Time-series foundation models produce rich embeddings, but whether quantum models can exploit them, and how far hybrid classical-quantum architectures scale, remains unclear. We address this by fine-tuning Chronos for power-grid event classification (PSML-5) with a quantum head on the model's embeddings. Grouping embeddings by physical sensor type before summarization already surpasses the best published baseline built for this benchmark, and with finer-grained features the quantum head outperforms a larger classical multilayer perceptron on identical inputs by 1.7-2.0 percentage points of balanced accuracy. Yet the gains saturate: past a point, feeding more information to the same fixed-width register yields no improvement. We show the bottleneck is neither the supply of information nor circuit expressiveness, but the bandwidth of the data intake. To overcome this limitation, we introduce the wing module, a self-contained few-qubit circuit that feeds additional information into the core circuit through a sparse, one-way coupling. Under a preregistered four-seed protocol, we attach wings to a fixed 12-qubit core with fixed features. Balanced accuracy increases with each added wing, from 83.6% with no wings (13 qubits, including a post-selection qubit) to 85.2% with two (19 qubits). Ablations establish that a circuit enlarged without new information gains nothing, while a wing fed information from the wrong sample harms accuracy. These results reframe scaling for quantum fine-tuning: added qubits help when they carry added inputs, not merely more parameters. Wings offer a modular and stable route to widening that bandwidth.
Sang Hyub Kim, Julien Baglio, R. Krishnakumar et al.· 0 citations
One of the strengths of our recently proposed Walking Cat Architecture for a trapped-ion quantum computer is that it is straightforward to extend and optimize for a specific application. As a proof-of-concept, here we present such optimizations for solving the $256$-bit elliptic curve discrete logarithm problem (ECDLP) on $\mathtt{secp256k1}$, which is the elliptic curve used by blockchain technologies such as Bitcoin, using Shor's algorithm. We optimize the circuits from Schrottenloher's recent work and arrive at a logical quantum circuit for solving the ECDLP using about $1450$ qubits and $40\cdot 10^6$ Toffoli gates, with a rigorous lower bound on the logical-level success probability that holds with confidence at least $1-2^{-128}$. Using our compilation toolchain with manual optimization of the logical layout and integrated routing, we produce estimates for the logical measurement depth and the required number of physical qubits by compiling all components to measurement schedules that obey the architectural constraints. A key ingredient is a fast CCZ magic-state factory and a depth-one CCZ state injection, reducing the execution time of CCZ gates by a factor of $31$. We increase the logical-measurement parallelism using non-overlapping cat-based measurements in parallel, and we leverage the recently proposed logical CliNR protocol to speed up Clifford operations. To reduce the qubit overhead, we introduce a more efficient loss correction protocol, design a layout that allows us to recycle the CliNR ancilla qubits, and provision reusable cat-state resources according to the circuit's peak measurement parallelism. All results and optimizations combined, we conclude that a trapped-ion quantum computer based on our architecture can solve the ECDLP on $\mathtt{secp256k1}$ in approximately 25.7 days using 19,397 physical qubits with an estimated success probability of $63\%$.
Thomas Häner, Felix Tripier, Jacob Young et al.· 0 citations
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