58 papers
This paper presents a programmable superconducting Josephson-junction-based leaky integrate-and-fire neuron that unifies in-memory computation, dual-timescale plasticity, and ultra-high-speed, low-power operation to enable energy-efficient neuromorphic computing.
This paper argues that the integration of classical HPC and quantum computing exposes critical limitations in current reproducibility frameworks, necessitating a cultural and methodological shift toward workflow-centered scientific practices that document both process abstractions and implementation contexts to ensure robust knowledge validation.
This paper proposes a systematic approach to Chalmers' "easy problems" of consciousness by demonstrating how an executable cognitive system, grounded in Kantian conceptual knowledge, can computationally derive abilities such as discrimination, attention, and reportability through its learning, emotional, and information-manipulation mechanisms.
This paper introduces Spectral Dynamics Reservoir Computing (SDRC), a hardware-efficient framework that leverages the fast spectral dynamics of physical systems to achieve high-speed, high-performance neuromorphic processing without the need for complex, precision-sensitive integrated circuits, as demonstrated by state-of-the-art results on benchmark and speech recognition tasks using spin waves.
This paper demonstrates that while stochastic Ising machines exhibit longer autocorrelation times for sampling neural-network quantum states of Heisenberg models, their massive parallelism projects a significant speed-up of 100 to 10,000 times over standard Metropolis-Hastings sampling, enabling efficient large-scale quantum simulations without requiring direct hardware deployment.
This paper proposes an improved constrained hypercube mixer for the Quantum Approximate Optimization Algorithm that reduces circuit size and enhances noise robustness for a broad class of linearly constrained combinatorial optimization problems, thereby advancing the practical applicability of QAOA in the NISQ era.
This paper introduces a novel qutrit quantum error-correcting code that enables a transversal implementation of the non-reversible AND gate by interpreting symmetric T-depth circuit decompositions as CSS codes, while also proposing mixed qubit-qutrit protocols for magic state distillation.
This paper demonstrates that while electromagnetic shielding effectively suppresses passive radiated emissions, it fails to prevent active RF probing attacks that exploit state-dependent impedance variations to leak execution-dependent information through backscattering.
This paper demonstrates that standard filamentary HfOx/Ti memristor arrays can achieve month-scale stable, energy-efficient on-chip learning with accuracy comparable to backpropagation by combining forward-only training algorithms with sub-1 V reset-only single-pulse updates.
This paper proposes a hardware-efficient framework for constrained optimization on probabilistic machines by introducing native multi-state p-dits and mean-field constraints to eliminate dense couplings, thereby restoring sparsity and enabling orders-of-magnitude acceleration via FPGA implementation.
This paper proposes a new paradigm for causal discovery that leverages crowdsourcing, expert elicitation, and LLM-based simulation to aggregate fragmented knowledge from multiple agents into a comprehensive global causal structure unattainable by any single entity.
This paper proposes a joint hardware-workload co-optimization framework using an evolutionary algorithm to design generalized in-memory computing accelerators that significantly reduce the energy-delay-area product across multiple neural network workloads, overcoming the limitations of single-workload specialized designs.
This paper introduces Human-Certified Module Repositories (HCMRs) as a new architectural framework that combines human oversight with automated analysis to curate, certify, and secure reusable software modules, thereby ensuring the trustworthiness and reliability of systems assembled by AI agents.
This foundational concept paper proposes the Adaptive Quantized Planetary Crater Detection System (AQ-PCDSys), an architecture integrating quantized neural networks and adaptive multi-sensor fusion to enable real-time, high-fidelity crater detection on resource-constrained, radiation-hardened space exploration hardware.
This study demonstrates through numerical simulations that while an 8-bit resolution is sufficient for optical Ising machines, surprisingly, using a 1-bit resolution modulator actually enhances performance across various benchmark optimization problems.
This paper explores the use of multipartite entanglement resources and four-dimensional qudits to efficiently implement distributed fan-out operations and global Mølmer-Sørensen gates, offering insights for future quantum circuit compilation and data center design.
This paper introduces a simulation framework using STIM to demonstrate that superconducting qubits with physical error rates up to 0.75 can be retained in surface code lattices without significantly degrading logical performance, thereby redefining defectiveness as a spectrum rather than a binary failure state and providing actionable metrics for hardware designers.
This paper proposes a memory-efficient classical simulation method for quantum machine learning that fuses consecutive gates in both forward and backward paths to minimize global memory access, achieving up to 30 times higher throughput and enabling the training of large-scale deep quantum circuits on consumer GPUs.