cond-mat.mtrl-sciOct 28, 2026
SaveAutonomous Quantum Measurement with Agentic AI
Organizations: Quantum Measurement Group, MIT, Cambridge, MA 02139, USA · Department of Nuclear Science and Engineering, MIT, Cambridge, MA 02139, USA · Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA 02139, USA · Center for Computational Science & Engineering, MIT, Cambridge, MA 02139, USA ·
Abstract
Quantum materials exhibit diverse electronic and functional phenomena, but their characterization requires extensive experimental expertise, iterative decision-making and accumulated laboratory knowledge that is difficult to document and transfer. Here we introduce Quantum Agent (QUANTA), an agentic artificial intelligence (AI) framework for autonomous quantum-materials characterization. Using a widely adopted Physical Property Measurement System (PPMS) as an experimental platform, QUANTA combines retrieval augmentation using scientific literature, experimental protocols and prior measurements with specialized agents for experimental planning, troubleshooting, and data interpretation. A separate deterministic control layer validates instrument actions. Guided by incoming data, QUANTA evaluates measurement quality, revises experimental plans and selects subsequent measurements. We demonstrate these capabilities through transport measurements of the topological Weyl semimetal TaP, as well as investigations of nonlinear responses in magnetic material Co2MnGa. These results establish QUANTA as a generalizable framework for evidence-driven, safeguarded and closed-loop quantum-materials characterization.
Figures & tables
Figure 1: Architecture of QUANTA for autonomous measurement of quantum materials. A human researcher (top yellow block) supplies the scientific question and authorizes physical interventions. The main agent (purple block) iterates through evidence review, measurement planning and interpretation, drawing on literature, protocols, manuals and prior data in a shared knowledge store; specialist agents support these tasks. Plans pass to an execution coordinator (central blue block) for validation before a dedicated worker acts through the PPMS and measurement computers. Inside the DynaCool (bottom left), the PPMS sets temperature and field, while external current source and lock-in instruments provide excitation and electrical readout. Instrument data and faults return to the agent for the next decision. The dashed yellow arrow denote the researcher’s physical oversight of the entire measurement process.
Figure 2: Autonomous calibration of transport-measurement parameters by QUANTA. a. Instrument-range recovery. A 100- A root mean square (RMS) excitation at 300 K and 0 T produces a lock-in overload. QUANTA increases the lock-in input headroom, after which 12 readable measurements are recovered. The panel also shows the detailed closed-loop workflow, including fault reporting, agent reasoning, proposal of revised settings, coordinator validation and execution of the corrected measurement. b. Selection of excitation current for a consistent resistance measurement. At 100 K and 0 T, the resistance inferred at 3 mA differs from the settled 1-mA reference. QUANTA therefore rejects the 3-mA condition and retains 1 mA, for which all readings pass the validity checks. c. Selection of excitation current for high signal-to-noise ratio (SNR). At 2 K and 0 T, reducing the current to 0.5 mA gives a low complex-signal SNR of 6.81, below the acceptance threshold of 10. QUANTA therefore retains the higher-current condition. d. Selection of acquisition time duration. At 2 K and +9 T, 1-s and 3-s lock-in time constants are evaluated over increasing averaging spans against a 5- within-window uncertainty target. The faster acquisition does not satisfy the required precision, so QUANTA retains the longer measurement condition.
Figure 3: Adaptive transport characterization of TaP. The user question (yellow) and retrieved knowledge (grey) define candidate transport hypotheses and motivate an initial measurement survey ( steps 1–2 ). Temperature- and field-dependent resistance measurements are acquired through proposed sequences, and the resulting trends are used to select appropriate analyses ( step 3 ) and refine the next measurement sequence ( step 4 ). The example data show zero-field resistance versus temperature together with raw transverse and longitudinal field-dependent resistance in TaP, without transverse anti-symmetrization or longitudinal symmetrization. The transverse curves retain a substantial field-even contribution. Checked boxes denote agent-selected actions. Refined measurements are returned for execution, after which the agent summarizes supported observations and remaining uncertainties ( step 5 ) and formulates a final response that separates candidate interpretations from established findings ( step 6 ).
