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Tzu-Hsiang Huang

Publications and source records attributed to Tzu-Hsiang Huang.

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Complexity Theory for Quantum Promise Problems

We begin by establishing structural results for several fundamental quantum complexity classes: p/mBQP, p/mQ(C)MA, $\text{p/mQSZK}_{\text{hv}}$, p/mQIP, p/mBQP/qpoly, p/mBQP/poly, and p/mPSPACE. This includes identifying complete problems, as well as proving containment and separation results among these classes. Here, p/mC denotes the corresponding quantum promise complexity class with pure (p) or mixed (m) quantum input states for any classical complexity class C. Surprisingly, our findings uncover relationships that diverge from their classical analogues -- specifically, we show unconditionally that p/mQIP$\neq$p/mPSPACE and p/mBQP/qpoly$\neq$p/mBQP/poly. This starkly contrasts the classical setting, where QIP$=$PSPACE and separations such as BQP/qpoly$\neq$BQP/poly are only known relative to oracles. More interestingly, these separation results further connected to the topic of for both quantum property testing and unitary synthesis. This new framework has numerous applications in quantum cryptography, particularly in the contexts of Microcrypt. We provide a better characterization of its primitives; for example, we show that OWSG and PRS can be broken by a p/mQCMA oracle, leading to a natural quantum analogue of Impagliazzo's five worlds by substituting the classical complexity classes in Pessiland, Heuristica, and Algorithmica with mBQP and mQCMA. Moreover, we establish the relativization barrier for proving the existence of EFI, noting that no such barrier currently exists within traditional complexity theory.

quant-ph

Moodie: An Early-Stage Design Exploration for Supporting Fear of Missing Out with LLM-based Chatbots

The excessive use of social media has led to the challenge known as Fear of Missing Out (FoMO). Existing studies fail to provide accessible, interactive tools that focus on the emotional and cognitive aspects of FoMO. This work presents Moodie, a chatbot designed using Large Language Models to support emotion regulation and reduce FoMO. We conducted a formative study to understand the needs of individuals with FoMO and developed Moodie. Then, we conducted a preliminary evaluative study (N=21) to observe how participants interact with Moodie and a baseline chatbot (GPT-4o) over one week. The results show that while both Moodie and a baseline chatbot reduced FoMO to a similar extent, Moodie resulted in greater engagement and social connection. This finding raises interesting questions about the advantages of purpose-built chatbots compared to general-purpose models for mental health support. Future research will include chat log analysis, prototype refinements, and longitudinal evaluations.

cs.HC

Real-Time Minimum-Energy Operating-Point Tracking for Battery-Powered Micro DC Motors Under Dynamically Variable Loading

Micro DC brushed motors are widely deployed in battery-powered biomedical systems, where limited energy budgets and variable physiological loading impose stringent efficiency and safety constraints. However, conventional actuation strategies rely on conservative voltage margins to avoid stalling, leading to systematic energy inefficiency. Furthermore, existing methods primarily optimize steady-state performance, neglecting the energy required to complete individual actuation cycles under dynamic conditions. This paper reveals that the energy consumption per mechanical cycle of a DC motor exhibits a non-monotonic dependence on driving voltage, with a load-dependent minimum that shifts with external loading. Based on this insight, we propose a real-time operating-point tracking method that enables the motor to autonomously converge to its minimum-energy condition. A lightweight load metric derived from current waveform features is introduced to detect load variation, and a two-phase adaptive voltage strategy is developed to track the optimal operating point online. Experimental results demonstrate that the proposed method can track the new minimum-energy operating region under both low-to-high and high-to-low loading transitions. With 3-cycle averaging, the mean response time is 11.55s for the low-to-high case and 11.16s for the high-to-low case, while the mean convergence voltage is 2.73V and 2.0V, respectively.

eess.SY