Quantum Cognitive Resonance Therapy: A Testable Research Path

Quantum cognitive resonance therapy would combine quantum-enabled brain sensing with timed neuromodulation to detect neural states and respond when intervention is most useful.

Today, this FutureSciences-defined integration is hypothetical rather than a clinical modality. Its pieces are real: wearable OPM-MEG records human brain fields (Boto et al., 2018), while adaptive stimulation can respond to biomarkers; no study has joined them in a quantum-enabled therapeutic loop.

The opportunity is timely because sensors are moving closer to the scalp, stimulation is becoming responsive, and governance has begun to address the special sensitivity of brain data. The scientific task is to determine whether integration adds measurable benefit over strong conventional alternatives.

What is Quantum Cognitive Resonance Therapy?

Quantum cognitive resonance therapy is a FutureSciences name for a research program that would link quantum-enabled neural measurement, an explicit model of brain state and precisely timed neuromodulation.

The word quantum refers first to enabling technology. Optically pumped magnetometers exploit atomic quantum states to detect magnetic fields, while nitrogen-vacancy defects in diamond can act as nanoscale magnetometers. Neither device implies that thought itself is a macroscopic quantum process. The word resonance must likewise be operational: it may mean matching stimulation to a measured oscillatory phase, frequency, network state or biomarker-defined window, not invoking an unspecified healing frequency.

A mature system would perform four linked functions. It would measure a neural signal, estimate a clinically relevant state, decide whether and when to intervene, and deliver a characterized stimulus under safety constraints. Every link would need a comparator. A quantum sensor would have to outperform electroencephalography, conventional MEG, implanted local-field-potential sensing or another suitable method on information gained, burden, reliability or clinical utility.

How it differs from nearby fields

Quantum neuroengineering studies quantum-enabled tools for neuroscience. Adaptive deep brain stimulation already uses neural feedback to adjust implanted stimulation. Temporal-interference stimulation seeks non-invasive access to deeper structures with ordinary electrical fields. Quantum cognition uses the mathematics of quantum probability to represent contextual judgments. Quantum cognitive resonance therapy would borrow from all four, but its identity depends on a complete measurement-to-intervention loop and a therapeutic question defined before the technology is chosen.

Why Quantum Cognitive Resonance Therapy matters

Quantum cognitive resonance therapy matters because many neurological and psychiatric symptoms vary faster than fixed treatment schedules can follow, while useful neural signals can be weak, distributed and obscured by motion or stimulation artifacts.

Continuous stimulation can treat severe disorders, but it may deliver energy during states that do not require it and can produce stimulation-related adverse effects. Responsive systems offer a different logic: measure first, intervene when a predefined state appears and stop when the target changes. In a blinded randomized crossover feasibility trial involving four men with Parkinson disease, personalized adaptive deep brain stimulation improved motor symptoms and quality of life compared with clinically optimized continuous stimulation (Oehrn et al., 2024). The result is encouraging but too small to establish general clinical superiority.

Quantum-enabled sensing could matter if it reveals information that current electrodes or optical methods miss. OPM-MEG places room-temperature sensors close to the head, improving fit across head sizes and allowing movement that conventional cryogenic MEG restricts. A portable or more motion-tolerant system could help study children, movement disorders and natural behavior. The decisive value, however, would not be sensor sensitivity alone. It would be better decisions: more reliable state classification, safer timing or improved outcomes.

The field also forces a useful standard on precision neuromodulation. “Personalized” should mean that a measured feature predicts when, where or how intervention helps an individual, and that this prediction survives prospective testing. Without that test, personalization can become a label attached after the result.

Foundations and history

Quantum cognitive resonance therapy inherits its foundations from biomagnetism, quantum sensing, systems neuroscience, neural engineering, control theory and clinical neuromodulation.

Disciplines of origin

Magnetoencephalography established that coordinated neuronal currents generate extracranial magnetic fields that can be measured non-invasively. Atomic physics supplied optically pumped magnetometers, which use light to prepare and read atomic spin states. Solid-state quantum sensing supplied nitrogen-vacancy centers in diamond. Neuroscience supplied oscillations, network dynamics and candidate biomarkers. Control engineering supplied feedback, latency analysis and stability. Clinical research supplied endpoints, randomization, blinding and adverse-event monitoring.

