FAQ
Frequently Asked Questions about HQS’ ITBQ Framework
ITBQ stands for Identify, Transform, Benchmark, Show Quantum Advantage. It is a structured framework for assessing whether a real-world problem is a credible use case for quantum computing.
General Questions About ITBQ
-
ITBQ stands for Identify, Transform, Benchmark, Show Quantum Advantage.
It is a structured framework for evaluating whether a use case is a good candidate for quantum computing. The goal is to move beyond vague claims about “quantum applications” and assess whether a real-world problem has a credible path to quantum utility or quantum advantage.
-
The framework is called ITBQ because the final step is not merely to “show” something, but to assess Quantum Advantage explicitly.
A possible wording such as ITBS — Identify, Transform, Benchmark, Show — would be less precise, because it would hide the central question: show what? The purpose of the framework is not only to demonstrate that a quantum method runs, but to evaluate whether it has a credible path to quantum utility or quantum advantage.
In addition, ITBS is already commonly used as an abbreviation for iliotibial band syndrome, a sports injury. Using ITBQ avoids confusion and makes the quantum-computing focus clearer.
In short: ITBQ ends with Q because the key question is quantum advantage.
-
ITBQ stands for:
• Identify the industry or scientific problem.
• Transform the problem into a quantum-computable formulation.
• Benchmark the quantum approach against the best classical solution.
• Show Quantum Advantage including scaling towards relevant problem sizes
These four steps help determine whether a quantum computing use case is realistic, useful and worth investing in.
-
Quantum computing has many proposed use cases, but not every important industry problem is a good quantum computing problem.
ITBQ is needed because many use cases look promising at first but fail when examined more carefully. Some cannot be efficiently transformed into quantum formulation. Others can already be solved well with classical computers. ITBQ helps identify which use cases have a real chance of benefiting from quantum computing.
-
The main message of ITBQ is simple:
A good quantum computing use case must be both useful and quantum relevant.
It must solve a real problem, map naturally or efficiently to quantum computing, be benchmarked against strong classical alternatives, and have a credible path to quantum advantage.
Good Quantum Computing Use Cases
-
A good quantum computing use case should meet four conditions:
1. It addresses the bottleneck in a real and specific industry or scientific problem.
2. It can be transformed automatically into a suitable quantum problem.
3. It is benchmarked against the best classical methods.
4. It has a credible path to quantum advantage for the relevant problem sizes.
-
No. Industrial relevance is necessary but not sufficient.
A problem can be highly relevant to industry and still be a poor quantum computing use case. To be credible, the problem must also have a clear connection to a quantum algorithm that can outperform the best classical method. And most importantly the quantum method needs to address the real bottleneck in the industrial use case. A very important first step is to map the steps that are currently used to solve the industrial problem. Next you need to identify the critical step or bottleneck and then verify that this step can be connected to a quantum algorithm. And you have to make sure to know which the best classical method is to solve this critical step.
ITBQ combines industrial relevance with quantum relevance.
-
Use cases are especially promising when the problem is already quantum-mechanical in nature.
Examples include:
• quantum simulation,
• spectroscopy,
• NMR simulation,
• strongly correlated materials,
• molecular quantum dynamics,
• open quantum systems,
• and selected quantum chemistry problems.
These areas often have a more natural connection to quantum computers than generic optimization problems.
-
A quantum demonstration shows that something can be done on a quantum device.
A quantum use case connects that capability to a real problem, a real user, a classical benchmark and a credible path to value.
ITBQ helps move from demonstrations to use cases.
-
Yes. A use case may fail ITBQ if:
• there is no value creating step that can be connected to a quantum algorithm
• the problem cannot be transformed efficiently,
• classical methods already solve it well,
• or there is no credible argument for quantum advantage.
Failing ITBQ is not necessarily bad. It helps save resources and redirect efforts toward more promising directions and can uncover classical solutions that solve the desired problem.
The Four ITBQ Steps
-
The Identify step asks whether there is a real problem worth solving.
This includes questions such as:
• Is there a clear industry or scientific pain point?
• Who is the end user?
• What is the concrete problem, e.g. molecules, that is solved?
• Which accuracy needs to be achieved/improved for the targeted quantity?
• Is the problem relevant beyond a purely academic setting?
The Identify step prevents teams from working on technically interesting problems that have no clear user or market relevance.
