# SQT Solve | Optimization, Simulation & Advanced Computing > Bring the problem. SQT formulates it, establishes a baseline, and tests optimization, simulation, AI, hybrid and quantum-inspired methods against it. The method has to earn its place. - Canonical page: https://spinqtech.com/solve - Language: en - Last significant update: 2026-09-12 - Site overview for agents: https://spinqtech.com/llms.txt --- ## Hero SQT SOLVE Bring the problem. We’ll find the right way to explore it. SQT helps teams understand, formulate and experiment with difficult business and operational problems using the computational methods that actually fit. Quantum is optional. Evidence is not. Complexity Solution ## What are you trying to make better? Problem Mapping Choose the closest one. The system will adapt to your context. Select a goal to visualize the structural complexity. ### Intents PLAN BETTER Schedules, routes, sequences, capacity. ALLOCATE BETTER People, inventory, machines, capital, resources. DECIDE UNDER UNCERTAINTY Demand, risk, capacity, disruption, alternatives. UNDERSTAND THE FUTURE Scenarios, pathways, trade-offs, what to do now. ## A difficult problem often leaves clues. Select a signal to see how structural complexity hides underneath. Select a clue on the right to diagnose the structure. If several of these are familiar, the difficulty may be in the structure of the problem itself. ### Signals One change breaks the plan Too many possibilities to compare Improving one objective makes another worse The answer changes when conditions change Expert intuition is carrying too much “Good enough” is the only practical answer ## Why Complexity The Roots of Complexity Small business. Huge decision space. A company does not need thousands of employees to face millions of possible decisions. Force System Observer The problem becomes difficult when the space of possible answers grows faster than your ability to evaluate it. ### Forces #### Possibilities The raw number of options. #### Constraints The rules that invalidate paths. #### Trade-offs Competing goals fighting for priority. #### Uncertainty Changing conditions requiring replanning. ## Your current method does not have to be wrong for a better method to exist. Most teams use the best practical approach available to them. The question is not whether your spreadsheet, software or experience is bad. The question is whether the value of a better answer justifies looking for one. ### Methods - Spreadsheet - ERP - Rules & Heuristics - Human Expertise - Existing Software - AI Chatbots ## Complexity looks different in every business. Industry Lens The Central Question ### Industries Logistics Which routes, vehicles and deliveries produce the best plan under real constraints? Manufacturing How should jobs, machines, materials and maintenance be scheduled? Supply Chain Where should inventory be positioned as demand changes? Technology How should limited infrastructure, compute or engineering capacity be allocated? Energy How should generation, storage, demand and uncertainty be balanced? Other How do you find the absolute best combination of choices in a massive space? ## A problem rarely belongs to only one category. Multi-Disciplinary SQT combines the lenses the problem requires. Your Problem ### Capabilities OPTIMIZATION Which feasible option creates the best outcome? SCHEDULING Who or what should happen when, where and in what sequence? SIMULATION What could happen under different assumptions and uncertainty? FUTURE SCENARIOS What future do we want, what paths could reach it and what should we do now? ## Better Formulation Sometimes the breakthrough is not a new technology. It is a better formulation. Before changing the computation, we challenge how the problem itself is represented. Messy Reality Assumptions, conflicting goals, unstated rules, and historical bias all mixed together. ### Steps Decision #### What decision are we actually making? Variables #### What can we change? Constraints #### Which rules are strictly real? Objectives #### What are we optimizing for? Alternatives #### What would materially better mean? ## Start where the problem requires. You do not have to start at Step 1. You do not have to complete all seven. Flexible Entry If you already have a well-formulated model, we can start directly at Experiment. If you only have a business mandate, we start at Learn. ### Steps 1 #### Learn Create enough shared understanding to move correctly. We map the business reality. No math yet. Who are the stakeholders? What is the actual goal? What is preventing it? 2 #### Think Challenge the current way of seeing the problem. We strip away assumptions. Are the ‘rules’ physics, or just historical habits? What if we relaxed them? 3 #### Discover Identify where computational advantage may exist. We look for the combinatorial explosion. Where does classical human intuition break down? That’s where we focus. 4 #### Formulate Translate the problem into objectives, variables and constraints. We build the mathematical representation. Objective functions, variables, and hard vs. soft constraints. 5 #### Experiment Test competing approaches against the baseline. We run the formulations against different solvers (Classical, AI, Quantum-inspired) to see which yields the best operational advantage. 6 #### Pilot Validate the approach in a realistic environment. We test the winning approach on a slice of real-world data or operations to prove the value isn’t just theoretical. 