Milestones that changed the question

1. 2013: adaptive deep brain stimulation in advanced Parkinson disease showed that a physiological signal could control stimulation rather than merely record it (Little et al., 2013).
2. 2016: diamond quantum defects detected the magnetic signature of action potentials from a single isolated marine-worm axon preparation, establishing an experimental neural-magnetometry anchor (Barry et al., 2016).
3. 2017: temporal interference modulated deep neural activity in mice using superposed kilohertz electrical fields (Grossman et al., 2017).
4. 2018: wearable OPM-MEG recorded human brain activity while participants moved, bringing quantum sensors into functional neuroimaging (Boto et al., 2018).
5. 2021–2024: closed-loop stimulation reached individualized human demonstrations and randomized feasibility testing, while temporal interference produced human target-engagement results (Scangos et al., 2021; Violante et al., 2023; Oehrn et al., 2024).

These milestones do not form a single lineage of therapy. They show that sensing, state estimation and timed intervention have advanced far enough to make integration a testable engineering question.

How it would work

Quantum cognitive resonance therapy would operate as a closed loop in which measurement, inference and stimulation are evaluated as one causal system.

Measure the state

An OPM-MEG array could record millisecond-scale magnetic activity without cryogenic sensors. Diamond sensors might eventually contribute at cellular or tissue scales, but present neural demonstrations remain experimental. The signal would require calibration against movement, environmental fields, muscle activity and the electromagnetic artifact created by the stimulator itself.

Estimate a target

A model would transform sensor data into a prespecified state estimate: for example, the probability that a pathological oscillation, network transition or learning window is present. Classical signal processing and machine learning would be the reference methods. Quantum machine learning would enter only if a defined encoding and computation produced reproducible gains against tuned classical baselines.

Time the intervention

The controller would trigger, shape or withhold an already characterized intervention according to the state estimate and safety rules. “Resonance” could mean phase-locked delivery, frequency matching or response to a network pattern, but each meaning requires a measurable variable and an outcome that changes when timing is altered.

Learn without drifting

A therapeutic controller cannot change silently. Any adaptive model would need bounded updates, audit trails, uncertainty estimates and rules for reverting to a safe mode. The system should distinguish biological change from sensor drift, medication effects, fatigue and context. A feedback loop that adapts faster than it can be validated may optimize an artifact instead of a patient-relevant state.

What exists today

Quantum cognitive resonance therapy remains a hypothetical integration as of September 2026, while several of its foundations already operate in people or controlled experiments.

Established and emerging foundations

OPM-MEG is the strongest quantum-enabled human measurement anchor. The 2018 wearable system demonstrated recordings during movement, and later work expanded portability, wireless acquisition and validation across larger samples and tasks (Wang et al., 2024; Cheng et al., 2024). These systems still contend with ambient fields, sensor calibration and motion artifacts. They measure; they do not treat.

At a smaller scale, diamond magnetometry has detected neuronal magnetic signals in an isolated preparation. That result supports a sensing principle, not a path from nanodiamond to a human implant. At the intervention layer, adaptive deep brain stimulation has reached human feasibility studies, while temporal interference has modulated targeted deep-brain activity in healthy volunteers. The latter used 20 adults in an fMRI experiment and 21 in a behavioral experiment (Violante et al., 2023). A separate randomized, double-blind crossover study included 45 healthy participants across two experiments and reported striatal target engagement and motor-learning effects (Wessel et al., 2023).

How far the evidence reaches

The evidence supports separate capabilities, not a therapeutic chain. OPM-MEG does not yet provide a validated disease-specific trigger for stimulation. Diamond sensors do not yet offer clinical neural readout. Human temporal-interference studies demonstrate target engagement and short-term behavioral effects in healthy volunteers, not durable treatment. Adaptive DBS proves that closed-loop therapy is possible, but its sensing commonly comes from implanted electrodes rather than quantum sensors.

Who is building its foundations

Quantum cognitive resonance therapy has no single laboratory, but verified teams are building its measurement, control and governance foundations.

