Quantum technology has the potential to transform scientific discovery, but quantum advantages often require processing capabilities well beyond the reach of experimental platforms.We show that coupling a single controllable qubit to an otherwise conventional sensor can exponentially reduce the number of measurements required to learn classical signals. These rigorous quantum advantages apply to fundamental sensing tasks, including learning Fourier coefficients, extracting temporal correlations from time-varying signals, and estimating transformations of physical observables. Using a superconducting cavity–qubit architecture, we experimentally demonstrate 107-fold reductions in the number of measurements required for Fourier-amplitude and time-varying signal learning. Our quantum feature sensing algorithms further enable orders-of-magnitude improvements in simulations of weak-signal dark matter detection and wireless communication applications. These quantum advantages are derived from Quantum Phase-Space Inference (QΨ), a unifying theory of quantum-enhanced experiments that simultaneously converts a set of experimental objectives and constraints into tight lower bounds and optimal quantum-enhanced learning algorithms while producing a certificate of quantum advantage. QΨ extends beyond the regimes captured by quantum Fisher information and provides a framework for systematically identifying rigorous quantum advantages in practical experimental tasks. Together, our results establish that near-term quantum technology can exponentially enhance our ability to learn from classical signals.
A positron is equivalent to an electron traveling backward through time. Casting transmon superconducting qubits as akin to electrons, we simulate a positron with a transmon subjectto particular resonant and off-resonant drives. We call positron-like transmons „antiqubits.“ An antiqubit’s effective gyromagnetic ratio equals the negative of a qubit’s. This fact enables us to time-invert a unitary implemented on a transmon by its environment. We apply this platform-specific unitary inversion, with qubit–antiqubit entanglement, to achieve a quantum advantage in phase estimation: consider measuring the strength of a field that points in an unknown direction. An entangled qubit–antiqubit sensor offers the greatest possible sensitivity (amount of Fisher information), per qubit, per application of the field. We prove this result theoretically and observe it experimentally. This work shows how antimatter, whether real or simulated, can enable platform-specific unitary inversion and benefit quantum information processing.
We utilize a superconducting qubit processor to experimentally probe the transition from non-Markovian to Markovian dynamics of an entangled qubit pair. We prepare an entangled statebetween two qubits and monitor the evolution of entanglement over time as one of the qubits interacts with a small quantum environment consisting of an auxiliary transmon qubit coupled to its readout cavity. We observe the collapse and revival of the entanglement as a signature of quantum memory effects in the environment. We then engineer the non-Markovianity of the environment by populating its readout cavity with thermal photons to show a transition from non-Markovian to Markovian dynamics, reaching a regime where the quantum Zeno effect creates a decoherence-free subspace that effectively stabilizes the entanglement between the qubits.
While quantum measurement theories are built around density matrices and observables, the laws of thermodynamics are based on processes such as are used in heat engines and refrigerators.The study of quantum thermodynamics fuses these two distinct paradigms. In this article, we highlight the usage of quantum process matrices as a unified language for describing thermodynamic processes in the quantum regime. We experimentally demonstrate this in the context of a quantum Maxwell’s demon, where two major quantities are commonly investigated; the average work extraction ⟨W⟩ and the efficacy γ which measures how efficiently the feedback operation uses the obtained information. Using the tool of quantum process matrices, we develop the optimal feedback protocols for these two quantities and experimentally investigate them in a superconducting circuit QED setup.