Operations Research & Optimisation Teams

Better decisions
begin with better evidence

Investigate where quantum optimisation can add value to your decision workflows. Start with the operational problem, establish a strong classical baseline and measure the cost of a useful answer.

Explore the evaluationDiscuss a workload

01 / Establish the case

Three questions before a quantum investment

A valuable decision problem, an unresolved classical bottleneck and an economical quantum contribution each need their own evidence.

  1. What is a better decision worth?

    Connect solution quality to an operational outcome: lower costs, greater throughput, better service or more effective use of scarce resources. State the constraints, decision frequency and value of a meaningful improvement.

    EstablishAn operational objective and a measurable value threshold

  2. What remains beyond the classical baseline?

    Establish what strong classical methods already achieve within your deadline. Identify remaining solution gaps, slow replanning or modelling compromises. Hardness alone does not establish an opportunity for quantum computing.

    EstablishA credible baseline and a specific unresolved bottleneck

  3. Can quantum deliver that improvement economically?

    Test whether a quantum or hybrid method creates enough additional value to justify its additional cost. Include encoding, classical work, quantum execution, queueing, validation and integration in the comparison.

    EstablishEvidence of incremental value within operational limits

Assess the original decision objective and feasibility, alongside elapsed time and cost. A better circuit score alone does not establish a better operational decision. Benchmarking perspective [5]

02 / Apply the questions

Different sectors, the same standard of evidence

Explore decisions with operational consequences. These research examples guide an evaluation; they are not performance results from Hilvarian.

Finance

Portfolio allocation, trade execution and payment ordering

The decision value
Measure transaction costs, risk-adjusted objectives or liquidity requirements under the same business rules. Released liquidity must be translated into funding value; it is not itself profit.
The classical baseline
Compare with the appropriate convex, mixed-integer or specialised heuristic approach. Identify which constraints, model details or time limits leave room for an improvement.
The quantum test
Require feasible solutions and a repeatable improvement after execution and integration costs, within the decision deadline.

MeasureLiquidity requirements · Transaction costs · Risk constraints · Decision latency

Healthcare

Staff and theatre scheduling, capacity allocation and kidney exchange

The decision value
Measure waiting time, overtime, resource use or feasible transplant matches while preserving clinical, staffing and fairness requirements.
The classical baseline
Test against mixed-integer, constraint-programming and robust-optimisation methods appropriate to the task. Specialised integer programming has already solved practical kidney-exchange instances to optimality.
The quantum test
Identify a remaining bottleneck on representative workloads. Compare feasible outcomes, reliability and total cost under the same clinical and operational constraints.

MeasureWaiting time · Overtime · Feasible matches · Schedule reliability

Industry

Production scheduling, logistics and energy dispatch

The decision value
Connect improvements to throughput, delivery performance, energy use and operating costs. Account for disruptions and the frequency at which decisions must be revised.
The classical baseline
Use a strong mixed-integer, constraint-programming or domain-specific baseline. Locate a costly modelling simplification or a difficult subproblem inside the full workflow.
The quantum test
Evaluate any proposed quantum component in the complete process. Include decomposition, data movement, classical repair and the effect on operational performance.

MeasureThroughput · Delivery performance · Energy cost · Replanning time

03 / Build a useful evaluation

From an operational question to a reproducible comparison

  1. Define the workload

    Preserve representative instances, constraints and the original objective. Set the operational deadline and the minimum valuable improvement.

  2. Establish the baseline

    Tune relevant classical methods fairly. Record feasible solution quality, valid bounds where available, elapsed time and cost.

  3. Test the contribution

    Repeat trials across instances and seeds. Separate raw quantum samples from classical refinement and compare complete workflows.

  4. Decide from the evidence

    Assess robustness, total cost and incremental value. Advance the cases that meet the criteria and record why others do not.

Hilvarian in Spinorio Lab

Connect the problem, the computation and the evidence

Use Hilvarian for supported optimisation problems in Spinorio Lab. Develop formulations and classical comparisons in notebooks, compile quantum circuits with Weylattice and retain supported experimental records through Spinorio Workflow.

Weighted Max-Cut is the documented starting point. Applying a solver to a sector workload requires a suitable formulation, supported inputs and validation against the original constraints.

Explore HilvarianExplore application solversExplore Spinorio Lab

Read the evidence

Research and operational references

Historical results retain their original setting and baseline. They do not establish expected savings for another organisation or a quantum implementation.

  1. [1]

    Bank of Italy · 2026

    Liquidity Optimization in Gross Settlement Systems with Quantum Reordering: Application to TARGET2

    Hybrid quantum and classical simulated-annealing comparisons for payment ordering.

  2. [2]

    Rath, Rajaram & Mahajan · Operations Research · 2017

    Integrated Anesthesiologist and Room Scheduling for Surgeries: Methodology and Application

    A deployed classical robust-optimisation study at UCLA, with reported resource and cost improvements.

  3. [3]

    Anderson et al. · PNAS · 2015

    Finding long chains in kidney exchange using the traveling salesman problem

    Specialised integer-programming methods that found optimal solutions for the practical instances studied.

  4. [4]

    MISO · 2025 Value Proposition

    The value of coordinated grid operations and planning

    System-level benefits of centralised markets, commitment, dispatch and regional coordination.

  5. [5]

    IBM Research · 2026

    A new playbook for quantum optimization benchmarking

    Practical problem classes, modelling compromises and comparisons with strong classical methods.

Start with your decision problem

What would a better answer change?

Bring your objective, constraints, current method and decision deadline. Define what a useful improvement would mean for your team.

Discuss an optimisation workload