At the University of Nottingham, researchers including Elizabeth Boto, Matthew Brookes and colleagues developed wearable OPM-MEG and have advanced movement-compatible and portable platforms. Their work turns atomic magnetometry into a human neuroimaging instrument (Boto et al., 2018).

At the University of California, San Francisco, teams led by investigators including Philip Starr, Edward Chang and Katherine Scangos have developed biomarker-guided stimulation strategies in Parkinson disease and treatment-resistant depression. The depression report concerned one individual; its value is methodological—personalized mapping followed by chronic closed-loop delivery—not population-level efficacy (Scangos et al., 2021).

At Imperial College London and collaborating institutions, Nir Grossman and colleagues translated temporal interference from animal work toward controlled human experiments. Their studies provide tools for testing deep target engagement without surgical electrodes, while leaving clinical effectiveness open (Violante et al., 2023).

Quantum-sensing groups associated with Harvard, MIT and collaborating laboratories demonstrated diamond-based neuronal magnetometry in experimental preparations. The relevant contribution is the measurement result, not an institutional claim to a therapy (Barry et al., 2016).

Governance is also a foundation. The OECD Recommendation on Responsible Innovation in Neurotechnology, adopted in 2019, calls for safety, stewardship, inclusion and protection of personal brain data. UNESCO adopted its Recommendation on the Ethics of Neurotechnology in November 2025, giving human dignity, autonomy and rights a global policy frame.

What is missing: breakthroughs still required

Quantum cognitive resonance therapy needs five advances that can be tested independently before integration is justified.

Knowledge gap: define the therapeutic state

The field needs a disease-specific state that is causal, measurable and useful for deciding when to intervene. A correlation with symptoms is insufficient. Investigators must show that the biomarker predicts response prospectively, transfers across sessions and adds information beyond medication status, behavior and conventional sensors. “Resonance” must be reduced to a variable such as phase, frequency or network configuration.

Demonstration gap: close the sensor-to-stimulator loop

No experiment has shown a quantum-enabled brain sensor driving therapeutic neuromodulation in real time. The first demonstration should use a predefined signal, fixed decision rules and a characterized stimulation method. Offline analysis cannot establish that the live loop meets latency, reliability or artifact requirements.

Engineering gap: separate brain signal from system artifact

The sensor must operate while a stimulator changes the electromagnetic environment. Motion, muscles, mains interference, coil currents and stimulation transients can resemble neural change. Progress requires calibration standards, shared artifact datasets, independent replication and failure detection that can suspend stimulation safely.

Clinical gap: prove incremental benefit

A quantum-enabled loop must be compared with optimized continuous stimulation, conventional closed-loop sensing and sham or yoked timing where appropriate. Trials must measure durable patient-relevant outcomes, not only signal changes. Adverse events, treatment burden, false triggers and reasons for withdrawal are part of the result.

Governance gap: protect agency and brain data

Neural measurements can reveal health, attention, intention or identity-linked patterns. Consent must cover model reuse, secondary inference and device updates. Systems need cybersecurity, access controls, explainable intervention logs and routes for patients to pause or contest adaptation. Access plans must prevent scarce infrastructure from widening disparities.

How it is studied: methods, data and validation

Quantum cognitive resonance therapy should be studied as a causal measurement-and-control problem, not as a collection of impressive devices.

Instruments and datasets

OPM-MEG, conventional MEG, electroencephalography, implanted local-field-potential recordings, functional MRI and behavioral tasks can measure complementary scales. Calibration recordings should include empty-room noise, controlled movement, muscle activity and stimulation-on artifacts. Open datasets need synchronized raw signals, sensor geometry, preprocessing decisions, uncertainty and adverse-event annotations.

Models and comparators

State decoders should be trained on one partition and evaluated on held-out people, sessions and sites. Useful comparators include simple spectral thresholds, linear models, modern classical machine learning and clinical decision rules. Quantum machine learning should not receive a weaker baseline. Data can make classical learners competitive even when the underlying computation appears difficult (Huang et al., 2021), and QML still faces trainability and benchmarking problems (Cerezo et al., 2022).

Validation sequence

1. Establish sensor validity against a reference signal and quantify motion, drift and stimulation artifacts.
2. Validate the biomarker out of sample and report calibration, sensitivity, specificity and decision latency.
3. Test target engagement with blinded active, sham and timing-control conditions.
4. Compare the integrated loop against conventional sensing and the best current treatment.
5. Replicate across sites before claiming transportability.

