To test the effectiveness of a drug one can advice two randomly selected groups of patients to take or not to take it, respectively. It is well-known that the causal effect cannot be identified if not all patients comply. This holds even when the non-compliers can be identified afterwards since latent factors like patient's personality can influence both his decision and his physical response. However, one can still give bounds on the effectiveness of the drug depending on the rate of compliance. Remarkably, the proofs of these bounds given in the literature rely on models that represent all relevant latent factors (including noise) by hidden classical variables. In strong analogy to the violation of Bell's inequality, some of these bounds fail if patient's behavior is influenced by latent quantum processes (e.g. in his nervous system). Quantum effects could fake an increase of the recovery rate by about 13% although the drug would hurt as many patients as it would help if everyone took it. The other bounds are true even in the quantum case. We do not present any realistic model showing this effect, we only point out that the physics of decision making could be relevant for the causal interpretation of every-day life statistical data.
To test the effectiveness of a drug one can advice two randomly selected groups of patients to take or not to take it, respectively. It is well-known that the causal effect cannot be identified if not all patients comply. This holds even when the non-compliers can be identified afterwards since latent factors like patient's personality can influence both his decision and his physical response. However, one can still give bounds on the effectiveness of the drug depending on the rate of compliance. Remarkably, the proofs of these bounds given in the literature rely on models that represent all relevant latent factors (including noise) by hidden classical variables. In strong analogy to the violation of Bell's inequality, some of these bounds fail if patient's behavior is influenced by latent quantum processes (e.g. in his nervous system). Quantum effects could fake an increase of the recovery rate by about 13% although the drug would hurt as many patients as it would help if everyone took it. The other bounds are true even in the quantum case. We do not present any realistic model showing this effect, we only point out that the physics of decision making could be relevant for the causal interpretation of every-day life statistical data.
To test the effectiveness of a drug, the medical researcher can instruct two randomly selected groups of patients to take the drug or not to take it. Each group should comply with the instructions exactly or else the causal effect cannot be identified satisfactorily. This holds true even when those who do not comply are identified afterwards, since latent factors such as patient's personality can influence both his decision and his physical response. However, one can still give bounds on the effectiveness of the drug depending on the rate of compliance. They are described by 16 inequalities. Remarkably, the proofs for these bounds given in the literature rely on models that represent all relevant latent factors influencing patient's behavior by hidden classical variables. In a strong analogy to the violation of Bell's inequality, half of the inequalities fail if patient behavior is influenced by latent quantum processes (e.g. in his nervous system). Quantum effects could fake an increase in the recovery rate by about 13%, although the drug would hurt as many patients as it would help if everyone took it. We show that the other eight inequalities remain true even in the quantum case. We do not present a realistic model showing the above effect. We only point out that the physics of decision-making could be relevant for the causal interpretation of every-day life statistical data. Our derivation of the remaining eight inequalities suggests how to avoid problematic hidden-variable models in classical causal reasoning by representing all latent factors by quantum systems.
Since Bell's theorem, it is known that local realism fails to explain quantum phenomena. Bell inequality violations manifestly show the incompatibility of quantum theory with classical notions of cause and effect. As recently found, however, the instrumental scenario-a pivotal tool in causal inference-allows for nonclassicality signatures going beyond this paradigm. If we are not limited to observational data and can intervene in our setup, then we can witness quantum violations of classical bounds on the causal influence among the involved variables even when no Bell-like violation is possible. That is, through interventions, the quantum behavior of a system that would seem classical can be demonstrated. Using a photonic setup-faithfully implementing the instrumental causal structure and switching between observation and intervention run by run-we experimentally witness such a nonclassicality. We also test quantum bounds for the causal influence, showing that they provide a reliable tool for quantum causal modeling.
Bell famously showed that it was possible to adjudicate between quantum theory and classical hidden-variable theories on experimental grounds rather than merely philosophical ones. These days, there is a new dispute, regarding the question of how to causally account for Bell correlations, with some suggesting a quantum common cause, others a classical superluminal influence. Here, the authors propose a way to move beyond philosophical disputes by comparing the predictive power of different models. They show that the classical models actually tend to overfit Bell test data.
