Pauli Taipale

Partitioning interacting services into independently rooted trust domains limits issuer-compromise reach while increasing calls across trust boundaries. Post-quantum replacements for public-key authentication and key-establishment mechanisms can increase crossing latency on constrained or lossy paths. We formulate the joint selection of trust domains and credential-derivation structures under policy and latency constraints, linking separate service-interaction and credential-derivation graphs through domain assignment. Credential blast radius measures weighted service impact after compromise. A linear upper bound supports optimization, while a joint event model gives exact expected impact. We identify when risk from issuers trusted across domains can be incorporated into this linear score, avoiding separate issuer-propagation calculations for each candidate. Although the general problem is NP-hard, we identify restricted cases that can be solved efficiently and exactly. Joint optimization yields lower blast radius than choosing boundaries first in 195 of 230 exhaustive synthetic comparisons, especially under chained delegation. A trace-derived replay used measured post-quantum costs, synthetic risk inputs, a fixed derivation family, and one to six trust domains. Under independent compromise events, mean expected impact was up to 36% lower than with one domain within the latency budget. The framework turns risk assumptions and measured crossing costs into candidate trust-domain and credential-derivation designs.

Selecting anomalous samples and explanatory features under fixed cardinalities defines a coupled optimization problem: feature-first selection can overlook features whose usefulness depends on the selected samples. In the analyzed model, joint calibration-error sensitivity is sample-count independent, while the sum-column feature-first rule's sensitivity grows linearly. To optimize this formulation, we introduce Coupling-Grouped XY Quantum Approximate Optimization Algorithm (CG-XY-QAOA), with constraint-preserving mixers and block-structured phase angles. On matched sparse IBM Heron R3 circuits, our implementation reduces circuit depth by 35.1%-54.8% against Qiskit's optimization-level 3 preset pass manager on the CZ-basis target and enables runs at 64 decision qubits (p=2) and 36 (p=3). At 20 decision qubits (p=5), fully grouped angles raise the fixed low-energy hit probability 25-fold to 3.7%, with 64.7% of measured samples satisfying both constraints. Benchmarks favor joint selection when feature usefulness varies across anomalies. Noiseless simulations show problem-structured grouping improves over same-depth XY-QAOA and parameter-matched type-preserving randomizations.

Quantum computers are expected to offer advantages in solving optimization problems challenging for classical computers. Quadratic Unconstrained Binary Optimization (QUBO) problems represent an important class of problems with relevance in finance and logistics. The Quantum Approximate Optimization Algorithm (QAOA) is a prominent candidate for solving QUBO problems on near-term quantum devices. In this paper, we evaluate the performance of both the standard QAOA and the adaptive derivative assembled problem tailored QAOA (ADAPT-QAOA) to solve QUBO problems of varying sizes and hardnesses for financial feature selection problems. Our main observation is that ADAPT-QAOA achieves substantially higher approximation ratios than standard QAOA for harder feature-selection problems (α = 0.6) with statistically significant improvements observed for problem sizes n = 6 and 14. However, the standard QAOA remains competitive for simpler problems. Additionally, we evaluate the practical feasibility and limitations of QAOA through a hardware-aware scaling analysis based on the real-device calibration data for various hardware platforms. We estimate that standard QAOA implementation on superconducting quantum computers provides a shorter time-to-solution compared to trapped-ion devices, while trapped-ion devices yield more favorable error rates. Our findings provide a comprehensive overview of the challenges, trade-offs, and strategies for deploying QAOA-based methods on near-term quantum hardware.

The emergence of quantum computing proposes a revolutionary paradigm that can radically transform numerous scientific and industrial application domains. However, realizing this promise in industrial applications is far from being practical today. In this paper, we discuss industry experiences with respect to quantum computing, and the gap between quantum software engineering research and state-of-the-practice in industry-scale quantum computing.

An apparatus and method for detecting silent speech. The method includes: measuring (402) three-dimensional acceleration data from vibrations caused by speech organs of the user during subvocalization using an accelerometer configured to be externally attachable to an anterior part of a neck below a mandible of a user; obtaining (404) repeatedly into a first-in first-out buffer from the accelerometer an acceleration data frame; combining (406) the acceleration data frames from the first-in first-out buffer into an input data structure; classifying (408) each input data structure into a class selected among a set of classes using a trained artificial neural network, each class expressing a specific word or background noise; setting (410) a final class to represent a predetermined number of consecutive classes of the input data structures; and performing (412) an action based on the final class.