Results that would weaken the program

The approach should be reconsidered if quantum sensing adds no stable information after artifact control, if the biomarker fails across sessions, if closed-loop timing does not outperform yoked or sham timing, if conventional sensors perform equally with less burden, or if adverse effects erase the expected benefit. A claimed QML gain should also disappear from the evidence map if tuned classical baselines or realistic noise remove it.

How feasible it is: distance and roadmap

Quantum cognitive resonance therapy is scientifically approachable because each link can be tested, but feasibility depends on whether integration creates benefit that simpler systems cannot deliver.

Current distance

The shortest route does not require a quantum computer or a new theory of consciousness. It begins with OPM-MEG as the quantum-enabled sensor, a classical state decoder and an already characterized non-invasive stimulation method. This route isolates the value of measurement from more distant claims about quantum algorithms or intracellular sensing.

On the Technology Readiness Level scale, which runs from basic principles at TRL 1 to an operational system at TRL 9 (NASA, 2023), the most advanced OPM-MEG platforms can be described cautiously as roughly TRL 5–6: prototypes have been validated in relevant research environments, but routine clinical deployment is limited. This is an editorial estimate, not a formal certification. The integrated therapy sits earlier because no complete prototype has been demonstrated.

Roadmap by stage

Next verifiable milestone

The next milestone is a preregistered, blinded crossover human feasibility study in which OPM-MEG detects a predefined neural state and triggers an already characterized non-invasive stimulation protocol. The same intervention should also be delivered under conventional-sensor guidance and sham or yoked timing. The primary test is reproducible target engagement within prespecified latency and artifact limits; safety and exploratory cognitive or clinical outcomes are secondary.

Signals of progress and reasons to rethink

Progress would appear as cross-session biomarker stability, lower false-trigger rates, successful operation during movement, open replication and a measurable gain over conventional sensing. The program should narrow or change direction if performance collapses outside one laboratory, if the loop cannot distinguish stimulation artifact from biology, or if added infrastructure produces no meaningful outcome.

Conditional horizons

• Under five years, medium confidence: better OPM-MEG validation, shared artifact benchmarks and laboratory closed-loop demonstrations are plausible if engineering and data standards converge.
• Five to fifteen years, low confidence: small clinical feasibility programs could emerge if a disease-specific biomarker and safe intervention pass comparative tests.
• Beyond fifteen years, low confidence: integrated clinical systems would require durable benefit, routine calibration, regulation, cybersecurity, trained teams and acceptable cost.

These horizons describe conditions, not arrival dates. Failure at an early stage should redirect resources before clinical claims expand.

Applications

Quantum cognitive resonance therapy could contribute first as a research instrument and only later, if comparative trials succeed, as a clinical system.

Uses already present in neighboring fields

OPM-MEG already supports movement-compatible functional brain measurement. Adaptive DBS already adjusts stimulation using recorded physiological signals. Temporal interference offers an experimental route to deep target engagement without implanted electrodes. These neighboring uses can improve experimental design even if they never merge into one product.

Near-term opportunities

A practical near-term application is state-dependent neuroscience: testing whether stimulation delivered at one measured network phase changes learning, movement or symptoms more than the same stimulation at another phase. Another is presurgical or diagnostic mapping for people who cannot remain still in conventional scanners. A third is comparative biomarker research, where OPM-MEG and standard sensors are recorded together to determine which signals generalize.

Longer-term possibilities

If measurement and control prove additive, future systems could support responsive rehabilitation, movement-disorder management, seizure-network research or carefully bounded psychiatric studies. Each indication would require its own biomarker, endpoint and risk analysis. One platform cannot inherit efficacy from another disorder merely because both involve oscillations.

Quantum cognition may help model context-dependent choices or symptom reports, but mathematical fit is not a treatment mechanism. Likewise, quantum machine learning could be explored for specific decoding tasks only after the task, data access and strong classical baselines are fixed. Fair optimization benchmarks show why progress should be judged with model-independent comparisons rather than platform labels (Koch et al., 2026).