-
The Transform step asks whether the real-world problem can be converted into a quantum-computable formulation.
This may involve mapping the problem or parts of it to:
• a Hamiltonian,
• a quantum circuit,
• a quantum simulation task,
• a spin model.
This step is often underestimated. Many real-world problems do not naturally fit quantum hardware and require significant reformulation before they can be evaluated seriously. And creating an automated workflow that performs the transformation can be challenging.
-
The Transform step is important because a real-world problem is rarely already a quantum problem.
For example, an industry problem may involve complex data, constraints, workflows, approximations and domain-specific assumptions. These must be translated into a form that a quantum computer can process.
If this transformation introduces too much overhead or changes the problem too much, the use case may no longer be practical.
-
The Benchmark step compares the quantum approach with the best available classical solution.
This is essential because quantum computing should not be compared only to weak or outdated classical methods. A serious benchmark should include strong classical algorithms, high-performance computing, domain-specific software, heuristics and approximations where relevant.
The benchmark should compare the full workflow, not just the quantum subroutine.
-
Quantum advantage matters in cases where the quantum approach performs better than the best realistic classical alternative.
In many cases, classical methods improve over time. A use case that looked promising for quantum computing may become less attractive once better classical algorithms or optimized software are considered.
Benchmarking creates scientific discipline and helps avoid exaggerated quantum advantage claims.
-
The final step, Show Quantum Advantage, asks whether the quantum approach can deliver a meaningful advantage over classical methods.
This advantage may be based on:
• faster computation,
• better scaling,
• improved accuracy,
• access to quantum dynamics,
• better representation of quantum correlations,
• or a new capability that is not practical classically.
It is important to consider the scaling of the quantum algorithm. Being able to solve a small problem on a quantum computer does not proof that large problems can also be solved efficiently.
Quantum Utility, Quantum Advantage and Benchmarks
-
Not always, but a strong use case should have a credible argument for why quantum computing can help.
A provable exponential speedup is ideal, but practical quantum advantage may also depend on accuracy, scaling, sampling, access to quantum dynamics or better representation of quantum correlations.
The key point is that the advantage claim must be explicit and testable.
-
A classical baseline is the best available non-quantum method for solving the same problem.
This may include:
• conventional algorithms,
• high-performance computing,
• tensor networks,
• quantum chemistry packages,
• classical simulation software,
• machine learning methods,
• or domain-specific industrial tools.
A quantum approach should be compared against the strongest realistic baseline, not against a simplified or outdated method.
-
Before claiming quantum advantage, it is necessary to understand how well the problem can be solved without a quantum computer.
This step often creates value by improving the classical workflow. It also prevents false claims of quantum advantage that disappear when better classical methods are used.
In ITBQ, this is part of the Benchmark step.
-
ITBQ places benchmarking at the center of quantum use-case assessment.
Benchmarking should not only measure hardware performance. It should compare complete application workflows, including quantum resources, classical pre-processing, post-processing, error mitigation, runtime, accuracy and cost.
-
ProduktbeschreibungITBQ is a practical framework for assessing the path to quantum advantage.
It does not assume that quantum advantage exists for a given use case. Instead, it asks what evidence is available, what steps are missing and whether the use case deserves further investment.
ITBQ Ratings and Next Steps
-
An ITBQ score is a structured assessment of how mature a quantum computing use case is across the four ITBQ dimensions.
For example, a use case may score highly on industrial relevance but poorly on transformation or benchmarking. This helps identify where further work is needed.
-
That is not a problem. ITBQ is not meant to produce a perfect final score on the first attempt. It is meant to support structured thinking and make assumptions visible.
If you are unsure about a rating, choose the lower rating and document why. The most important part is not the number itself, but the reasoning behind it.
A conservative rating is often more useful than an optimistic one, because it shows where further work is needed.
The ITBQ rating should be understood as a living assessment. As more information becomes available — for example, a better classical benchmark, a clearer quantum formulation, or new experimental data — the rating can be updated.
-
The rating should point directly to the next action. A low score in one ITBQ category means that this part of the use case needs more work before the overall quantum advantage claim can be considered mature.
For example:
• If Identify is weak, the next step is to clarify the real user, the pain point, the market or scientific relevance, and the value of solving the problem.
