QuICS

Edge states—localized electronic states at the boundaries of a material—are often attributed to structural defects or topological features in crystalline solids. In finite 𝜋-conjugated systems such as graphene nanoribbons, boron nitride, and short segments of single-walled carbon nanotubes, these edge states can lead to electron scattering and fluorescence quenching. Computational studies have shown that certain chemical modifications, such as tailored edge-passivation and fullerene-end capping, can suppress these states.

While trapped ions are well-developed technologies for both quantum computation and simulation, incorporating them into nodes of a quantum network typically requires quantum frequency conversion (QFC). QFC extends the network's operating range given that most atomic ions emit polarization-entangled photons in the visible or near-infrared wavelengths.We demonstrate two-stage, polarization-preserving QFC for shifting Ba+ single photons upwards of 375 THz to the telecom O-band for quantum networking.

Recently, an experiment using a quantum processor realized a protocol for ‘Certified Randomness’, generating remotely verifiable randomness appealing for applications involving mutually untrusting parties. This protocol builds on the success of pushing the ability of quantum computers to perform beyond-classical computational tasks and leverages the classical hardness of sampling from random quantum circuits to certify 70 kbits of entropy against a realistic adversary using best-known attacks.

There has been increasing interest in classifying mixed quantum states with topological order, particularly in understanding when states connected by local noise channels remain in the same topological phase. This framework has recently been applied to topological quantum error-correcting codes, where the use of the Petz recovery map has shown that phase transitions in mixed states align with the decodability threshold of these codes. Motivated by these insights, we introduce a scalable, parallelized, quasi-local decoder that achieves near-optimal performance for topological codes.

Building large-scale modular quantum computers and quantum networks require high fidelity, high efficiency, and long lifetime quantum memories [1]. Quantum memories are proposed to increase photon-mediatated matter-qubit entanglment rates by synchronizing photon interference between network nodes [2]. Hybrid quantum networking leverages trapped ions’ high fidelity operations and neutral-atoms’ single photon manipulation for increased entanglement rates over single-species quantum networks [3-8].

In this talk, we will mainly focus on noisy quantum trees: at each node of a tree, a received qubit unitarily interacts with fresh ancilla qubits, after which each qubit is sent through a noisy channel to a different node in the next level. Therefore, as the tree depth grows, there is a competition between the irreversible effect of noise and the protection against such noise achieved by delocalization of information.

Many-body fermionic systems can be simulated in a hardware-efficient manner using a fermionic quantum processor. Neutral atoms trapped in optical potentials can realize such processors, where non-local fermionic statistics are guaranteed at the hardware level. Implementing quantum error correction in this setup is however challenging, due to the atom-number superselection present in atomic systems, that is, the impossibility of creating coherent superpositions of different particle numbers.