Risks, ethics and governance

Quantum cognitive resonance therapy would join intimate neural data to an intervention, so errors could affect both what a system infers and what it does.

Physical and clinical risk

Risks include stimulation-related adverse effects, false triggers, missed events, interactions with medication or implants and delayed access to effective care. Non-invasive does not mean risk-free. Low-intensity electrical stimulation requires screening, dosing discipline, adverse-event reporting and regulatory oversight (Antal et al., 2017). An adaptive controller adds software failure and model drift to the device risk.

Mental privacy and inference

Raw neural data may be less revealing than marketing suggests, yet combined datasets can support inferences about health, attention or behavior. Governance should treat the full pipeline—signals, features, labels, model outputs and logs—as sensitive. Participants need to know which secondary inferences are prohibited and whether data can train future models.

Autonomy and identity

A responsive device can change mood, movement or cognition while learning from the same person. Patients should retain meaningful control over goals, adaptation and discontinuation. Clinicians need interpretable logs showing why stimulation occurred. Consent is an ongoing process when software, biomarkers or intended uses change.

Cybersecurity and dual use

A compromised controller could expose data, alter settings or suppress warnings. Security therefore belongs in the scientific design: authenticated updates, least-privilege access, tamper-evident records and safe degradation. Dual-use concerns include covert monitoring, coercive optimization and deployment outside health care without equivalent safeguards.

Equity and evidence

Quantum sensors and shielded facilities can be expensive. If studies recruit narrow populations, models may work poorly across age, anatomy, disability or cultural context. Procurement should not outrun comparative effectiveness. The OECD and UNESCO frameworks support anticipatory governance, public deliberation, inclusion and protection of human dignity before deployment, not after harm appears.

How to study Quantum Cognitive Resonance Therapy and contribute

Quantum cognitive resonance therapy can be approached through existing disciplines rather than a degree with that name.

Education pathways

At undergraduate level, useful foundations include physics, electrical engineering, biomedical engineering, computer science, neuroscience, psychology, statistics or ethics. Graduate work can specialize in atomic magnetometry, biomagnetism, signal processing, neural engineering, clinical trials, control systems, computational neuroscience or science and technology policy. Clinical leadership requires the appropriate medical and professional training; technical expertise does not substitute for patient-care competence.

Skills that connect the field

• Electromagnetism, quantum sensing and sensor calibration
• Neural time-series analysis, source reconstruction and uncertainty
• Control theory, real-time software and safety engineering
• Study design, blinding, preregistration and causal inference
• Clinical endpoints, adverse-event assessment and human factors
• Privacy engineering, cybersecurity, research ethics and regulation

Careers available now

Current roles include quantum-sensor engineer, MEG physicist, neural-signal scientist, neuromodulation researcher, clinical neuroengineer, biostatistician, regulatory scientist, neuroethicist and medical-device security specialist. Future roles may combine these skills, but teams will remain more credible than a single person claiming mastery of every layer.

Where to begin today

Start with reproducible comparisons. Reanalyze an open MEG dataset, build an artifact-detection benchmark, compare a simple decoder with a more complex one, or audit whether a neuromodulation paper separates target engagement from clinical benefit. Study the wider FutureSciences domain map and its evidence classification. A useful contribution is often a negative result that prevents an attractive but unstable biomarker from entering a feedback loop.

Open questions for future researchers

Quantum cognitive resonance therapy becomes useful as a research agenda when its unknowns are framed as answerable questions.

1. Which neural states predict a change in symptoms or learning strongly enough to justify real-time intervention?
2. Does OPM-MEG add stable, decision-relevant information beyond EEG, conventional MEG or implanted sensing under realistic movement?
3. Can a stimulation artifact be separated from the neural response quickly enough for safe feedback control?
4. Which definition of resonance—phase, frequency, network mode or plasticity window—produces a causal and reproducible effect?
5. Can any quantum machine-learning method outperform tuned classical baselines on held-out neural data after hardware cost and noise are counted?
6. What consent, data architecture and oversight let people benefit from adaptive neurotechnology without losing control over neural information?

Frequently asked questions

Quantum cognitive resonance therapy raises practical questions about what is real, what the name means and how evidence should be judged.

Does Quantum Cognitive Resonance Therapy exist yet?

