory positioning, and store or fulfillment optimization to improve service levels while controlling operational costs.
In healthcare and life sciences operations, it can support scheduling, capacity planning, and resource coordination across facilities, staff, and equipment to improve operational performance in complex environments.
Across all sectors, VeloxQ is positioned as a practical optimization engine: topology-agnostic, scalable, and deployable on current infrastructure, bringing many of the workflow benefits associated with quantum annealing-style optimization approaches into an enterprise-ready solution available now.
This makes VeloxQ a strong fit for organizations seeking quantum-readiness from advanced physics-inspired optimization while building long-term optionality for future quantum-integrated workflows.
References
Logistics
Optimize routing, scheduling, and supply chain decisions with quantum-ready workflows running on today’s infrastructure.
VeloxQ can support quantum-ready logistics optimization workflows across routing, dispatching, load planning, scheduling, resource allocation, and network design using the same QUBO modeling layer commonly associated with quantum annealing programs, but running on conventional hardware today.
For logistics teams, this means you can start building optimization pipelines now (problem formulation, scenario testing, pilot deployment, and production integration) while preserving a future path to hybrid quantum-classical workflows as hardware matures.
VeloxQ is especially well suited to heterogeneous logistics environments, where multiple decision layers must work together such as fleet routing, delivery sequencing, warehouse task coordination, capacity balancing, and shift or asset scheduling, because QUBO-based optimization can be used as a common decision engine across these interconnected workflow types.
This aligns with how quantum logistics adoption is evolving in practice: many published and commercial efforts focus on combinatorial optimization use cases (for example vehicle routing and scheduling) and frequently rely on hybrid approaches rather than fully quantum end-to-end execution.
Compared with quantum annealing deployments that may require hardware-specific embedding and topology constraints, VeloxQ is presented as topology-agnostic for QUBO problem structures, which can simplify experimentation and accelerate rollout across different logistics use cases and business units.
In short, VeloxQ offers a practical “deploy now, stay quantum-ready” path for logistics organizations that want to standardize optimization workflows across heterogeneous operations today while maintaining compatibility with tomorrow’s hybrid quantum optimization stack.
References
Finance
Improve portfolio and risk-constrained optimization with a quantum-ready QUBO approach deployable on conventional hardware.
VeloxQ can support quantum-ready financial optimization workflows across portfolio construction, rebalancing, capital allocation, risk-aware asset selection, treasury and liquidity decisions, and other constraint-heavy optimization tasks—using a QUBO modeling approach that is widely used in quantum optimization research and annealing-based finance workflows.
For financial institutions, this creates a practical path to build optimization pipelines now (problem formulation, policy and constraint encoding, scenario analysis, pilot deployment, and production integration) on conventional hardware, while preserving compatibility with future hybrid quantum-classical architectures as quantum systems mature.
VeloxQ is especially well suited to heterogeneous finance workflows, where multiple decision layers must work together—such as portfolio selection, exposure limits, budget constraints, rebalancing rules, and operational execution constraints—because QUBO-based optimization can serve as a common decision engine across these interconnected optimization stages.
This aligns with how quantum finance optimization is evolving in practice: many implementations focus on combinatorial portfolio and allocation problems, frequently combining quantum or annealing-based components with classical methods in hybrid workflows rather than relying on fully quantum end-to-end execution.
Compared with quantum annealing deployments that can introduce hardware-specific embedding and topology constraints, VeloxQ is positioned as a topology-agnostic QUBO solver running on current infrastructure, which can simplify experimentation, accelerate deployment, and support broader rollout across teams and use cases.
In short, VeloxQ offers a practical “deploy now, stay quantum-ready” approach for financial organizations seeking immediate value from advanced optimization while building long-term optionality for quantum-integrated decision workflows.
References
Drug Discovery
Accelerate drug discovery decisions with QUBO-based optimization across complex R&D workflows on current infrastructure.
VeloxQ can support quantum-ready drug discovery workflows wherever combinatorial optimization is a bottleneck like across candidate selection, constrained molecular design, docking subproblems, fragment assembly, pose prioritization, hit triage, and portfolio-style experiment planning. VeloxQ does that by using a QUBO modeling approach that aligns with many quantum annealing and quantum-inspired research workflows.
