Search arXivSearch

arXiv subjects

Anastashia Jebraeilli

Publications and source records attributed to Anastashia Jebraeilli.

3 recordsLinked to original sources

Error Suppression in Distributed Quantum Computing with Heterogeneous-Distance Lattice Surgery

Distributed quantum computing requires fault-tolerant operations across inter-QPU links that can be substantially noisier than local gates. Uniformly increasing code distance provides additional protection but also enlarges data patches used for local storage and computation. Here, we introduce distributed heterogeneous-distance lattice surgery using an eight-data-patch ancilla-mediated (8-DAM) architecture, which will be useful for near-term quantum devices with less qubit overhead. In this architecture, the central ancilla spanning the inter-QPU boundary is enlarged while the data patches retain distance $d$. The protocol uses traveling stabilizers to suppress hook errors during merge and split operations between these unequal-distance patches. Circuit-level simulations of rotated surface codes at fixed local depolarizing noise show that logical-readout error rates depend only weakly on link noise. The resulting advantage over conventional lattice surgery grows as link errors increase. Comparisons with uniform distance implementations demonstrate comparable logical error suppression with reduced physical-qubit overhead. We also demonstrate how 8-DAM layouts support two simultaneous distributed logical CNOT operations between four logical data qubits using a single enlarged ancilla. At higher link noise, this construction yields lower logical error rates and more stability than two independent distributed logical CNOTs. These results support selective ancilla enlargement as a resource-efficient approach to fault-tolerant distributed quantum computing.

quant-ph

STQS: A Unified System Architecture for Spatial Temporal Quantum Sensing

Quantum sensing (QS) harnesses quantum phenomena to measure physical observables with extraordinary precision, sensitivity, and resolution. Despite significant advancements in quantum sensing, prevailing efforts have focused predominantly on refining the underlying sensor materials and hardware. Given the growing demands of increasingly complex application domains and the continued evolution of quantum sensing technologies, the present moment is the right time to systematically explore distributed quantum sensing architectures and their corresponding design space. We present STQS, a unified system architecture for spatiotemporal quantum sensing that interlaces four key quantum components: sensing, memory, communication, and computation. By employing a comprehensive gate-based framework, we systemically explore the design space of quantum sensing schemes and probe the influence of noise at each state in a sensing workflow through simulation. We introduce a novel distance-based metric that compares reference states to sensing states and assigns a confidence level. We anticipate that the distance measure will serve as an intermediate step towards more advanced quantum signal processing techniques like quantum machine learning. To our knowledge, STQS is the first system-level framework to integrate quantum sensing within a coherent, unified architectural paradigm. STQS provides seamless avenues for unique state preparation, multi-user sensing requests, and addressing practical implementations. We demonstrate the versatility of STQS through evaluations of quantum radar and qubit-based dark matter detection. To highlight the near-term feasibility of our approach, we present results obtained from IBM's Marrakesh and IonQ's Forte devices, validating key STQS components on present day quantum hardware.

quant-ph

Quantum simulation of a qubit with non-Hermitian Hamiltonian

Modeling non-Hermitian Hamiltonians is increasingly important in classical and quantum domains, especially when studying open systems, $PT$ symmetry, and resonances. However, the quantum simulation of these models has been limited by the extensive resources necessary in iterative methods with exponentially small postselection success probability. Here we employ a fixed-depth variational circuit to circumvent these limitations, enabling simulation deep into the $PT$-broken regime surrounding an exceptional point. Quantum simulations are carried out using IBM superconducting qubits. The results underscore the potential for variational quantum circuits and machine learning to push the boundaries of quantum simulation, offering new methods for exploring quantum phenomena with near-term intermediate-scale quantum technology.

quant-ph