OPM-MEG, diamond neural magnetometry, adaptive deep brain stimulation and human temporal-interference experiments already exist. Quantum cognitive resonance therapy itself does not yet exist as a demonstrated clinical system; it is a proposed integration whose decisive test is whether quantum-enabled sensing improves a closed-loop intervention beyond conventional sensing, optimized treatment and sham.

What makes the approach quantum?

The most defensible quantum component is the sensor physics: atomic spins in optically pumped magnetometers or quantum defects in diamond. Quantum probability may also model contextual decisions, and quantum computing may be tested for specific algorithms. None of these uses proves that cognition depends on coherent quantum processing in the brain (Pothos & Busemeyer, 2022).

Is this a treatment people can receive?

No integrated treatment is available. Some neighboring interventions, including deep brain stimulation, are used clinically for defined indications, while adaptive protocols and temporal interference remain at different research and regulatory stages. Anyone seeking care should discuss established options with qualified clinicians rather than infer availability from a proposed research name.

Could quantum sensors read thoughts?

Quantum sensors measure physical signals, not thoughts as sentences. OPM-MEG records magnetic fields associated with population-level neural currents, and interpretation depends on models, tasks and noisy context. Some mental or health-related states may become statistically inferable, which is why neural data deserve strong privacy protections, but broad claims of direct mind reading exceed current evidence.

How is it different from adaptive deep brain stimulation?

Adaptive deep brain stimulation typically senses local neural activity through implanted electrodes and adjusts stimulation using a defined biomarker. Quantum cognitive resonance therapy would test whether an external or different quantum-enabled sensor adds information and whether timing around a measured network state improves results. Adaptive DBS is therefore both a foundation and a demanding comparator.

What experiment would move the field forward most?

A blinded crossover feasibility study should compare identical non-invasive stimulation under OPM-MEG guidance, conventional-sensor guidance and sham or yoked timing. The study should preregister the biomarker, target-engagement endpoint, latency and artifact limits. A clear incremental result would justify larger trials; equivalence would redirect effort toward the simpler sensing method.

Related future sciences

Quantum cognitive resonance therapy sits among FutureSciences fields that test its sensors, timing, materials and theories of experience.

• Quantum Neuroengineering examines quantum-enabled tools for measuring and modeling the brain.
• Neuro-Temporal Plasticity Engineering asks when neural systems are most able to change.
• Quantum Neurosynaptic Engineering explores quantum materials and sensing at synaptic and neuromorphic interfaces.
• Consciousness Engineering addresses measurement, intervention and governance around conscious states.

References and further reading

These sources support the evidence map, feasibility assessment, methods, and governance discussion for Quantum Cognitive Resonance Therapy.

