September 1728

Quantum theory is notoriously counterintuitive, and yet remains entirely self-consistent when applied universally. Here we uncover a new manifestation of its unusual consequences. We demonstrate, theoretically and experimentally (by means of polarization-encoded single-photon qubits), that Heisenberg’s uncertainty principle leads to the impossibility of stringing together logical deductions about outcomes of consecutive non-compatible measurements. This phenomenon resembles the geometry of a Penrose triangle, where each corner is locally consistent while the global structure is impossible. Besides this, we show how overlooking this non-trivial logical structure leads to the erroneous possibility of distinguishing non-orthogonal states with a single measurement.

While the quantum mutual information is a fundamental measure of quantum information, it is only defined for spacelike-separated quantum systems. Such a limitation is not present in the theory of classical information, where the mutual information between two random variables is well-defined irrespective of whether or not the variables are separated in space or separated in time. Motivated by this disparity between the classical and quantum mutual information, we employ the pseudo-density matrix formalism to define a simple extension of quantum mutual information into the time domain. As in the spatial case, we show that such a notion of quantum mutual information in time serves as a natural measure of correlation between timelike-separated systems, while also highlighting ways in which quantum correlations distinguish between space and time. We also show how such quantum mutual information is time-symmetric with respect to quantum Bayesian inversion, and then we conclude by showing how mutual information in time yields a Holevo bound for the amount of classical information that may be extracted from sequential measurements on an ensemble of quantum states.

The study of non-classicality is essential to understand the quantum-to-classical transition in physical systems. Recently a witness of non-classicality has been proposed, linking the ability of a system (“the mediator”) to create quantum correlations between two quantum probes with its non-classicality, intended as the existence of at least two non-commuting variables. Here we propose a new inequality that quantitatively links the increase in quantum correlations between the probes to the degree of non-commutativity of the mediator’s observables. We test the inequality for various degrees of non-classicality of the mediator, from fully quantum to fully classical. This quantum-to-classical transition is simulated via a phase-flip channel applied to the mediator, inducing an effective reduction of the non-commutativity of its variables. Our results provide a general framework for witnessing non-classicality, quantifying the non-classicality of a system via its intrinsic properties (such as its Hilbert space dimension and observable commutators) beyond the specifics of interaction dynamics.

We present a method for gradient computation in quantum algorithms implemented on linear optical quantum computing platforms. While parameter-shift rules have become a staple in qubit gate-based quantum computing for calculating gradients, their direct application to photonic platforms has been hindered by the non-unitary nature of differentiated phase-shift operators in Fock space. We introduce a photonic parameter-shift rule that overcomes this limitation, providing an exact formula for gradient computation in linear optical quantum processors. Our method scales linearly with the number of input photons and utilizes the same parameterized photonic circuit with shifted parameters for each evaluation. This advancement bridges a crucial gap in photonic quantum computing, enabling efficient gradient-based optimization for variational quantum algorithms on near-term photonic quantum processors. We demonstrate the efficacy of our approach through numerical simulations in quantum chemistry and generative modeling tasks, showing superior optimization performance as well as robustness to noise from finite sampling and photon distinguishability compared to other gradient-based and gradient-free methods.