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arXiv:2611.00561v1 Announce Type: new Abstract: Quantum infrared spectroscopy (QIRS), which exploits quantum interference between photon-pair generation processes, enables infrared spectroscopy using only visible and near-infrared (VNIR) light sources and detectors.
arXiv QuantumRead article →arXiv:2611.00521v1 Announce Type: new Abstract: We experimentally evaluate five estimators for weak-value amplification (WVA) using a position pointer to measure birefringence time delays. Two general approaches are considered: measuring the position shift $\Delta x$ and measuring the probability change $\Delta P_w$ of successful postselection.
arXiv QuantumRead article →arXiv:2611.00500v1 Announce Type: new Abstract: A weak coherent link can produce unusually early arrivals without changing stationary count rates. Missed jumps along routes available without the link can produce similar records, obscuring its presence.
arXiv QuantumRead article →arXiv:2611.00410v1 Announce Type: new Abstract: We study the dynamic purification of a continuously monitored quantum harmonic oscillator coupled to a thermal bath, where the control is the quadrature measurement angle of up to two detectors with prescribed strengths and efficiencies.
arXiv QuantumRead article →arXiv:2611.00404v1 Announce Type: new Abstract: With recent improvements to syndrome extraction scheduling and decoding, and the availability of complete transversal Clifford operations and lattice surgery, color code is an especially promising platform for quantum processors with local connectivity. The key ingredient for universal computation is thus generating useful magic states with limited resources.
arXiv QuantumRead article →arXiv:2611.00309v1 Announce Type: new Abstract: It is common in particle detector models in cavity QED to often omit zero modes or assume their state, compromising causality or introducing ambiguity in their predictions. We show that spatial-derivative coupling eliminates both problems.
arXiv QuantumRead article →arXiv:2611.00303v1 Announce Type: new Abstract: Quantum computing is a rapidly developed field of science. In the NISC era, quantum machine learning stands out, potentially outperforming classical machine learning. In this paper, we explore the applicability of quantum machine learning to a real-world application. We solved the problem of prediction of the temperature of GPUs in a high-performance computing cluster.
arXiv QuantumRead article →arXiv:2611.00272v1 Announce Type: new Abstract: Quantum linear regression models, such as quantum extreme learning machines and quantum fidelity kernels, are trained on features that can only be estimated from finitely many measurement shots. Sampling noise is usually treated as a nuisance that more shots suppress; we show instead that it can fundamentally reshape the instabilities of the learning problem.
arXiv QuantumRead article →arXiv:2611.00255v1 Announce Type: new Abstract: We benchmark quantum convolutional neural networks (QCNNs) and their classical surrogates for phase differentiation in one-dimensional strongly correlated systems with beyond-nearest-neighbour terms.
arXiv QuantumRead article →arXiv:2611.00233v1 Announce Type: new Abstract: We consider nearest-neighbour interactions on spin-1/2 chains. We find the full set of integrable models of this form and apply a numerical algorithm to determine the "closest" integrable model for a given Hamiltonian. This allows us to define a model-independent distance to integrability.
arXiv QuantumRead article →arXiv:2611.00203v1 Announce Type: new Abstract: Characterizing local Hamiltonians that host interesting many-body states as eigenstates has been an important problem in quantum many-body physics. For matrix product states (MPS), there is a well-known construction of parent Hamiltonians that have the MPS as the frustration-free ground state.
arXiv QuantumRead article →arXiv:2611.00201v1 Announce Type: new Abstract: Real-time quantum simulation of field theories offers access to scattering and decay dynamics, circumventing the limitations of Euclidean lattice methods. However, representing bosonic fields on qubits requires a finite-dimensional approximation and can entail costly multi-qubit operations.
arXiv QuantumRead article →arXiv:2611.00144v1 Announce Type: new Abstract: This paper determines exactly the R\'enyi-2 entanglement of purification of the normalized symmetric Werner state in every local dimension $d\ge2$ \begin{equation*} \mathrm{EoP}_2(W_{+,d})=\log_2\frac{4d(d+1)}{d^2+3d+4}.