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2. Barry, J. F., Turner, M. J., Schloss, J. M., Glenn, D. R., Song, Y., Lukin, M. D., Park, H., & Walsworth, R. L. (2016). Optical magnetic detection of single-neuron action potentials using quantum defects in diamond. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.1601513113. A correction was published in 2017.
3. Wang, X., Teng, P., Meng, Q., Jiang, Y., Wu, J., Li, T., Wang, M., Guan, Y., Zhou, J., Sheng, J., Gao, J. H., & Luan, G. (2024). Performance of optically pumped magnetometer magnetoencephalography: validation in large samples and multiple tasks. Journal of Neural Engineering. https://doi.org/10.1088/1741-2552/ad9680.
4. Schofield, H., Hill, R. M., Feys, O., Holmes, N., Osborne, J., Doyle, C., Bobela, D., Corvilain, P., Wens, V., Rier, L., Bowtell, R., Ferez, M., Mullinger, K. J., Coleman, S., Rhodes, N., Rea, M., Tanner, Z., Boto, E., de Tiège, X., …, & Brookes, M. J. (2024). A novel, robust, and portable platform for magnetoencephalography using optically-pumped magnetometers. Imaging Neuroscience. https://doi.org/10.1162/imag_a_00283.
5. Oehrn, C. R., Cernera, S., Hammer, L. H., Shcherbakova, M., Yao, J., Hahn, A., Wang, S., Ostrem, J. L., Little, S., & Starr, P. A. (2024). Chronic adaptive deep brain stimulation versus conventional stimulation in Parkinson’s disease: a blinded randomized feasibility trial. Nature Medicine. https://doi.org/10.1038/s41591-024-03196-z.
6. Scangos, K. W., Khambhati, A. N., Daly, P. M., Makhoul, G. S., Sugrue, L. P., Zamanian, H., Liu, T. X., Rao, V. R., Sellers, K. K., Dawes, H. E., Starr, P. A., Krystal, A. D., & Chang, E. F. (2021). Closed-loop neuromodulation in an individual with treatment-resistant depression. Nature Medicine. https://doi.org/10.1038/s41591-021-01480-w.
7. Little, S., Pogosyan, A., Neal, S., Zavala, B., Zrinzo, L., Hariz, M., Foltynie, T., Limousin, P., Ashkan, K., FitzGerald, J., Green, A. L., Aziz, T. Z., & Brown, P. (2013). Adaptive deep brain stimulation in advanced Parkinson disease. Annals of Neurology. https://doi.org/10.1002/ana.23951.
8. Violante, I. R., Alania, K., Cassarà, A. M., Neufeld, E., Acerbo, E., Carron, R., Williamson, A., Kurtin, D. L., Rhodes, E., Hampshire, A., Kuster, N., Boyden, E. S., Pascual-Leone, A., & Grossman, N. (2023). Non-invasive temporal interference electrical stimulation of the human hippocampus. Nature Neuroscience. https://doi.org/10.1038/s41593-023-01456-8.
9. Wessel, M. J., Beanato, E., Popa, T., Windel, F., Vassiliadis, P., Menoud, P., Beliaeva, V., Violante, I. R., Abderrahmane, H., Dzialecka, P., Park, C. H., Maceira-Elvira, P., Morishita, T., Cassara, A. M., Steiner, M., Grossman, N., Neufeld, E., & Hummel, F. C. (2023). Noninvasive theta-burst stimulation of the human striatum enhances striatal activity and motor skill learning. Nature Neuroscience. https://doi.org/10.1038/s41593-023-01457-7.
10. Grossman, N., Bono, D., Dedic, N., Kodandaramaiah, S. B., Rudenko, A., Suk, H. J., Cassara, A. M., Neufeld, E., Kuster, N., Tsai, L. H., Pascual-Leone, A., & Boyden, E. S. (2017). Noninvasive Deep Brain Stimulation via Temporally Interfering Electric Fields. Cell. https://doi.org/10.1016/j.cell.2017.05.024.
11. Grover, S., Fayzullina, R., Bullard, B. M., Levina, V., & Reinhart, R. M. G. (2023). A meta-analysis suggests that tACS improves cognition in healthy, aging, and psychiatric populations. Science Translational Medicine. https://doi.org/10.1126/scitranslmed.abo2044.
12. Pothos, E. M., & Busemeyer, J. R. (2022). Quantum Cognition. Annual Review of Psychology. https://doi.org/10.1146/annurev-psych-033020-123501.
13. Cerezo, M., Verdon, G., Huang, H. Y., Cincio, L., & Coles, P. J. (2022). Challenges and opportunities in quantum machine learning. Nature Computational Science. https://doi.org/10.1038/s43588-022-00311-3.
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17. Antal, A., Alekseichuk, I., Bikson, M., Brockmöller, J., Brunoni, A., Chen, R., Cohen, L., Dowthwaite, G., Ellrich, J., Flöel, A., Fregni, F., George, M., Hamilton, R., Haueisen, J., Herrmann, C., Hummel, F., Lefaucheur, J., Liebetanz, D., Loo, C., …, & Paulus, W. (2017). Low intensity transcranial electric stimulation: Safety, ethical, legal regulatory and application guidelines. Clinical Neurophysiology. https://doi.org/10.1016/j.clinph.2017.06.001.
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Explore. Discover. Transcend.

Quantum cognitive resonance therapy begins with a physical fact: brain activity produces signals that new sensors can measure with increasing flexibility.

Discover whether those measurements can identify a state that makes an intervention more precise, safer or more effective—and whether the gain survives comparison with simpler technology.

Transcend labels by testing the whole chain. The concrete next step is a preregistered blinded crossover study of OPM-MEG-guided stimulation against conventional sensing and sham.