7 #### Continue Operationalize, improve or scale what proved valuable. We integrate the engine into your existing systems, providing ongoing competitive advantage. ## The current method gets a seat at the table. Before testing an alternative, we establish what already works and how well it works. Baseline Alternative Note: Illustrative comparison An alternative only matters if it creates enough advantage to justify changing. ### Dimensions Solution quality 40 Decision speed 30 Cost 70 Capacity 50 Risk 60 Human effort 80 Resilience 40 ## We do not decide the technology before understanding the problem. Technology Agnostic The Problem The objective isn’t quantum The objective is a better answer ### Methods - Classical computing - Optimization - AI - Simulation - Quantum-inspired - Hybrid computing - Quantum computing ## The work should leave evidence behind. Note: Demo artifacts Something understood, tested and usable. ### Outputs - Complex Problem Map - Opportunity assessment - Problem formulation - Current-state baseline - Data readiness assessment - Simulation / Optimization model - Experiment notebook - Benchmark / POC / Pilot design - Decision memo ## Not every difficult problem needs SQT. Worth exploring Simpler problem first ### Strong - The decision matters economically. - There are many interacting choices or constraints. - Current methods regularly settle for ‘good enough.’ - Small improvements could create meaningful value. - The problem repeats or can be tested. - Someone owns the outcome. ### Not Ready - The underlying business process is undefined. - The objective cannot yet be described. - A simpler operational fix obviously comes first. - There is no meaningful value in improving the answer. ## You should not need a research department to investigate a hard problem. SQT operates with an AI-first core team and brings domain, mathematical, scientific and technology specialists into the work when the problem requires them. Client Problem SQT AI-First Core Problem formulation & orchestration Specialist Expertise As required Technology Partners Infrastructure & compute Specialist depth on demand. Not permanent overhead. ## No technology theater. Quantum is allowed to lose. ### Statements - We will not call every hard problem a quantum problem. - We will not claim an advantage before benchmarking it. - We will not recommend complexity when a simpler fix is better. - We will make assumptions, limitations and uncertainty explicit. - We will bring specialist expertise when the work requires it. ## Start by finding out whether the problem is worth exploring. Problem Fit Scan Starting the scan does not mean booking a sales call, buying consulting, committing budget, or starting a project. The scan looks at: ### Looks At - What you are trying to improve. - Why the decision is difficult. - How it is solved today. - What a better answer could be worth. - Whether there is enough readiness to experiment. Potential Results ### Results - Strong candidate - Worth understanding first - Solve simpler problem - Learning opportunity ## Frequently Asked Questions ### Items #### How do I know if my problem is complex enough? If your current method struggles to find optimal answers, forces you to compromise frequently, or scales poorly when variables change, it is likely complex enough to warrant exploration. #### Do I need to know which computational method I need? No. You bring the business problem and the constraints. We determine which computational approach, or combination of approaches, fits best. #### Does my company need a technical team? No. We act as your advanced capability partner. If you have a technical team, we collaborate with them. If you do not, we provide the necessary depth. #### Do I need clean data before starting? Not necessarily. We can assess your data readiness as part of the initial exploration and help define what data is actually required to improve the decision. #### Does SQT only solve quantum problems? No. The objective is a better answer, not quantum for the sake of quantum. We use classical optimization, AI, simulation, and hybrid methods as appropriate. #### What happens if classical computing is better? Then we use classical computing. We benchmark alternatives against the baseline to prove value, regardless of the underlying technology. #### Do we have to complete all seven stages? No. If you already have a strong formulation, we can start with experimentation. If an early stage reveals a simpler fix, we stop there. #### Can we start with a small experiment? Yes. In fact, we prefer starting with a focused baseline and an experiment notebook before proposing large-scale pilots. #### Can SQT work with our existing software and team? Yes. We complement your existing systems. The goal is often to provide an advanced decision engine that integrates with your current workflow. #### What happens when specialist expertise is required? We bring in specific domain, mathematical, or scientific experts from our network exactly when the problem demands it, without adding permanent overhead. #### How do you decide whether an experiment is successful? We measure success against the baseline using the business dimensions that matter to you: solution quality, speed, cost, capacity, and resilience. ## You don’t need to know the solution before you start. Tell us what is difficult, how you solve it today and why a better answer would matter. We’ll help determine whether the problem deserves deeper exploration. Start with the Problem Fit Scan. No quantum knowledge required. --- Generated from the same content source as https://spinqtech.com/solve. Spin Quantum Tech (SQT) is part of Bernier Group.