arXiv QuantumRead article →arXiv:2610.06937v2 Announce Type: replace Abstract: Reducing logic depth during FPGA technology mapping can leave fanout bottlenecks in the resulting LUT network. LUT replication redistributes these loads but consumes area and increases loading on shared fanins. We propose FAPO, a mapper-independent post-mapping optimizer that jointly refines sink assignments and LUT cuts under a final-area constraint.
arXiv Computer Science ARRead article →arXiv:2608.19300v2 Announce Type: replace Abstract: As process technology scaling slows, microarchitectural innovation has become the primary driver of performance gains, making pre-silicon Performance Verification (PV) more critical than ever.
arXiv Computer Science ARRead article →arXiv:2511.17971v3 Announce Type: replace Abstract: Tensor-train (TT) compression reduces neural-network parameters, but the latency of a tensorized layer is set by its contraction order and its mapping onto hardware: a contraction path with fewer multiply-accumulate operations (MACs) is not necessarily faster on a systolic array.
arXiv Computer Science ARRead article →arXiv:2611.03325v1 Announce Type: cross Abstract: Fully homomorphic encryption (FHE) enables privacy-preserving computation on encrypted data without decryption. Despite its strong security guarantees, FHE incurs substantial computational and memory overhead.
arXiv Computer Science ARRead article →arXiv:2611.03138v1 Announce Type: cross Abstract: Progress in robot learning, particularly physical multi-agent reinforcement learning (MARL), is currently challenged by a limited availability of accessible, standardized benchmarks. While simulation-based MARL has produced remarkable emergent behaviors, from coordinated team play to complex tool use, translating these advances to physical systems remains difficult.
arXiv Computer Science ARRead article →arXiv:2611.02525v1 Announce Type: cross Abstract: Touching a virtual agent involves more than detecting a finger on a display: the contact surface can deform and resist the user's push, and the agent can respond to that action. We describe the Desktop Amid Touch (DAT) screen, which combines spatial projection onto a semi-transparent, deformable mesh with touch sensing and motorized control of resistance to…
arXiv Computer Science ARRead article →arXiv:2611.02517v1 Announce Type: cross Abstract: This study aimed to examine the impact of finger movements in hugging robots on users by experimentally investigating how the presence and patterns of such movements affect users psychologically.
arXiv Computer Science ARRead article →arXiv:2611.02449v1 Announce Type: cross Abstract: Expectation-maximization (EM) for Gaussian mixture models (GMMs) requires scoring $N$ data points against $K$ mixture components. Prior fused GPU implementations, notably Flash-GMM, eliminate materialization of the $N \times K$ responsibility matrix but traverse the component dimension twice to compute normalization terms and accumulate sufficient statistics.
arXiv Computer Science ARRead article →arXiv:2611.00301v1 Announce Type: cross Abstract: Floating point computing has dominated scientific computations since the 1950s because of its versatile ability to approximate real numbers with both sufficiently high precision and sufficiently great range.
arXiv Computer Science ARRead article →arXiv:2611.00104v1 Announce Type: cross Abstract: Cluster counting ($dN/dx$) measures discrete ionization occurrences along a charged particle track traversing a gaseous medium and is currently being proposed to serve particle identification (PID) in next-generation drift chambers, as it is expected to offer higher separation power than the traditional approach based on the ionization energy loss measurement…
arXiv Computer Science ARRead article →arXiv:2611.03503v1 Announce Type: new Abstract: Matrix multiplication dominates both PHY signal processing and Deep Neural Network (DNN) inference, yet these domains place opposing demands on the matrix engine. Signal processing requires low-latency execution of relatively small matrix operations, whereas DNN inference, particularly at large batch sizes, prioritizes high throughput and energy efficiency for large…
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