Speaker
Description
In the last decade, a swift progress has been made in the coherent control of materials’ excitation beyond the optical cycle (1). In particular, it was established that multi-photon excitation (or tunneling) of electrons manifests itself as transient electronic levels (so-called laser dressing of electrons) in solids (2). This situation can also be described as an excitation of polaritons (2, 3), a coupled photon-electron quasi-particle that holds a high spatial and temporal coherence, inducing measurable consequences at the macroscopic scales (4, 5).
As a result, designing qubits based on the ultrafast response of condensed matter to light appears capable of holding the promise for a highly-controllable, scalable, and low consumption computational device.
In this poster, we present activities of the research group “Quantum Dynamics of Systems”, created in August 2024 and member of the Laboratory of Quantum Computing of IT4I. Its activities are split as follows.
(i) Bottom-up photonic qubit design. While the idea of using photons as qubit was well assessed in the past (6), recent progress made in polaritonic spectroscopy has advanced the possibility of a bosonic computer working at ambient temperature using widely available materials. A research direction of the group is to account for realistic experimental conditions in descriptions of light-matter interaction [in classical polaritonics (4, 5), in ab-initio density functional theory (1, 2, 7) and in quantum chemical formalisms (8)] in situations of strong light-matter coupling and in lossy regimes, in view of improving qubit lifetime and control, reducing the generation of errors, along with keeping a large hardware scalability.
(ii) Top-down qubit architecture. Superconducting qubits that will be hosted by IT4I (LUMI-Q) work using transmons, polaritonic states controlled using microwaves (i.e., at cm to meter wavelengths) in ultracold and vacuum conditions. Reaching a deep understanding of their functioning will be necessary to enable improvements and match adequacy of the future machines with market demands.
(iii) Quantum algorithmics. In the long term, quantum computing holds the promise to lower the exponential cost of quantum chemistry. However, recently proposed scaling laws predicted that millions of quantum gates would be necessary to enable ground-state simulations of quantum chemistry and density functional theory at the scale of few atoms (9). As a result, an assistance of quantum code development by machine learning appears necessary.
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