• If Transform is weak, the next step is to define the quantum formulation, for example the Hamiltonian, circuit, encoding, simulation task or hybrid workflow.
• If Benchmark is weak, the next step is to compare against the best available classical method, not only a simple or convenient baseline.
• If Quantum Advantage is weak, the next step is to make the advantage claim more precise: What exactly should become faster, more accurate, more scalable or newly possible?
In this sense, ITBQ is not only an assessment scheme. It is also a roadmap for improving a quantum computing use case. The lowest-rated category often indicates the most important next step.
Application Areas
-
Spectroscopy is a strong use case because many spectroscopy problems involve quantum dynamics, spin systems, Hamiltonians and time evolution.
For example, NMR spectroscopy can be formulated as the simulation of interacting nuclear spins. This makes it naturally aligned with quantum simulation methods.
Spectroscopy is also industrially important in pharmaceuticals, chemistry, materials science, process analytics and diagnostics.
-
NMR is a good example of ITBQ logic.
• Identify: NMR is a widely used analytical method with real industrial and scientific users. To get the highest score, however, you need to specify a relevant molecule of interest.
• Transform: NMR problems can be mapped to spin Hamiltonians and quantum time evolution, since they are quantum mechanically in nature.
• Benchmark: Classical NMR simulation methods exist and can be used as serious baselines.
• Quantum Advantage: Solving an NMR simulation problem involves a quantum mechanical time evolution, which is a task a quantum computer is good for. For sufficiently complex spin systems, quantum computers may offer a credible path to advantage.
This makes NMR a strong candidate for quantum-enabled spectroscopy.
-
Yes, but ITBQ often reveals that optimization use cases require careful scrutiny.
Many optimization problems are industrially important but transforming them into quantum formulations can introduce significant overhead. In addition, classical optimization solvers are highly developed and often very competitive.
ITBQ helps determine whether a quantum optimization use case is genuinely promising or mainly attractive at a high level.
-
Yes. Quantum chemistry is a natural area for ITBQ because many problems are quantum-mechanical at their core.
However, not every quantum chemistry problem automatically leads to quantum advantage. ITBQ helps assess whether the problem has the right structure, whether the active space is meaningful, whether classical methods are already sufficient, and whether the quantum approach has a realistic path to better scaling or accuracy.
Strategic and Organizational Use
-
ITBQ is useful for:
• quantum computing companies,
• industrial end users,
• public funding agencies,
• investors,
• research institutions,
• policymakers,
• technology strategists,
• standardization bodies,
• and quantum software developers.
It provides a common language for discussing whether a quantum computing use case is credible.
-
Companies can use ITBQ to decide whether a quantum computing project is worth pursuing.
The framework helps companies avoid investing in quantum projects that sound attractive but lack a realistic path to value. It also helps define proof-of-concept projects with clear success criteria, classical baselines and end-user relevance.
-
Funding agencies can use ITBQ to evaluate quantum computing proposals more consistently.
Instead of asking only whether a use case is interesting, ITBQ asks whether the use case has passed the necessary steps toward quantum advantage:
• Is the problem real?
• Is the quantum formulation clear?
• Has it been benchmarked fairly?
• Is the advantage claim credible?
This can improve funding discipline and reduce the risk of supporting vague or overhyped applications.
-
Yes. ITBQ can help public buyers evaluate quantum technology proposals.
Procurement decisions should not be based only on hardware specifications or broad application promises. ITBQ helps buyers ask whether a proposed quantum solution has a real user, a clear quantum formulation, a serious benchmark and a credible path to advantage.
-
ITBQ supports quantum strategy by turning broad application discussions into structured decision-making.
It helps organizations answer:
• Which use cases should we priorities?
• Which ones need more transformation work?
• Which ones already have strong classical solutions?
• Which ones have a credible path to quantum advantage?
• Where should we invest time, money and technical effort?
-
ITBQ reduces hype by requiring concrete evidence at each step.
A use case cannot be considered mature just because it is linked to a large market or an important industry. It must also be transformed into a quantum formulation, benchmarked against strong classical methods and connected to a credible advantage mechanism.
This makes quantum computing claims more transparent and accountable.
-
Yes. The Benchmark step often requires improving the best classical approach.
This can create immediate value even before quantum advantage is achieved. In some cases, the process of assessing a quantum use case leads to better classical software, better workflows or a clearer understanding of the industrial problem.