Figure 4: Testing competing origins of the nonlinear transverse voltage in Co 2 MnGa. QUANTA progressively tests the mechanism assignment through four questions. Q1 , current sweeps from 0.1–1 mA establish an approximately quadratic response, with fitted exponents close to 2, but this scaling is compatible with both electronic and Joule-heating-induced signals. Q2 , temperature-dependent measurements reveal a sign reversal in the imaginary transverse component, although the full complex response remains finite and the reversal alone does not establish distinct mechanisms. Q3 , frequency sweeps over 17.777–3555.4 Hz show temperature-dependent spectral shapes after normalization at 177.77 Hz, ruling out simple rescaling of a fixed response while remaining compatible with either multiple contributions or a single process with evolving dynamics. Q4 , conductance-based scaling is strongly ill-conditioned because the longitudinal resistance spans only 3.28% across the measured temperature range, preventing reliable separation of candidate contributions. QUANTA therefore retains both electronic and thermoelectric origins as plausible and reports the microscopic attribution as inconclusive.
Appendix figures & tables12 assets
Supplementary material from the paper’s appendix.
Appendix
Supplementary Fig. 1: Physical configuration of the agentic PPMS experimental platform. (a) Laboratory overview showing the PPMS control computer, DynaCool, external measurement rack, module switch, and measurement computer. (b) Close-up of the external measurement electronics, including the Keithley 6221 current source, two SR860 lock-in amplifiers, and sample wiring box. (c) DynaCool sample-interface region and adjacent connection hardware. The photographs identify the physical components corresponding to the control and measurement paths in main-text Fig. 1; individual sample-contact assignments depend on the experimental wiring.
Supplementary Fig. 2: Scientific workflow and instrument connectivity of QUANTA. (a) Agent-guided measurement workflow, distinguishing researcher-defined objectives and physical verification, AI-based scientific coordination, stored knowledge and campaign records, and deterministic execution software. The AI coordinator retrieves evidence, formulates measurement plans, and interprets results, while software validation and a persistent worker enforce the authorized operating envelope and record experimental outcomes. (b) Connectivity among the researcher’s computer, measurement computer, PPMS control computer, and physical instruments. The AI coordinator communicates with the MCP service over SSH, while the persistent worker interfaces with the PPMS through MultiPyVu and with the external electrical instruments through NI-VISA/GPIB. SQLite storage supports persistent execution records, numerical analysis, and a read-only dashboard. Arrows distinguish software communication, stored records, electrical signals, and the sample environment.
Supplementary Fig. 3: Detailed software architecture, execution safeguards and data provenance. The implementation-level architecture underlying the workflow in Supplementary Fig. 2 . (a) The researcher defines the objective, operating envelope and stop authority, while the AI coordinator retrieves evidence and selects measurements. (b) Structured requests pass through application-level validation and a durable queue before execution by the persistent worker, which maintains exclusive instrument ownership. Deployment checks, stop controls, instrument-state safeguards and immutable run records support bounded execution and auditability. (c) NI-VISA/GPIB carries instrument commands and readbacks, separately from excitation, voltage and phase-reference signal paths. (d) MultiPyVu connects the worker to MultiVu and the DynaCool. Run-level persistence does not constitute an unattended campaign scheduler; source-off attempts do not zero the magnet or cancel retained PPMS targets.
| Layer | Required content and role |
| Researcher intake | Objective, sample identity, physical wiring evidence, allowed temperature, field and excitation range, time budget, completion criterion, excluded physical actions and stop authority. Establishes campaign scope but does not encode an executable command |
| Repository entry instructions | Exact deployment, worker owner, configuration, state directory and requirement to read the operating guide. Prevents reuse of a stale endpoint or a different sample’s profile |
| Operating guide | Manual and literature retrieval, sequence search, qualification, validation, monitoring, troubleshooting and shutdown policy. Defines how a scientific objective is converted into bounded stages |
| Campaign record and checklist | Hypotheses, controls, authorization, limits, accepted and rejected evidence, pending route and final-state requirement. Maintains the evolving scientific state and handoff record |
| Plan and deployment configuration | Ordered set points, rates, electrical settings, repetitions, stop conditions, numerical ceilings and configuration hash. Supplies the only representation accepted by the validator and worker |
| SQLite run record | Plans, runs, readings, events, cached state, timestamps, quality flags and shutdown result. Connects a scientific decision to the exact action and returned evidence |
Supplementary Table 1: Instruction and record hierarchy used by QUANTA.
