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Cross-Disciplinary Open Problems: Make the Interface Testable

A cross-disciplinary quantum project succeeds when a method from one field resolves a well-defined uncertainty in another. The research question must be meaningful to both communities and survive both sets of baselines. Relabeling a familiar effect as quantum does not establish a new mechanism, advantage or application.

Helpful background. Error budgets provides a common language for combining model, numerical and statistical uncertainty.

Choose an interface with a measurable output

Section titled “Choose an interface with a measurable output”
Interface Topic assessment Question that can guide a project
Quantum algorithms and chemistry Chemistry algorithms, chemistry at scale Which chemical decision is limited by an electronic observable that can be computed affordably?
Quantum dynamics and biology Quantum biology Does a specified quantum mechanism causally change a biological function under relevant conditions?
Sensing and biological samples Biological and nanoscale sensing Does the probe improve useful inference after backaction and sample variability?
Information and gravitation Gravity-mediated entanglement, holography and information Which operational result follows from which gravity or information assumptions?
Quantum attacks and security Cryptanalysis interface Which attack resource or security assumption changes under a concrete algorithm?

These questions are routes into assessments, not promises of novelty. Established: quantum mechanics underlies chemical and material structure. Active: identifying when specifically quantum coherence, correlations or computation improve a target function. Speculative: broad functional claims without a discriminating model or control experiment.

Separate mechanism, function and advantage

Section titled “Separate mechanism, function and advantage”

An observed coherent oscillation can establish a dynamical feature without proving a biological benefit. A sensor can detect a signal without outperforming an established assay. A quantum calculation can reproduce a molecule without changing a chemically relevant prediction. Lambert and colleagues’ biology review and Bauer and colleagues’ algorithm review illustrate why the target and baseline matter. Biology review, chemistry review.

The comparison should include the strongest relevant classical or conventional explanation. Ask a domain collaborator to identify that baseline before building an elaborate quantum model.

Entry project: a two-model discrimination study

Section titled “Entry project: a two-model discrimination study”

Choose one published claim with an observable dataset or a reproducible small model. Construct the proposed quantum explanation and a credible alternative with comparable adjustable parameters. Identify an intervention, not merely another fit, on which their predictions differ. Deliver parameter-identifiability tests, a proposed measurement and an uncertainty budget. A laptop can establish feasibility; biological samples, spectroscopy or fabrication require a suitable collaborator.

The baseline is the specialist community’s accepted model. Failure tests vary nuisance parameters, match model flexibility and use data withheld from fitting. Stop if the models are observationally indistinguishable within attainable precision; propose a more informative observable instead. Research begins with a validated distinction, a useful negative result or a method transfer that improves the actual domain task.

Novelty, resources and long-term challenges

Section titled “Novelty, resources and long-term challenges”

Large goals—useful fault tolerance, decisive strongly correlated calculations, dependable quantum networks or quantum-gravity tests—are collections of smaller evidence gaps. Choose one gap that can be closed with available methods and resources. Verify terminology in both literatures and record which result would make the project obsolete or change its interpretation.

Reviewed 11 October 2026; the selected reviews are 2013 and 2020 entry anchors, not evidence that any particular subproblem remains open today. Use the linked topic owners for claim-specific updates.

  • B. Bauer, S. Bravyi, M. Motta and G. K.-L. Chan, “Quantum Algorithms for Quantum Chemistry and Quantum Materials Science,” Chemical Reviews 120, 12685–12717 (2020). Preprint.
  • N. Lambert et al., “Quantum biology,” Nature Physics 9, 10–18 (2013). Article.