-
ITBQ helps SMEs and startups communicate quantum value more clearly.
Instead of making broad claims, companies can show where their use case stands in the ITBQ process. This can improve conversations with investors, customers, public funders and partners.
ITBQ, Standardization and Public Policy
-
ITBQ can support standardisation by providing a common structure for reporting and evaluating quantum use cases.
A standard ITBQ-based report could include:
• the problem definition,
• the quantum transformation,
• the classical baseline,
• the benchmark method,
• resource estimates,
• and the evidence for quantum utility or advantage.
This would make quantum computing claims easier to compare across projects, companies and funding programs.
-
ITBQ can support European quantum policy by helping funding programmes focus on use cases with a credible path to impact.
It provides a practical way to distinguish between:
• interesting research ideas,
• industrially relevant but quantum-weak problems,
• quantum-relevant but commercially unclear problems,
• and use cases with both scientific credibility and end-user value.
This can help policymakers prioritise funding, structure calls, evaluate proposals and define application-oriented roadmaps.
-
ITBQ can help standardisation roadmaps define how quantum computing applications should be described, evaluated and benchmarked.
This is important because standards should not only cover hardware interfaces, software stacks and performance metrics. They should also cover how claims of quantum utility and quantum advantage are made.
An ITBQ-based standard could help define:
• what information a quantum use case must provide,
• how the transformation to a quantum problem should be documented,
• how classical baselines should be selected,
• how benchmarks should be reported,
• and what evidence is required before claiming quantum advantage.
Technical Scope of ITBQ
-
No. ITBQ can be used for both near-term quantum devices and future fault-tolerant quantum computers.
For near-term devices, ITBQ helps assess whether a use case can deliver already. For fault-tolerant quantum computers, it helps assess long-term quantum advantage and resource requirements.
-
Yes. ITBQ is well suited for hybrid quantum-classical workflows.
In many realistic applications, a quantum computer will not solve the entire problem alone. Instead, it will be one part of a larger workflow that includes classical pre-processing, quantum execution, classical post-processing, validation and domain-specific interpretation.
ITBQ helps make these workflow assumptions explicit and formulate the transformation step carefully.
-
Yes. ITBQ can be applied to algorithmic ideas, industrial applications and scientific workflows.
For algorithms, ITBQ helps ask whether the algorithm addresses a real use case and whether it can outperform classical methods. For applications, ITBQ helps ask whether the industrial or scientific problem can actually be transformed into a quantum-computable task.
About HQS and ITBQ
-
ITBQ was developed by HQS Quantum Simulations, a quantum software company based in Karlsruhe, Germany.
HQS works on quantum simulation algorithms and applies them to industrially relevant problems in chemistry, materials science and spectroscopy.
-
HQS Quantum Simulations is a quantum software company focused on applying quantum computing to real problems in chemistry, materials science and spectroscopy. The company works at the interface between quantum algorithms, classical simulation, industrial use cases and scientific software.
This position makes HQS familiar with a central challenge in quantum computing: a use case must be both industrially relevant and quantum relevant. Many proposed applications satisfy only one of these two requirements.
ITBQ was developed from practical experience in assessing where quantum computers may create real value, including use cases such as NMR simulation, quantum chemistry and spectroscopy.
HQS has also published technical work on quantum use-case assessment and quantum advantage, including the paper “What is a good use case for quantum computers?”. ITBQ is therefore not only a communication concept, but a structured methodology derived from technical work on real quantum computing applications.
-
You can read the ITBQ paper here:
“What is a good use case for quantum computers?”
-
Yes. The interactive ITBQ tool allows users to explore and assess quantum computing use cases using the Identify, Transform, Benchmark, Quantum Advantage framework.
Try it here:
-
If you have questions about ITBQ, quantum use-case assessment, or how to apply the framework to your own problem, you can contact HQS Quantum Simulations.
Please reach out via:
use-caes@quantumsimulations.de
For specific discussions, it is helpful to include:
• a short description of your use case,
• the industry or scientific context,
• what you hope quantum computing could improve,
• whether a classical solution already exists,
• and which ITBQ step you are currently unsure about.
This helps us understand whether the question is mainly about identifying the use case, transforming it to quantum, benchmarking, or assessing quantum advantage.