| Case | Evidence, guarded action and interpretation limit |
| TaP overload, 300 K, 0 T | Evidence: At RMS and , the SR860 overloaded with a input range and sensitivity; no reading was accepted. Action: Stop excitation, confirm source-off and retest in a new validated plan with a range and sensitivity. Conclusion: The retest produced 12/12 readable values, establishing recovery but not the physical overload cause |
| TaP current bracket, 100 K, 0 T | Evidence: Within a 1–3–1 mA RMS bracket, the 3-mA run and subsequent 1-mA return differed by in . Their combined descriptive block uncertainty was , giving a three-times-uncertainty threshold of , which exceeded the alternative 1% signal-scale floor. The observed difference exceeded this threshold. Action: Withhold the 3-mA field scan and return to a longer-settled 1-mA plan. Conclusion: The 1-mA scan yielded 180/180 valid readings; heating, non-ohmic response, contact change, pickup and drift were not separated, and 3-mA damage was not established |
| TaP low-current bracket, 2 K, 0 T | Evidence: In the 1–0.5–1 mA RMS comparison, the 0.5-mA run gave , where and are the mean current-normalized components and and are their four-block standard uncertainties. This fell below the selected detectability cutoff of 10. Action: Do not promote 0.5 mA as an equivalent high-field setting. Conclusion: No mean shift was resolved, but equivalence remained unproven because detectability failed |
| TaP timing test, 2 K, +9 T | Evidence: Four 61-reading traces compared 1-s and 3-s time constants. Two traces per setting, three candidate delays for 1 s, two for 3 s and three window sizes produced analysis windows. The selected precision target was , subject to validity, correlation, batch-count, stationarity and repeatability checks. This target was a standard uncertainty, not a 95% interval half-width. Action: Retain all traces and withhold a faster production setting until all acceptance criteria pass. Conclusion: All 30 windows were inconclusive; unresolved repeatability was not treated as zero, and neither a timing setting nor a net time saving was qualified |
Supplementary Table 2: Examples of structured evidence-to-action decisions. TaP qualification metrics and acceptance criteria are defined in Section 2.2 .
Supplementary Fig. 4: Adaptive measurement planning and run closure in Co 2 MnGa. (a) Experimental decision sequence, from review of the initial 300 K survey to revised sampling, temperature-dependent acquisition and final-state checks. Purple denotes AI review and planning, blue denotes software execution, and yellow identifies human decisions or interventions. (b) Initial 300 K out-of-plane field sweep, acquired with 85 points per branch. (c–e) Subsequent measurements at 100, 20 and 2 K using the revised sampling protocol of 25 points and six repeats per point. Filled circles and open squares distinguish opposite field-sweep directions. (f) Zero-field resistance during the temperature sequence and final return to 300 K. The run concluded with recorded source-off and environmental return checks; the physical puck exchange remained pending.
Supplementary Fig. 5: Reference-assisted investigation of an unexpected transport response in Co 2 MnGa. (a) Diagnostic workflow initiated by an approximately 4% change in the measured in-phase response. The agent proposed a reference-resistor comparison and evaluated the results, while a human operator performed the required physical connections. Purple indicates AI review and planning, blue indicates software acquisition, and yellow indicates human intervention. (b) Initial sample measurement using the 20- A current-source range. (c) Reference-resistor measurement with the same range, exhibiting a comparable response. (d) Reference-resistor measurement using the 2- A range, showing a substantially reduced variation. (e) Sample remeasurement after restoring the original wiring and using the 2- A range. Blue circles and orange squares represent the two lock-in channels during 100–200–100 nA RMS excitation comparisons. The shaded region denotes the comparison margin. The diagnostic sequence supported retaining 100 nA but did not qualify 200 nA or uniquely establish the origin of the initial response.