For R&D teams, this creates a practical path to build optimization pipelines now (problem formulation, constraint encoding, benchmarking, pilot integration, and production deployment) on conventional hardware, while preserving compatibility with future hybrid quantum-classical architectures as quantum hardware matures.
VeloxQ is especially well suited to heterogeneous drug discovery workflows, where multiple decision layers must work together such as library pruning, fragment or feature selection, docking pose filtering, multi-constraint prioritization, and downstream assay selection. This mainly because QUBO can serve as a common optimization layer across these interconnected steps.
This is valuable in drug discovery because many high-impact tasks involve difficult trade-offs between objective quality and operational constraints (for example potency proxies, selectivity, novelty, synthesizability, structural compatibility, and resource limits), and QUBO formulations provide a flexible way to encode those trade-offs into a unified optimization problem.
Importantly, VeloxQ complements (rather than replaces) existing computational chemistry, docking, and AI pipelines: it can be used to accelerate combinatorial search and decision optimization stages while chemistry simulation, scoring, and experimental validation remain in the broader workflow.
This mirrors how quantum-readiness is developing in pharma and computational drug design: many practical efforts are hybrid, combining classical preprocessing and domain models with quantum or quantum-inspired optimization components for specific bottlenecks instead of attempting fully quantum end-to-end pipelines from day one.
Compared with quantum annealing workflows that can require hardware-specific embedding and topology constraints, VeloxQ is positioned as a topology-agnostic QUBO solver on current infrastructure, which can simplify experimentation and help teams standardize optimization workflows across discovery programs and therapeutic areas.
In short, VeloxQ offers a practical “deploy now, stay quantum-ready” path for drug discovery organizations that want immediate value from QUBO-based optimization today while building long-term optionality for future quantum-integrated discovery workflows.
References
Energy
Optimize grid, storage, and asset planning with quantum-ready QUBO workflows built for today’s energy operations.
VeloxQ can support quantum-ready energy-sector optimization workflows across generation scheduling, unit commitment, economic dispatch, optimal power flow, grid topology decisions, storage scheduling, DER coordination, microgrid formation, and flexibility orchestration—using a QUBO modeling layer that aligns with many quantum annealing and quantum-inspired optimization approaches explored in the power and energy domain.
For energy companies, utilities, and grid operators, this creates a practical path to build optimization pipelines now (problem formulation, constraint encoding, benchmarking, pilot deployment, and production integration) on conventional hardware, while preserving compatibility with future hybrid quantum-classical architectures as quantum systems mature.
VeloxQ is especially well suited to heterogeneous energy workflows, where multiple decision layers must work together—such as day-ahead planning, network operating constraints, asset and storage scheduling, and local flexibility activation—because QUBO-based optimization can act as a common optimization layer across interconnected subproblems and scenario analyses.
This is valuable in the energy sector because many high-impact decisions involve hard combinatorial trade-offs (cost, reliability, resilience, emissions, network limits, reserve requirements, and uncertainty handling), and current research increasingly explores QUBO formulations and hybrid quantum / quantum-inspired methods for exactly these kinds of constrained optimization bottlenecks.
Importantly, VeloxQ complements existing EMS/SCADA, forecasting, simulation, and optimization stacks rather than replacing them: it can accelerate combinatorial search and decision-optimization stages while domain simulation, controls, and operational execution remain in the broader workflow.
This mirrors how quantum-readiness is developing in energy: reviews and case studies consistently emphasize hybrid strategies, benchmark-driven evaluation, and selective deployment on suitable subproblems (especially binary/quadratic formulations) rather than fully quantum end-to-end operations from day one.
Compared with quantum annealing workflows that can require hardware-specific embedding and connectivity constraints, VeloxQ is positioned as a topology-agnostic QUBO solver on current infrastructure, which can simplify experimentation and help standardize optimization workflows across generation, grid, and distributed-energy use cases.
In short, VeloxQ offers a practical “deploy now, stay quantum-ready” path for energy organizations seeking immediate value from QUBO-based optimization while building long-term optionality for future quantum-integrated energy operations.
References
The science of today is the technology of tomorrow.