Explaining observations in terms of causes and effects is central to empirical science. However, correlations between entangled quantum particles seem to defy such an explanation. This implies that some of the fundamental assumptions of causal explanations have to give way. We consider a relaxation of one of these assumptions, Bell's local causality, by allowing outcome dependence: a direct causal influence between the outcomes of measurements of remote parties. We use interventional data from a photonic experiment to bound the strength of this causal influence in a two-party Bell scenario, and observational data from a Bell-type inequality test for the considered models. Our results demonstrate the incompatibility of quantum mechanics with a broad class of nonlocal causal models, which includes Bell-local models as a special case. Recovering a classical causal picture of quantum correlations thus requires an even more radical modification of our classical notion of cause and effect.
Background Quantum computing and quantum machine learning (QML) are promising experimental technologies that can improve precision medicine applications by reducing the computational complexity of algorithms driven by big, unstructured, real-world data. The clinical problem of knee osteoarthritis is that, although some novel therapies are safe and effective, the response is variable, and defining the characteristics of an individual who will respond remains a challenge. In this study, we tested a quantum neural network (QNN) application to support precision data-driven clinical decisions to select personalized treatments for advanced knee osteoarthritis. Methodology After obtaining patients' consent and Research Ethics Committee approval, we collected the clinicodemographic data before and after the treatment from 170 patients eligible for knee arthroplasty (Kellgren-Lawrence grade ≥3, Oxford Knee Score (OKS) ≤27, age ≥64 years, and idiopathic aetiology of arthritis) treated over a two-year period with a single injection of microfragmented fat. Gender classes were balanced (76 males and 94 females) to mitigate gender bias. A patient with an improvement ≥7 OKS was considered a responder. We trained our QNN classifier on a randomly selected training subset of 113 patients to classify responders from non-responders (73 responders and 40 non-responders) in pain and function at one year. Outliers were hidden from the training dataset but not from the validation set. Results We tested our QNN classifier on a randomly selected test subset of 57 patients (34 responders, 23 non-responders) including outliers. The no information rate was 0.59. Our application correctly classified 28 responders out of 34 and 6 non-responders out of 23 (sensitivity = 0.82, specificity = 0.26, F1 Statistic = 0.71). The positive and negative likelihood ratios were 1.11 and 0.68, respectively. The diagnostic odds ratio was 2. Conclusions Preliminary results on a small validation dataset showed that QML applied to data-driven clinical decisions for the personalized treatment of advanced knee osteoarthritis is a promising technology to reduce computational complexity and improve prognostic performance. Our results need further research validation with larger, real-world unstructured datasets, as well as clinical validation with an artificial intelligence clinical trial to test model efficacy, safety, clinical significance, and relevance at a public health level.
BACKGROUND: Factors that participate in the biological changes associated with a placebo are not completely understood. Natural evolution, mean regression, concomitant procedures and other non specific effects are well-known factors that contribute to the "placebo effect". In this article, we suggest that quantum-like correlations predicted by a probabilistic modeling could also play a role. RESULTS: An elementary experiment in biology or medicine comparing the biological changes associated with two placebos is modeled. The originality of this modeling is that experimenters, biological system and their interactions are described together from the standpoint of a participant who is uninvolved in the measurement process. Moreover, the small random probability fluctuations of a "real" experiment are also taken into account. If both placebos are inert (with only different labels), common sense suggests that the biological changes associated with the two placebos should be comparable. However, the consequence of this modeling is the possibility for two placebos to be associated with different outcomes due to the emergence of quantum-like correlations. CONCLUSION: The association of two placebos with different outcomes is counterintuitive and this modeling could give a framework for some unexplained observations where mere placebos are compared (in some alternative medicines for example). This hypothesis can be tested in blind trials by comparing local vs. remote assessment of correlations.