| Campaign configuration | Recorded action envelope and interpretation |
| TaP AC transport | Environment: 2–301 K and 9\text{,}\mathrm{T} ; maximum rates $2\text{\,}\mathrm{K}\text{\,}{\mathrm{min}}^{-1}$ and $0.01\text{\,}\mathrm{T}\text{\,}{\mathrm{s}}^{-1}$ . Electrical: $I_{\mathrm{peak}}\leq$4.3\text{\,}\mathrm{mA} , compliance 0.1\text{,}\mathrm{V}$$ and configured estimated power . Run bound: 3,000 points, 50,000 readings and 24 h. The current ceiling was a conservative qualification boundary, not a demonstrated sample-safe limit |
| Co 2 MnGa out-of-plane Hall | Environment: 2–300 K and 9\text{,}\mathrm{T} ; maximum rates $3\text{\,}\mathrm{K}\text{\,}{\mathrm{min}}^{-1}$ and $0.02\text{\,}\mathrm{T}\text{\,}{\mathrm{s}}^{-1}$ . Electrical: $I_{\mathrm{peak}}\leq 0.30\,\mu\mathrm{A}$ , compliance $\leq$0.1\text{\,}\mathrm{V} and configured estimated power 10\text{,}\mathrm{nW}$$ . Run bound: 10,000 points, 100,000 readings and 24 h. Both voltage channels were read sequentially; a transverse channel label did not by itself establish Hall polarity |
| Co 2 MnGa in-plane nonlinear campaign | Environment: 2–300 K and 1\text{,}\mathrm{T} ; maximum rates $4\text{\,}\mathrm{K}\text{\,}{\mathrm{min}}^{-1}$ and $0.01\text{\,}\mathrm{T}\text{\,}{\mathrm{s}}^{-1}$ . Electrical: $I_{\mathrm{peak}}\leq$1.5\text{\,}\mathrm{mA} , compliance 5\text{,}\mathrm{V} and port-power screening $\leq$15\text{\,}\mathrm{mW} ; commissioned transport used at most RMS with a source-on timer. Run bound: 10,000 points, 100,000 readings and 6 h. Port-power screening was not a measurement of sample or contact dissipation |
Supplementary Table 3: Selected deployed action envelopes and experiment-specific gates.
| Observation class | Response and remaining limitation |
| Stale deployment, owner mismatch or unverified wiring map | Hold dispatch; reconcile worker, configuration and physical record. Unresolved: actual lead continuity, grounding and sample identity require independent evidence |
| Malformed or out-of-envelope plan | Reject before queueing and return the failed field or numerical bound. Scientific desirability cannot override a hard limit |
| Source compliance, overtemperature or inconsistent source state | Stop the affected run, preserve flags and attempt verified source-off. Unresolved: programmed current is not an independent measurement of sample current |
| SR860 input overload or reference loss during excitation | Reject affected readings, stop when required and use a new validated diagnostic plan. Unresolved: a range change can restore acquisition without identifying the physical cause |
| Chamber fault, non-finite or persistently unknown state | Pause or fault, attempt source-off and retain the last PPMS targets for explicit review. Unresolved: chamber mode is not a pressure measurement; electrical shutdown does not zero the magnet |
| Communication or worker fault | Preserve durable run state, block blind restart and reconcile ownership before a new run. Unresolved: process or bus failure can prevent a guaranteed shutdown; this is not an emergency-stop system |
Supplementary Table 4: Fault and data-quality classes used in the supervisory workflow.
Supplementary Fig. 6: Read-only campaign dashboard. Example Co 2 MnGa transport workspace showing the campaign objective, recorded-run progress, roadmap, cached temperature/field/source state, measurement tabs, individual field branches and provenance controls. The visible “telemetry unverified” and “estimate withheld” messages illustrate the freshness policy: the interface does not present historical cache values as a verified live instrument state. The dashboard has no instrument-control path.
Supplementary Fig. 7: Composition of the laboratory sequence corpus. (a) Number of .seq files by contributor directory. (b) Multi-label modality counts based on active command tokens; one file may contribute to several bars. (c) Direct case-insensitive material-name matches in sequence paths for the four manuscript examples. TaP and Co 2 MnGa therefore require explicit same-material exclusion in a held-out evaluation.
Supplementary Fig. 8: Agent activity and token usage in the PPMS project. (a) Cumulative token consumption by task category, including campaign monitoring and coordination, specialist review and implementation, measurement planning and execution, instrument diagnosis and qualification, and supporting activities. Values are reported in millions of tokens. (b) Allocation of total token usage across the Co 2 MnGa (CMG) and TaP, together with shared system and review activities. (c) Aggregate token composition, comprising 2.753 billion cached-input tokens (97.5%), 64.78 million uncached-input tokens (2.3%) and 7.00 million output tokens (0.2%). Colors distinguish cached input, uncached input and output across all panels. The statistics represent cumulative recorded model usage, including development and supporting activities, rather than independent reasoning steps or direct measures of scientific performance.
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