Edward Teller
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Our EU projects
Quantum Sp. z o.o. obtained funding for the implementation of the project entitled: “VeloxQ: the use of dynamic systems in decision-making processes based on knowledge obtained in the machine learning process, at various levels of complexity, in the optimization of industrial processes” implemented under Measure 1.1. of the Smart Growth Operational Programme 2014-2020, co-financed by the European Regional Development Fund, Grant Agreement No. POIR.01.01.01-00-0061/22-00 dated 11.05.2023.
Project objective
Development of digital solutions for solving combinatorial optimization problems learned from data by AI systems. As part of the research work, integrated algorithms (using classical and quantum resources) will be developed into a platform providing innovative services under the name veloxQ, using in particular dynamic systems in decision-making processes at various levels of complexity in the optimization of industrial processes.
Implementation period:
01.01.2023 – 31.12.2023
Project value:
Total value: PLN 7 528 727,06
Eligible expenditure: PLN 7 528 727,06
ERDF funding obtained: PLN 5 722 716,35
PROJECT TITLE: “Dynamic Resource Allocation in Industrial Ecosystems Prone to Disruptions Using Physically-Inspired Algorithms and Machine Learning”
PROJECT OBJECTIVE: Development of an innovative tool for optimizing resource management and scheduling in situations of dynamic changes and disruptions.
BENEFICIARY: QUANTUMZ.IO Limited Liability Company
PROJECT SUBJECT
Quantumz.io Limited Liability Company is implementing a project entitled “Dynamic Resource Allocation in Industrial Ecosystems Prone to Disruptions Using Physically-Inspired Algorithms and Machine Learning,” co-financed by the European Union funds within the European Funds for a Modern Economy Program. The subject of the project is the development of the XaosQ information system for managing dynamic resource allocation in an environment susceptible to disturbances. For this purpose, physically-inspired algorithms, including quantum algorithms, and methods from the field of machine learning will be utilized. A stochastic model of disturbances occurring in the process of dynamic resource allocation and a scheduling model generating stable solutions will be developed.
Research
STAGE 1
Stochastic Model of Disturbances
Construction and simulations of a stochastic model of disturbances occurring in the process of dynamic resource allocation.
STAGE 2
Costs of Stability
Determining the measure of costs of (in)stability of the schedule.
STAGE 3
Scheduling Model
Development of a scheduling model that generates solutions stable with respect to stability measures.
STAGE 4
Rescheduling Algorithm
Development of an algorithm that enables dynamic resource allocation (rescheduling) considering cost functions along with preliminary implementation.
STAGE 5
HPC Implementation
Implementation of algorithms from previous stages utilizing HPC resources in the form of a prototype solution.
Project Objective
The objective of the project is to develop an innovative tool for optimizing resource management and scheduling in situations of dynamic changes and disruptions. By using hybrid approaches that combine classical and quantum technologies, the algorithm will enable companies in these industries to quickly and efficiently respond to disturbances, minimizing their impact on schedules and reducing the cascading effect. The target group for the project’s results is the aviation industry (aircraft scheduling problem; ASP) and the transportation industry (vehicle routing problem; VRP).
Project Outcome
The result of the project will be a new innovative service, XaosQ – dynamic resource allocation in industrial ecosystems prone to disturbances, which will change the way companies handle disruptions, contributing to the transformation of the target market. The introduction of the service to the market will contribute to competitive advantage and market value growth, accelerating the transformation towards the use of advanced computing technologies and preparing the sector for the era of quantum computing. XaosQ will become a key element in the future management of resources, logistics, and digital security, influencing the acceleration of the transformation of the target market.
Project Value
16 136 370 PROJECT VALUE IN PLN
11 728 260 GRANT VALUE IN PLN
POIR.01.01.01-00-0061/22-00
FENG.01.01–IP.02–0625/23
POIR.01.01.01-00-0061/22-00
FENG.01.01–IP.02–0625/23
Extras
Quantum Information Explained in a Comic Book Format
“Revolution of state” is a popular-science book that introduces readers to the fundamentals of quantum computing in an accessible but rigorous way. It explains key ideas from quantum mechanics—such as qubits, superposition, entanglement, quantum gates, and measurement—and shows how they are used in quantum information processing. The book also presents practical examples and protocols, including quantum cryptography (BB84), no-cloning, and quantum teleportation, to connect theory with real applications.
Book